WO2022036801A1 - Method and system for achieving coexistence of heterogeneous networks - Google Patents
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Definitions
- the present invention relates to the technical field of heterogeneous network communication, and more particularly, to a method and system for realizing coexistence of heterogeneous networks.
- the existing heterogeneous signal identification schemes are all based on artificially extracted time-frequency features for heterogeneous signal identification.
- the biggest drawback of this method is that the required sampling time is long, that is, better identification results can be obtained only when the spectral resolution is high.
- the solution of switching channels becomes inadvisable due to the increased number of devices, which leads to more and more busy channels. Adding nodes to improve ZigBee visibility will bring extra cost and little effect.
- Cross-protocol communication technology transmission The information is inefficient and the physical layer modification process is cumbersome and complicated.
- the subcarrier zeroing technology needs to be modified on both the WiFi transmitter and the receiver. Modifying the WiFi receiving device is trivial and unrealistic.
- the purpose of the present invention is to overcome the above-mentioned defects of the prior art, and to provide a method and system for realizing the coexistence of heterogeneous networks, so as to solve the heterogeneous network in which the WiFi equipment causes serious interference to the ZigBee nodes of the overlapping channels caused by the unequal transmission power. coexistence problem.
- a method for realizing coexistence of heterogeneous networks includes the following steps:
- Step S1 The WiFi sending end uses the set first idle channel detection threshold to determine the channel state based on the collected environment samples, and in the case of determining that the channel is in the idle state;
- Step S2 in the case of judging that the channel is in an idle state, within the set distributed inter-frame gap time, input the environment sample into a pre-trained neural network model to identify whether there is a heterogeneous signal;
- Step S3 In the case of identifying the existence of heterogeneous signals, the WiFi transmitter reduces the first idle channel detection threshold to the second idle channel detection threshold, ends the distributed inter-frame gap time, enters the backoff process, and is in the backoff process.
- the time slot uses the second idle channel detection threshold to detect the channel state, and then determines the transmission timing of the WiFi data packet according to the detection result.
- a system for realizing coexistence of heterogeneous networks includes:
- Channel state detection unit the WiFi sending end uses the set first idle channel detection threshold to determine the channel state based on the collected environment samples;
- Heterogeneous signal identification unit in the case of judging that the channel is in an idle state, within the set distributed inter-frame gap time, the environment sample is input into the pre-trained neural network model to identify whether there is a heterogeneous signal;
- the WiFi transmitter reduces the first idle channel detection threshold to the second idle channel detection threshold, ends the distributed inter-frame gap time, enters the backoff process, and The backoff time slot uses the second idle channel detection threshold to detect the channel state, and then determines the transmission timing of the WiFi data packet according to the detection result.
- the advantage of the present invention is that the provided heterogeneous signal identification design can automatically extract the ZigBee signal with a low signal-to-noise ratio through a deep learning model, so as to achieve excellent performance even under the microsecond-level delay. Recognition rate.
- the coexistence mechanism design only needs to modify the medium access control layer at the WiFi sending end, and the rest are compatible with the existing network, which is more convenient for deployment and large-scale promotion.
- the heterogeneous signal identification module is designed in combination with the coexistence mechanism, which can realize the existence of low-power ZigBee signals at the WiFi transmitter with a very short delay, so as to prevent the WiFi transmitter from simultaneously transmitting WiFi data packets during ZigBee data transmission.
- the invention starts from the interference source, solves the coexistence problem of heterogeneous networks with unequal power, and realizes a more fair and reasonable channel competition situation.
- FIG. 1 is a flowchart of a method for realizing coexistence of heterogeneous networks according to an embodiment of the present invention
- FIG. 2 is an overall process diagram of a method for realizing coexistence of heterogeneous networks according to an embodiment of the present invention
- FIG. 3 is a flowchart of identifying a heterogeneous signal according to an embodiment of the present invention.
- FIG. 4 is a topology diagram of an experimental device for collecting sample data according to an embodiment of the present invention.
- FIG. 5 is a schematic diagram of a process for identifying heterogeneous signals according to an embodiment of the present invention.
- FIG. 6 is a structural diagram of a convolutional neural network according to an embodiment of the present invention.
- FIG. 7 is a schematic diagram of a coexistence mechanism in a heterogeneous network according to an embodiment of the present invention.
- FIG. 8 is a schematic diagram of an experimental result of identifying a heterogeneous signal according to an embodiment of the present invention.
- FIG. 9 is a topology diagram of a protocol performance verification simulation according to an embodiment of the present invention.
- FIG. 10 is a schematic diagram of protocol performance of a WiFi transmitter using different parameters according to an embodiment of the present invention.
- FIG. 11 is a schematic diagram of protocol performance for different topological distances according to an embodiment of the present invention.
- the method for realizing coexistence of heterogeneous networks generally includes: step S110, the WiFi sending end uses the set first idle channel detection threshold based on the collected environment samples to determine the channel state; step S120, in In the case of judging that the channel is in an idle state, within the set distributed inter-frame gap time, input the environment sample into the pre-trained neural network model to identify whether there is a heterogeneous signal; step S130, after identifying the existence of a heterogeneous signal
- the WiFi transmitter reduces the first idle channel detection threshold to the second idle channel detection threshold, ends the distributed inter-frame gap time, enters the back-off process, and uses the second idle channel detection threshold in the back-off time slot
- the channel state is detected, and then the transmission timing of the WiFi data packet is determined according to the detection result.
- the present invention mainly includes the design of heterologous signal recognition and the design of heterologous signal coexistence mechanism.
- heterogeneous signal identification and coexistence mechanism (or called coexistence protocol) will be introduced in detail to facilitate understanding. Taking a heterogeneous network composed of WiFi and ZigBee as an example, it will be described in conjunction with FIG. 2 .
- the present invention adopts deep learning technology to realize the identification of ZigBee signals when the signal-to-noise ratio is low.
- the process of heterogeneous signal identification includes: collecting a large amount of data to construct the original data set; after labeling the original data set according to the frequency domain features of higher spectral resolution, and then dividing into frames to obtain fewer sample points and Corresponding labels, obtain the training set; send the training set to CNN (Convolutional Neural Network, convolutional neural network) for training.
- CNN Convolutional Neural Network, convolutional neural network
- the robustness of the model can be further verified by the test set, so as to realize the effective identification of ZigBee signals with low signal-to-noise ratio in extremely short delay.
- constructing a training set includes the following steps:
- Step S101 collecting signals.
- the distance and transmission parameters of the experimental equipment are set as shown in Figure 4.
- Two general software radio equipment USRPN210 is used as the WiFi protocol transceiver.
- the WiFi transmitter is in WiFi channel 6, with a standard transmit power of 16dBm and a rate of 54Mbps. Continuously transmit up to 1500 byte packets.
- the TelosB device is used as the ZigBee protocol transmission node, and the ZigBee sending node sends 20-byte data packets every 1ms with the ZigBee channel 16 overlapping with the WiFi channel 6, the standard transmission power of the ZigBee device of 0dBm, and the data transmission rate of 250kbps.
- Step S102 divide long frames and acquire labels.
- the original sample data streams of all data sets are divided into long frames at every 1024 sample points, and Hanning window is applied frame by frame to reduce the interference caused by signal fluctuations, and then each long frame is subjected to fast Fourier transform to obtain spectral images ,
- spectral features the signal bandwidth and the center frequency
- a peak finding method is used to find spectral features. Specifically, the peak search method compares the change of the slope of the line connecting two adjacent points. If the adjacent slope rises by more than 30%, it is regarded as a peak.
- a ZigBee signal spectrogram with a spectral resolution of 1024 points has several peaks. , the center of the peak is the center frequency, and the width of the peak boundary is the bandwidth. The presence or absence of a ZigBee signal in the frame can be easily identified using the bandwidth and center frequency at high spectral resolution.
- the signal bandwidth is 2MHz and its center frequency falls near 2.435GHz, it can be determined that there is a ZigBee signal in this data, and the label is set to ZigBee (indicating that there is a heterogeneous signal ZigBee), otherwise, whether it is noise or WiFi signal , are marked with the Noise label.
- Step S103 obtaining short frames and corresponding labels
- step S102 the long frame divided by 1024 sample points has higher spectral resolution.
- the method of truncating the sample points is adopted to obtain a short frame of every 64 points. .
- the spectral characteristics of the spectrogram of the short frame are no longer obvious, so the means of extracting the spectral bandwidth and the center frequency are no longer feasible.
- the label of the short frame is obtained by means of the label of the long frame.
- the corresponding label of the short frame can be obtained only by expanding the label by 16 times. Since a ZigBee data packet continuously occupies about 36 frames divided by 1024 points, the signal boundary frame is at the junction of ZigBee signal and noise, which may contain both ZigBee signal and noise signal. Direct expansion will cause wrong labels. In order to avoid confusion and ensure label accuracy, preferably, the signal boundary long frame and the corresponding label are removed, and then the short frame operation is performed.
- Step S104 data vectorization
- a vectorization operation is performed to convert the short frame of IQ data into a four-dimensional vector form N example ⁇ Dim IQ ⁇ Dim value ⁇ Dim channel which is convenient for CNN to read and use.
- Nexample represents the number of short frames
- the cPickle library is used to serialize and store it into a file for the CNN model to use.
- the short frame vector and corresponding label (ie, the vectorized sample data set) obtained through the above data processing process will be sent to the CNN model for training.
- Using CNN can find the hidden features of weak ZigBee signals and the recognition speed is fast.
- the first layer of the network is a zero-padding layer (Zero Padding) to save boundary information; then, 128 convolutions with a convolution kernel size of (1,3) and a stride of (1,1) are input
- the convolution layer composed of filters, and the ReLU (linear rectification function) function is used as the activation function to introduce nonlinearity.
- This convolution layer is the key to extracting the hidden features of the signal; then, Dropout is performed to randomly discard half of the neurons to avoid overfitting. , which is beneficial to improve the robustness of the model; perform the same zero-padding, convolution, and dropout operations to obtain deeper and more representative hidden features; then, send the multi-dimensional data to the Flatten (flattening) layer
- One-dimensionalization is convenient for subsequent fully-connected layers; then two fully-connected layers are set as output layers, in order to obtain the weighted sum of the high-dimensional features obtained by the convolutional layer to obtain the scores of each category, and then map them to probabilities through the Softmax function.
- a CNN model that can effectively identify heterologous signals such as ZigBee with very few sample points in a short time is obtained.
- ED ElectronicgyDetection, energy detection
- the environmental signal sampling points are sent into the deep learning model to determine the existence of the ZigBee signal. If it does not exist, it will be business as usual. If it exists, the CNN classifier will set a ZigBeeOn flag and pass it to the MAC.
- the MAC layer will lower the CCA (Clear Channel Assessment, idle channel detection) threshold, so that it will be more sensitive to ZigBee packets with low signal energy, so the energy detection will determine that the channel is busy, Trigger the backoff mechanism, delay the current transmission, and reserve the channel for ZigBee signal transmission, so as to avoid the phenomenon of the ZigBee data packet being destroyed caused by the simultaneous transmission of two protocols, and realize the fair competition of the channel between the two signals with unequal power.
- the lowering degree of the threshold may be determined according to the actual application scenario of the network, or determined according to simulation, which is not limited in the present invention.
- the next step is to design a reasonable MAC layer mechanism to delay the transmission of the WiFi signal to prevent its access channel from interfering with the normal reception of ZigBee data packets. Since WiFi devices cannot wait all the time, they must find an opportunity to re-access the channel to achieve a fair and reasonable channel allocation scheme for heterogeneous protocols.
- the coexistence mechanism designed by the present invention is shown in Figure 7.
- the WiFi transmitter is more sensitive to the weak ZigBee signal, so as to determine the channel Busy and triggers its inherent back-off mechanism, delaying the transmission of WiFi packets and giving up the channel for ZigBee data transmission.
- DCF Distributed Coordination Function
- the WiFi sender when it has a data packet to transmit, it first samples a small amount of environmental samples for energy detection to determine the channel state. At this time, since the ZigBee transmission power is lower than the energy detection threshold, the WiFi will determine that the channel is idle and wait for the protocol 802.11 DIFS (Distributed Inter-frame Spacing) of 9 microseconds specified by g/n.
- DIFS Distributed Inter-frame Spacing
- a small number of environmental samples used for energy detection are sent into the pre-trained CNN model during the DIFS time to determine the existence of the ZigBee signal, and if there is, a ZigBeeOn flag is set.
- the WiFiMAC layer will reduce the CCA threshold after receiving the flag bit.
- the sender ends the DIFS and enters the backoff process, and randomly selects a value from the contention window as the value of the random backoff counter.
- the backoff time consists of several backoff time slots. CCA will be performed in each backoff time slot. Since the CCA threshold has been lowered, the presence of the ZigBee signal will be sensed, and the channel will be determined to be busy, thus suspending the random backoff counter and delaying the transmission of WiFi data packets. In this way, there is no scenario in which the WiFi signal and the ZigBee signal exist in the channel at the same time, thus solving the problem that the WiFi data packet destroys the ZigBee data packet at the ZigBee receiving end.
- the present invention also provides a system for realizing coexistence of heterogeneous networks, which is used to realize one aspect or multiple aspects of the above method.
- the system includes: a channel state detection unit, where the WiFi sending end uses a set first idle channel detection threshold to determine the channel state based on the collected environmental samples; a heterogeneous signal identification unit: when it is determined that the channel is in an idle state, the In the set distributed inter-frame gap time, input the environment sample into the pre-trained neural network model to identify whether there is a heterogeneous signal; coexistence protocol unit: in the case of identifying the existence of a heterogeneous signal, the WiFi sender The idle channel detection threshold is reduced to the second idle channel detection threshold, the distributed inter-frame gap time is ended, the backoff process is entered, and the second idle channel detection threshold is used to detect the channel state in the backoff time slot, and then the WiFi is determined according to the detection result.
- the transmission timing of the packet can be applied to actually deployed heterogeneous networks, and
- the wireless protocol data is obtained from the experimental platform built by USRP N210 and TelosB, and the convolutional neural network is obtained by offline training on the training set via the graphics card loaded with Intel Graphics 620 and the central processing unit of Intel Corei7-8550U. model, and then send the test set into the model to obtain the recognition accuracy shown in Figure 8(a) and the confusion matrix shown in Figure 8(b) (sample points are 64). c) The running times corresponding to different sample points are shown.
- the predicted value of ZigBee label is consistent with the actual value, which means that all ZigBee signals are recognized, and 0.0187% of noise signals will be mistaken for ZigBee
- the signal, after analysis, is caused by part of the WiFi signal (the label of the WiFi signal is also Noise, because it only cares whether there is a ZigBee signal), but the proportion is too small, and the impact on the performance is negligible.
- Figure 8(c) is the average time for the model to run each short frame. The longer the number of sample points, the longer the corresponding running time. When the number of sample points is 64 points, the average recognition time is 66 microseconds.
- the speed will be increased by more than 10 times, which can be shortened to 6us.
- a special neural network chip such as TPU
- the speed will be increased by more than 10 times, which can be shortened to 6us.
- the present invention uses the convolutional neural network model to identify heterogeneous signals, which fully complies with the design objectives and has excellent performance.
- the performance of the coexistence protocol is verified, and the verification is carried out by means of simulation.
- the simulation uses the ns-3 library, and the simulation topology is shown in Figure 9. The general settings are consistent with the signal acquisition experiment, but due to the ns-3 library, if the distance between the ZigBee transceiver nodes is less than 2 meters, the phenomenon of continuous channel preemption may occur. Therefore, the default distance is set to 5 meters.
- the throughput of WiFi is used as the performance indicator, and since ZigBee sends less data and the rate is low, it is not suitable to measure the throughput. Therefore, it is preferable to use PRR (PacketReceiveRate, packet reception rate) as the measurement indicator.
- PRR PacketReceiveRate, packet reception rate
- the specific coexistence protocol simulation process is as follows: firstly, modify the settings of the WiFi transmitter to verify the performance of the coexistence protocol of the present invention under various WiFi standard transmission rates. It can be seen from Fig. 10(a) that, compared with the standard protocol, the protocol designed by the present invention brings more than twice the improvement of the PRR of ZigBee, which means that the protocol design brings great benefits to the ZigBee protocol.
- the sending rate is low, the WiFi throughput is slightly lost compared to the standard protocol.
- the sending rate is high, the loss of WiFi throughput is minimal.
- FIG 10(b) shows the performance of different WiFi data packet lengths. At this time, the WiFi data transmission rate has been adjusted back to the default 54Mbps. It can be seen from the figure that the larger the packet length, the larger the throughput, and this protocol has little effect on the WiFi throughput, and the use of the coexistence mechanism protocol of the present invention will increase the PRR by more than two times, with excellent performance.
- the PRR When d z is within 2m, the signal-to-noise ratio of the receiving end is large enough, and the PRR is close to 100%; when it increases to 3m, the PRR can be increased from 90% to 100% by using the protocol proposed by the present invention; while increasing d z to more than 4m , the PRR begins to drop sharply, and the PRR will be increased by nearly 2 times by using the present invention. Since the distance between the ZigBee sender and the WiFi sender remains unchanged, the throughput is constant, and the protocol of the present invention has little impact on the throughput.
- the protocol of the present invention will increase the PRR by about two times, and the WiFi throughput will be almost unchanged.
- the present invention proposes to apply deep learning to ZigBee signal recognition for the first time.
- the convolutional neural network model applies the convolutional neural network model to wireless protocol identification, and provides a brand-new idea of heterogeneous signal identification.
- the present invention designs a MAC layer coexistence protocol in combination with the deep learning identification results, and solves the problem of cross-protocol interference caused by power asymmetry by simply modifying the CCA threshold of the DCF mechanism adopted by WiFi, starting from the interference side, and realizing WiFi and ZigBee.
- the protocol is more fair and reasonable channel contention strategy.
- the deep learning-based heterogeneous signal identification scheme proposed by the present invention achieves a recognition accuracy rate as high as 99.9%, does not require high spectral resolution, and achieves extremely short delay at the microsecond level.
- the existing coexistence mechanism design scheme either requires additional nodes to be laid, or needs to modify the transmitter and the receiver at the same time, while the coexistence protocol design proposed by the present invention, combined with the existing DCF mechanism, only needs to modify the WiFi transmitter, and
- the current commercial equipment has high compatibility and is more convenient for large-scale promotion and deployment.
- the present invention may be a system, method and/or computer program product.
- the computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present invention.
- a computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device.
- the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
- Non-exhaustive list of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
- RAM random access memory
- ROM read only memory
- EPROM erasable programmable read only memory
- flash memory static random access memory
- SRAM static random access memory
- CD-ROM compact disk read only memory
- DVD digital versatile disk
- memory sticks floppy disks
- mechanically coded devices such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
- Computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.
- the computer readable program instructions described herein may be downloaded to various computing/processing devices from a computer readable storage medium, or to an external computer or external storage device over a network such as the Internet, a local area network, a wide area network and/or a wireless network.
- the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
- a network adapter card or network interface in each computing/processing device receives computer-readable program instructions from a network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
- the computer program instructions for carrying out the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages.
- Source or object code written in any combination, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as the "C" language or similar programming languages.
- the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
- the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through the Internet connect).
- LAN local area network
- WAN wide area network
- custom electronic circuits such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs)
- FPGAs field programmable gate arrays
- PDAs programmable logic arrays
- Computer readable program instructions are executed to implement various aspects of the present invention.
- These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer or other programmable data processing apparatus to produce a machine that causes the instructions when executed by the processor of the computer or other programmable data processing apparatus , resulting in means for implementing the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
- These computer readable program instructions can also be stored in a computer readable storage medium, these instructions cause a computer, programmable data processing apparatus and/or other equipment to operate in a specific manner, so that the computer readable medium on which the instructions are stored includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
- Computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executing on a computer, other programmable data processing apparatus, or other device to implement the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.
- each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more functions for implementing the specified logical function(s) executable instructions.
- the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
- each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation in hardware, implementation in software, and implementation in a combination of software and hardware are all equivalent.
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Abstract
Disclosed in the present invention is a method and system for achieving coexistence of heterogeneous networks. The method comprises: on the basis of an acquired environmental sample, a WiFi transmitting terminal determining a channel state by using a set first idle channel detection threshold; when it is determined that a channel is in an idle state, inputting, within a set distributed inter-frame spacing time, the environmental sample into a pre-trained neural network model to identify whether a heterogeneous signal exists; and when the existence of a heterogeneous signal is identified, the WiFi transmitting terminal reducing the first idle channel detection threshold to a second idle channel detection threshold, terminating the distributed inter-frame spacing time, entering a backoff process, and detecting, in a backoff time slot, the channel state by using the second idle channel detection threshold, thereby determining a transmission opportunity of a WiFi data packet according to a detection result. In the present invention, a heterogeneous signal can be accurately identified, thus reducing the interference between heterogeneous networks.
Description
本发明涉及异构网络通信技术领域,更具体地,涉及一种实现异构网络共存的方法和***。The present invention relates to the technical field of heterogeneous network communication, and more particularly, to a method and system for realizing coexistence of heterogeneous networks.
物联网技术的蓬勃发展带来无线通讯设备的爆发式增长,繁多的设备因需求不同而采用性能各异的多种通信协议,采用不同协议的设备于同一频段下进行无线通信将导致跨协议干扰。例如,在最为通用的ISM2.4GHZ频段下,WiFi设备因其占用频宽大,发射功率高,传输范围远等因素无法感知发射功率低的同频段下的ZigBee信号,WiFi数据包与ZigBee数据包于ZigBee接收节点发生碰撞,从而极大干扰ZigBee节点的信息传输。The vigorous development of the Internet of Things technology has brought about the explosive growth of wireless communication devices. Many devices use various communication protocols with different performances due to different needs. Wireless communication between devices using different protocols in the same frequency band will lead to cross-protocol interference. . For example, in the most common ISM2.4GHZ frequency band, WiFi devices cannot perceive ZigBee signals in the same frequency band with low transmit power due to factors such as large occupied bandwidth, high transmit power, and long transmission range. ZigBee receiving nodes collide, which greatly interferes with the information transmission of ZigBee nodes.
近年来,各种无线网络都在拓展其应用领域,从而形成多种无线协议于同一空间共存的情形,由此跨协议干扰愈发严重,异种协议共存问题已成为制约无线网络发展的瓶颈。目前,缓解跨协议干扰、解决异种信号共存问题主要从异种信号识别和共存机制设计两方面入手。In recent years, various wireless networks are expanding their application fields, resulting in the coexistence of multiple wireless protocols in the same space. As a result, cross-protocol interference has become more and more serious. The problem of coexistence of heterogeneous protocols has become a bottleneck restricting the development of wireless networks. At present, alleviating cross-protocol interference and solving the problem of heterogeneous signal coexistence mainly start from two aspects: heterogeneous signal identification and coexistence mechanism design.
若不能有效探测并识别异质干扰源,就无法采取对应措施解决共存问题。传统手段通过定制的频谱分析仪以高采样率根据异种信号频谱特征进行识别。近几年,研究人员通过异质干扰独有的时频特征实现常见通信芯片的干扰源识别。例如利用WiFiBeacon的周期性实现ZigBee芯片上识别WiFi干扰,又如通过WiFi芯片中RSSI值运用决策树学习以识别多种异构信号。Without effective detection and identification of heterogeneous sources of interference, countermeasures cannot be taken to address coexistence issues. Traditional methods identify heterogeneous signals based on spectral characteristics at high sampling rates through custom-built spectrum analyzers. In recent years, researchers have realized the identification of interference sources of common communication chips through the unique time-frequency characteristics of heterogeneous interference. For example, using the periodicity of WiFiBeacon to identify WiFi interference on the ZigBee chip, or using decision tree learning to identify multiple heterogeneous signals through the RSSI value in the WiFi chip.
在获悉干扰源之后,设计合理的机制使异种协议和谐地使用信道便成重中之重。常见手段包括通过认知无线电或干扰检测机制探知各个拥塞程度以选择合适的信道。近年来研究者提出通过提高ZigBee可见性以使WiFi发射端避让,例如,在WiFi发射端附近额外放置ZigBee节点发送冗余数 据;又如,增加ZigBee的Preamble(前导)以预留信道供远端的ZigBee链路传输;再如,运用搭载跨协议通信技术的网络服务器实现信道协调和规划;或者,利用子载波置零技术,通过WiFi发射端预留出部分与ZigBee信道重叠的子载波从而实现异种信号的频域隔离。After learning the source of interference, designing a reasonable mechanism to allow heterogeneous protocols to use the channel harmoniously becomes a top priority. Common approaches include detecting various congestion levels through cognitive radio or interference detection mechanisms to select an appropriate channel. In recent years, researchers have proposed to improve the visibility of ZigBee to avoid the WiFi transmitter, for example, placing additional ZigBee nodes near the WiFi transmitter to send redundant data; another example, adding ZigBee Preamble (preamble) to reserve channels for remote ends Another example is to use a network server equipped with cross-protocol communication technology to achieve channel coordination and planning; or, to use the sub-carrier zeroing technology to reserve some sub-carriers that overlap with the ZigBee channel through the WiFi transmitter. Frequency Domain Isolation of Heterogeneous Signals.
现有异种信号识别方案中均基于人工提取的时频特征进行异质信号识别,这种方法的最大的弊端是所需的采样时间长,即频谱分辨率高时才能得到较优的识别结果。而在现有的共存机制设计中,切换信道的方案由于设备增多导致信道愈发繁忙而变得不可取,增设节点提高ZigBee可见性将带来额外的成本且收效甚微,跨协议通信技术传输信息较低效且物理层修改流程繁琐复杂,子载波置零技术需要在WiFi发送端和接收端上均进行修改,修改WiFi接收设备琐碎且不现实。The existing heterogeneous signal identification schemes are all based on artificially extracted time-frequency features for heterogeneous signal identification. The biggest drawback of this method is that the required sampling time is long, that is, better identification results can be obtained only when the spectral resolution is high. However, in the existing coexistence mechanism design, the solution of switching channels becomes inadvisable due to the increased number of devices, which leads to more and more busy channels. Adding nodes to improve ZigBee visibility will bring extra cost and little effect. Cross-protocol communication technology transmission The information is inefficient and the physical layer modification process is cumbersome and complicated. The subcarrier zeroing technology needs to be modified on both the WiFi transmitter and the receiver. Modifying the WiFi receiving device is trivial and unrealistic.
发明内容SUMMARY OF THE INVENTION
本发明的目的是克服上述现有技术的缺陷,提供一种实现异构网络共存的方法和***,以解决发送功率不对等导致的WiFi设备给重叠信道的ZigBee节点带来严重干扰的异构网络共存问题。The purpose of the present invention is to overcome the above-mentioned defects of the prior art, and to provide a method and system for realizing the coexistence of heterogeneous networks, so as to solve the heterogeneous network in which the WiFi equipment causes serious interference to the ZigBee nodes of the overlapping channels caused by the unequal transmission power. coexistence problem.
根据本发明的第一方面,提供一种实现异构网络共存的方法。该方法包括以下步骤:According to a first aspect of the present invention, a method for realizing coexistence of heterogeneous networks is provided. The method includes the following steps:
步骤S1:WiFi发送端基于采集的环境样本利用设定的第一空闲信道检测阈值判断信道状态,并在判断信道处于空闲状态的情况下;Step S1: The WiFi sending end uses the set first idle channel detection threshold to determine the channel state based on the collected environment samples, and in the case of determining that the channel is in the idle state;
步骤S2:在判断信道处于空闲状态的情况下,在所设置的分布式帧间间隙时间内,将所述环境样本输入到预训练的神经网络模型以识别是否存在异种信号;Step S2: in the case of judging that the channel is in an idle state, within the set distributed inter-frame gap time, input the environment sample into a pre-trained neural network model to identify whether there is a heterogeneous signal;
步骤S3:在识别出存在异种信号的情况下,WiFi发送端将第一空闲信道检测阈值降低至第二空闲信道检测阈值,并结束所述分布式帧间间隙时间,进入退避过程,且在退避时隙利用第二空闲信道检测阈值检测信道状态,进而根据检测结果确定WiFi数据包的传输时机。Step S3: In the case of identifying the existence of heterogeneous signals, the WiFi transmitter reduces the first idle channel detection threshold to the second idle channel detection threshold, ends the distributed inter-frame gap time, enters the backoff process, and is in the backoff process. The time slot uses the second idle channel detection threshold to detect the channel state, and then determines the transmission timing of the WiFi data packet according to the detection result.
根据本发明的第二方面,提供一种实现异构网络共存的***。该***包括:According to a second aspect of the present invention, a system for realizing coexistence of heterogeneous networks is provided. The system includes:
信道状态检测单元:WiFi发送端基于采集的环境样本利用设定的第一空闲信道检测阈值判断信道状态;Channel state detection unit: the WiFi sending end uses the set first idle channel detection threshold to determine the channel state based on the collected environment samples;
异种信号识别单元:在判断信道处于空闲状态的情况下,在所设置的分布式帧间间隙时间内,将所述环境样本输入到预训练的神经网络模型以识别是否存在异种信号;Heterogeneous signal identification unit: in the case of judging that the channel is in an idle state, within the set distributed inter-frame gap time, the environment sample is input into the pre-trained neural network model to identify whether there is a heterogeneous signal;
共存协议单元:在识别出存在异种信号的情况下,WiFi发送端将第一空闲信道检测阈值降低至第二空闲信道检测阈值,并结束所述分布式帧间间隙时间,进入退避过程,且在退避时隙利用第二空闲信道检测阈值检测信道状态,进而根据检测结果确定WiFi数据包的传输时机。Coexistence protocol unit: In the case of identifying the presence of heterogeneous signals, the WiFi transmitter reduces the first idle channel detection threshold to the second idle channel detection threshold, ends the distributed inter-frame gap time, enters the backoff process, and The backoff time slot uses the second idle channel detection threshold to detect the channel state, and then determines the transmission timing of the WiFi data packet according to the detection result.
与现有技术相比,本发明的优点在于,所提供的异种信号识别设计,将信噪比低的ZigBee信号通过深度学***合理的信道竞争局面。Compared with the prior art, the advantage of the present invention is that the provided heterogeneous signal identification design can automatically extract the ZigBee signal with a low signal-to-noise ratio through a deep learning model, so as to achieve excellent performance even under the microsecond-level delay. Recognition rate. The coexistence mechanism design only needs to modify the medium access control layer at the WiFi sending end, and the rest are compatible with the existing network, which is more convenient for deployment and large-scale promotion. The heterogeneous signal identification module is designed in combination with the coexistence mechanism, which can realize the existence of low-power ZigBee signals at the WiFi transmitter with a very short delay, so as to prevent the WiFi transmitter from simultaneously transmitting WiFi data packets during ZigBee data transmission. The invention starts from the interference source, solves the coexistence problem of heterogeneous networks with unequal power, and realizes a more fair and reasonable channel competition situation.
通过以下参照附图对本发明的示例性实施例的详细描述,本发明的其它特征及其优点将会变得清楚。Other features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings.
被结合在说明书中并构成说明书的一部分的附图示出了本发明的实施例,并且连同其说明一起用于解释本发明的原理。The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention.
图1是根据本发明一个实施例的实现异构网络共存的方法的流程图;1 is a flowchart of a method for realizing coexistence of heterogeneous networks according to an embodiment of the present invention;
图2是根据本发明一个实施例的实现异构网络共存的方法的整体过程图;2 is an overall process diagram of a method for realizing coexistence of heterogeneous networks according to an embodiment of the present invention;
图3是根据本发明一个实施例的识别异种信号的流程图;3 is a flowchart of identifying a heterogeneous signal according to an embodiment of the present invention;
图4是根据本发明一个实施例的采集样本数据的实验设备拓扑图;4 is a topology diagram of an experimental device for collecting sample data according to an embodiment of the present invention;
图5是根据本发明一个实施例的识别异种信号的过程示意;5 is a schematic diagram of a process for identifying heterogeneous signals according to an embodiment of the present invention;
图6是根据本发明一个实施例的卷积神经网络的结构图;6 is a structural diagram of a convolutional neural network according to an embodiment of the present invention;
图7是根据本发明一个实施例的异构网络中的共存机制示意图;7 is a schematic diagram of a coexistence mechanism in a heterogeneous network according to an embodiment of the present invention;
图8是根据本发明一个实施例的识别异种信号的实验结果示意;8 is a schematic diagram of an experimental result of identifying a heterogeneous signal according to an embodiment of the present invention;
图9是根据本发明一个实施例的协议性能验证仿真的拓扑图;9 is a topology diagram of a protocol performance verification simulation according to an embodiment of the present invention;
图10是根据本发明一个实施例的WiFi发送端采用不同参数的协议性能示意图;FIG. 10 is a schematic diagram of protocol performance of a WiFi transmitter using different parameters according to an embodiment of the present invention;
图11是根据本发明一个实施例的不同拓扑距离的协议性能示意图。FIG. 11 is a schematic diagram of protocol performance for different topological distances according to an embodiment of the present invention.
现在将参照附图来详细描述本发明的各种示例性实施例。应注意到:除非另外具体说明,否则在这些实施例中阐述的部件和步骤的相对布置、数字表达式和数值不限制本发明的范围。Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the invention unless specifically stated otherwise.
以下对至少一个示例性实施例的描述实际上仅仅是说明性的,决不作为对本发明及其应用或使用的任何限制。The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
对于相关领域普通技术人员已知的技术、方法和设备可能不作详细讨论,但在适当情况下,所述技术、方法和设备应当被视为说明书的一部分。Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.
在这里示出和讨论的所有例子中,任何具体值应被解释为仅仅是示例性的,而不是作为限制。因此,示例性实施例的其它例子可以具有不同的值。In all examples shown and discussed herein, any specific values should be construed as illustrative only and not limiting. Accordingly, other instances of the exemplary embodiment may have different values.
应注意到:相似的标号和字母在下面的附图中表示类似项,因此,一旦某一项在一个附图中被定义,则在随后的附图中不需要对其进行进一步讨论。It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further discussion in subsequent figures.
参见图1所示,本发明提供的实现异构网络共存的方法总体上包括:步骤S110,WiFi发送端基于采集的环境样本利用设定的第一空闲信道检测阈值判断信道状态;步骤S120,在判断信道处于空闲状态的情况下,在所设置的分布式帧间间隙时间内,将所述环境样本输入到预训练的神经网络模型以识别是否存在异种信号;步骤S130,在识别出存在异种信号的情况下,WiFi发送端将第一空闲信道检测阈值降低至第二空闲信道检测阈值,并结束所述分布式帧间间隙时间,进入退避过程,且在退避时隙利用第二 空闲信道检测阈值检测信道状态,进而根据检测结果确定WiFi数据包的传输时机。本发明主要包括异种信号识别的设计和异种信号共存机制的设计。Referring to Fig. 1 , the method for realizing coexistence of heterogeneous networks provided by the present invention generally includes: step S110, the WiFi sending end uses the set first idle channel detection threshold based on the collected environment samples to determine the channel state; step S120, in In the case of judging that the channel is in an idle state, within the set distributed inter-frame gap time, input the environment sample into the pre-trained neural network model to identify whether there is a heterogeneous signal; step S130, after identifying the existence of a heterogeneous signal In the case of , the WiFi transmitter reduces the first idle channel detection threshold to the second idle channel detection threshold, ends the distributed inter-frame gap time, enters the back-off process, and uses the second idle channel detection threshold in the back-off time slot The channel state is detected, and then the transmission timing of the WiFi data packet is determined according to the detection result. The present invention mainly includes the design of heterologous signal recognition and the design of heterologous signal coexistence mechanism.
在下文中,将具体介绍异种信号识别和共存机制(或称共存协议),并便于理解,以WiFi和ZigBee组成的异构网络为例,结合图2进行说明。In the following, the heterogeneous signal identification and coexistence mechanism (or called coexistence protocol) will be introduced in detail to facilitate understanding. Taking a heterogeneous network composed of WiFi and ZigBee as an example, it will be described in conjunction with FIG. 2 .
一、关于异种信号识别1. About the identification of heterogeneous signals
对于异种信号识别,本发明采用深度学习技术实现信噪比低时的ZigBee信号的识别。总体而言,异种信号识别过程包括:采集大量数据构建原始数据集;根据较高频谱分辨率的频域特征对原始数据集进行标注后,再分帧获得满足低时延的较少样本点及对应标签,获得训练集;将训练集送入CNN(Convolutional Neural Network,卷积神经网络)进行训练。此外,还可进一步应用测试集验证该模型的鲁棒性,从而实现极短时延内低信噪比的ZigBee信号的有效识别。For the identification of heterogeneous signals, the present invention adopts deep learning technology to realize the identification of ZigBee signals when the signal-to-noise ratio is low. In general, the process of heterogeneous signal identification includes: collecting a large amount of data to construct the original data set; after labeling the original data set according to the frequency domain features of higher spectral resolution, and then dividing into frames to obtain fewer sample points and Corresponding labels, obtain the training set; send the training set to CNN (Convolutional Neural Network, convolutional neural network) for training. In addition, the robustness of the model can be further verified by the test set, so as to realize the effective identification of ZigBee signals with low signal-to-noise ratio in extremely short delay.
具体地,结合图3、图4和图5所示,构建训练集包括以下步骤:Specifically, with reference to Figure 3, Figure 4 and Figure 5, constructing a training set includes the following steps:
步骤S101,采集信号。Step S101, collecting signals.
为确保原始信号的纯净性,在屏蔽室进行信号采集。实验设备距离和发送参数设置如图4所示,采用两台通用软件无线电设备USRPN210作WiFi协议收发装置,WiFi发送端于WiFi信道6、以16dBm的标准发射功率和54Mbps的速率持续发送长达1500字节的数据包。采用TelosB设备作ZigBee协议传输节点,ZigBee发送节点每隔1ms、以与WiFi信道6重叠的ZigBee信道16、0dBm的ZigBee设备标准发送功率、250kbps的数据传输速率发送20字节的数据包。To ensure the purity of the original signal, signal acquisition was performed in a shielded room. The distance and transmission parameters of the experimental equipment are set as shown in Figure 4. Two general software radio equipment USRPN210 is used as the WiFi protocol transceiver. The WiFi transmitter is in WiFi channel 6, with a standard transmit power of 16dBm and a rate of 54Mbps. Continuously transmit up to 1500 byte packets. The TelosB device is used as the ZigBee protocol transmission node, and the ZigBee sending node sends 20-byte data packets every 1ms with the ZigBee channel 16 overlapping with the WiFi channel 6, the standard transmission power of the ZigBee device of 0dBm, and the data transmission rate of 250kbps.
采集实验首先仅运行WiFi接收设备和ZigBee收发设备,以25Mbps采样率采集10s,构建纯净的ZigBee信号数据集1;再仅运行WiFi收发设备,以25Mbps采集10s,构建纯净的WiFi信号数据集2;然后,同时运行WiFi收发设备和ZigBee收发设备,以25Mbps采样率采集10s数据构建数据集3。由于数据包收发之间有间隙,因此数据集1中ZigBee信号占比约60%,其余是底噪,数据集2用于提升模型鲁棒性,无需关心WiFi信号占比,数据集3中ZigBee信号占比约7%,其余是底噪和WiFi信号。数据集1与数据集2构成训练集,数据集3作为测试集。In the acquisition experiment, first only run the WiFi receiving device and ZigBee transceiver device, collect at 25Mbps sampling rate for 10s, and construct a pure ZigBee signal dataset 1; then only run the WiFi transceiver device, collect at 25Mbps for 10s, construct a pure WiFi signal dataset 2; Then, run the WiFi transceiver and ZigBee transceiver at the same time, and collect 10s data at a sampling rate of 25Mbps to construct dataset 3. Since there is a gap between data packets sending and receiving, the ZigBee signal in data set 1 accounts for about 60%, and the rest is the noise floor. Data set 2 is used to improve the robustness of the model, and there is no need to care about the proportion of WiFi signals. The signal accounts for about 7%, and the rest is the noise floor and WiFi signal. Data set 1 and data set 2 constitute a training set, and data set 3 is a test set.
步骤S102,分长帧并获取标签。Step S102, divide long frames and acquire labels.
所有数据集的原始样本数据流以每1024个样本点分帧得到长帧,逐帧应用汉宁窗以减少信号波动带来的干扰,随后对每个长帧进行快速傅里叶变换获得频谱图像,The original sample data streams of all data sets are divided into long frames at every 1024 sample points, and Hanning window is applied frame by frame to reduce the interference caused by signal fluctuations, and then each long frame is subjected to fast Fourier transform to obtain spectral images ,
进一步地,从频谱图中提取出信号带宽和中心频率这两个频谱特征,用于识别ZigBee信号的存在性。在一个实施例中,采用峰值查找法找出频谱特征。具体地,峰值查找法对比两个相邻点连线斜率的变化,若邻近斜率上升超过30%,则认定此为一个峰值,一张频谱分辨率为1024个点的ZigBee信号频谱图有若干峰值,峰值中心即是中心频率,峰值边界宽度即是带宽。利用高频谱分辨率下的带宽和中心频率能够容易地辨认出该帧是否存在ZigBee信号。例如,若信号带宽是2MHz,其中心频率落在2.435GHz附近,则可判定这段数据中存在ZigBee信号,则将标签设置成ZigBee(表示存在异种信号ZigBee),否则,无论是噪声还是WiFi信号,均标记为Noise标签。Further, two spectral features, the signal bandwidth and the center frequency, are extracted from the spectrogram to identify the existence of the ZigBee signal. In one embodiment, a peak finding method is used to find spectral features. Specifically, the peak search method compares the change of the slope of the line connecting two adjacent points. If the adjacent slope rises by more than 30%, it is regarded as a peak. A ZigBee signal spectrogram with a spectral resolution of 1024 points has several peaks. , the center of the peak is the center frequency, and the width of the peak boundary is the bandwidth. The presence or absence of a ZigBee signal in the frame can be easily identified using the bandwidth and center frequency at high spectral resolution. For example, if the signal bandwidth is 2MHz and its center frequency falls near 2.435GHz, it can be determined that there is a ZigBee signal in this data, and the label is set to ZigBee (indicating that there is a heterogeneous signal ZigBee), otherwise, whether it is noise or WiFi signal , are marked with the Noise label.
步骤S103,获取短帧及对应标签Step S103, obtaining short frames and corresponding labels
在步骤S102中,以1024个样本点划分的长帧具有较高的频谱分辨率,为满足较短时延的需求,将采取截短样本点的方式,获得每64个点一帧的短帧。此时,短帧的频谱图的频谱特征已不再明显,因此提取频谱带宽和中心频率的手段已不再可行。In step S102, the long frame divided by 1024 sample points has higher spectral resolution. In order to meet the requirement of shorter time delay, the method of truncating the sample points is adopted to obtain a short frame of every 64 points. . At this time, the spectral characteristics of the spectrogram of the short frame are no longer obvious, so the means of extracting the spectral bandwidth and the center frequency are no longer feasible.
在一个实施例中,借助长帧的标签来获取短帧的标签。例如,将1024个样本点的长帧再分为64个样本点的短帧时,只需标签扩充16倍即可得到短帧对应标签。由于一次ZigBee数据包连续占用约36个以1024点划分的帧,信号边界帧处于ZigBee信号和噪声的交界处,可能既包含ZigBee信号也包含噪声信号,在这种情况下,将边界长帧标签直接进行扩充将出现错误标签,为避免混淆且保证标签准确性,优选地,移除信号边界长帧及对应标签后再进行分短帧操作。In one embodiment, the label of the short frame is obtained by means of the label of the long frame. For example, when a long frame of 1024 sample points is subdivided into a short frame of 64 sample points, the corresponding label of the short frame can be obtained only by expanding the label by 16 times. Since a ZigBee data packet continuously occupies about 36 frames divided by 1024 points, the signal boundary frame is at the junction of ZigBee signal and noise, which may contain both ZigBee signal and noise signal. Direct expansion will cause wrong labels. In order to avoid confusion and ensure label accuracy, preferably, the signal boundary long frame and the corresponding label are removed, and then the short frame operation is performed.
步骤S104,数据向量化Step S104, data vectorization
在一个优选实施例中,对于步骤S103中获得短帧,进行向量化操作,以将IQ数据短帧变成方便CNN读取和使用的四维向量形式N
example× Dim
IQ×Dim
value×Dim
channel。其中Nexample代表短帧数量,Dim
IQ=2分别存储IQ两个通道的数据,Dim
value=64意味着每个短帧仅含64个样本点,Dim
channel=1是图像处理中代表黑白图像的值。例如,使用numpy库向量化短帧数据流后,采用cPickle库进行序列化存储到文件中供CNN模型备用。
In a preferred embodiment, for the short frame obtained in step S103, a vectorization operation is performed to convert the short frame of IQ data into a four-dimensional vector form N example × Dim IQ ×Dim value ×Dim channel which is convenient for CNN to read and use. Nexample represents the number of short frames, Dim IQ = 2 stores the data of the two IQ channels respectively, Dim value = 64 means that each short frame contains only 64 sample points, and Dim channel = 1 is the value representing black and white images in image processing . For example, after using the numpy library to vectorize the short frame data stream, the cPickle library is used to serialize and store it into a file for the CNN model to use.
对于CNN模型的具体网络结构参见图6所示,经过上述数据处理流程得到的短帧向量与对应标签(即向量化的样本数据集),将送入CNN模型中进行训练。利用CNN能找出微弱ZigBee信号的隐藏特征且识别速度快。参见图6,第一层网络是零填充层(Zero Padding)用以保存边界信息;然后,送入卷积核大小是(1,3)、步长是(1,1)的128个卷积滤波器组成的卷积层,并采用ReLU(线性整流函数)函数作激活函数引入非线性,此卷积层是提取信号隐藏特征的关键;随后,进行Dropout随机丢弃一半神经元以避免过拟合,有利于提升模型鲁棒性;再同样的进行一次补零、卷积,dropout操作,以获得更深层的、更具代表性的隐藏特征;随后,送入Flatten(压平)层将多维数据一维化便于后续全连接层使用;随后设置两个全连接层作为输出层,目的是将卷积层得到的高维特征加权求和得到每个类别的分数,再经过Softmax函数映射为概率。最终得到一个能在较短时间,以极少样本点有效识别ZigBee等异种信号的CNN模型。For the specific network structure of the CNN model, see Figure 6. The short frame vector and corresponding label (ie, the vectorized sample data set) obtained through the above data processing process will be sent to the CNN model for training. Using CNN can find the hidden features of weak ZigBee signals and the recognition speed is fast. Referring to Figure 6, the first layer of the network is a zero-padding layer (Zero Padding) to save boundary information; then, 128 convolutions with a convolution kernel size of (1,3) and a stride of (1,1) are input The convolution layer composed of filters, and the ReLU (linear rectification function) function is used as the activation function to introduce nonlinearity. This convolution layer is the key to extracting the hidden features of the signal; then, Dropout is performed to randomly discard half of the neurons to avoid overfitting. , which is beneficial to improve the robustness of the model; perform the same zero-padding, convolution, and dropout operations to obtain deeper and more representative hidden features; then, send the multi-dimensional data to the Flatten (flattening) layer One-dimensionalization is convenient for subsequent fully-connected layers; then two fully-connected layers are set as output layers, in order to obtain the weighted sum of the high-dimensional features obtained by the convolutional layer to obtain the scores of each category, and then map them to probabilities through the Softmax function. Finally, a CNN model that can effectively identify heterologous signals such as ZigBee with very few sample points in a short time is obtained.
二、关于异构网络中的共存机制设计2. Design of Coexistence Mechanism in Heterogeneous Networks
WiFi发送数据包前,将采样少量环境信号样本进行ED(EnergyDetection,能量检测)以判断信道是否空闲。在本发明实施例中,同时将环境信号采样点送入深度学***竞争信道。阈值的调低程度可根据网络的实际应用场景确定, 或根据仿真确定,本发明对此不进行限制。Before WiFi sends data packets, a small number of environmental signal samples will be sampled for ED (EnergyDetection, energy detection) to determine whether the channel is idle. In the embodiment of the present invention, the environmental signal sampling points are sent into the deep learning model to determine the existence of the ZigBee signal. If it does not exist, it will be business as usual. If it exists, the CNN classifier will set a ZigBeeOn flag and pass it to the MAC. (Medium Access Control, medium access control) layer, the MAC layer will lower the CCA (Clear Channel Assessment, idle channel detection) threshold, so that it will be more sensitive to ZigBee packets with low signal energy, so the energy detection will determine that the channel is busy, Trigger the backoff mechanism, delay the current transmission, and reserve the channel for ZigBee signal transmission, so as to avoid the phenomenon of the ZigBee data packet being destroyed caused by the simultaneous transmission of two protocols, and realize the fair competition of the channel between the two signals with unequal power. The lowering degree of the threshold may be determined according to the actual application scenario of the network, or determined according to simulation, which is not limited in the present invention.
由CNN模型识别出微弱的ZigBee信号正在信道中传输后,下一步便是设计一个合理的MAC层机制推迟WiFi信号的传输,防止其接入信道干扰ZigBee数据包的正常接收。由于WiFi设备也不能一直等待,必须找机会重新接入信道,以实现公平合理的异种协议信道分配方案。After the CNN model identifies that the weak ZigBee signal is being transmitted in the channel, the next step is to design a reasonable MAC layer mechanism to delay the transmission of the WiFi signal to prevent its access channel from interfering with the normal reception of ZigBee data packets. Since WiFi devices cannot wait all the time, they must find an opportunity to re-access the channel to achieve a fair and reasonable channel allocation scheme for heterogeneous protocols.
本发明设计的共存机制如图7所示,通过修改WiFi固有的DCF(Distributed Coordination Function,分布式协调功能)机制的参数,即降低CCA阈值使WiFi发射端对ZigBee微弱信号更敏感,从而判定信道繁忙并触发其固有的退避机制,推迟WiFi数据包的传输,让出信道供ZigBee数据传输。The coexistence mechanism designed by the present invention is shown in Figure 7. By modifying the parameters of the inherent DCF (Distributed Coordination Function) mechanism of WiFi, that is, reducing the CCA threshold, the WiFi transmitter is more sensitive to the weak ZigBee signal, so as to determine the channel Busy and triggers its inherent back-off mechanism, delaying the transmission of WiFi packets and giving up the channel for ZigBee data transmission.
具体地,当WiFi发送端有数据包需要传输时,其首先采样少量环境样本进行能量检测判定信道状态,此时由于ZigBee发送功率低于能量检测阈值,WiFi将判定信道空闲,并等待如协议802.11g/n规定的9微秒的DIFS(Distributed Inter-frame Spacing,分布式帧间间隙)。而在本发明实施例中,会在DIFS时间将用于能量检测的少量环境样本送入预训练的CNN模型中进行ZigBee信号存在性的判别,若存在则设置一个ZigBeeOn的标志位。WiFiMAC层接收到该标志位后将降低CCA阈值,此时发送端结束DIFS进入退避过程,并从竞争窗口中随机选择一个数值作随机回退计数器的值,退避时间由若干个退避时隙构成,每个退避时隙均会进行CCA,由于CCA阈值已被降低,因此将会感知到ZigBee信号的存在,认定信道繁忙,从而挂起随机回退计数器,推迟WiFi数据包的传输。通过这种方式,便不会出现WiFi信号与ZigBee信号同时存在于信道中的场景,因而解决了WiFi数据包对ZigBee接收端的ZigBee数据包毁坏的问题。Specifically, when the WiFi sender has a data packet to transmit, it first samples a small amount of environmental samples for energy detection to determine the channel state. At this time, since the ZigBee transmission power is lower than the energy detection threshold, the WiFi will determine that the channel is idle and wait for the protocol 802.11 DIFS (Distributed Inter-frame Spacing) of 9 microseconds specified by g/n. In the embodiment of the present invention, a small number of environmental samples used for energy detection are sent into the pre-trained CNN model during the DIFS time to determine the existence of the ZigBee signal, and if there is, a ZigBeeOn flag is set. The WiFiMAC layer will reduce the CCA threshold after receiving the flag bit. At this time, the sender ends the DIFS and enters the backoff process, and randomly selects a value from the contention window as the value of the random backoff counter. The backoff time consists of several backoff time slots. CCA will be performed in each backoff time slot. Since the CCA threshold has been lowered, the presence of the ZigBee signal will be sensed, and the channel will be determined to be busy, thus suspending the random backoff counter and delaying the transmission of WiFi data packets. In this way, there is no scenario in which the WiFi signal and the ZigBee signal exist in the channel at the same time, thus solving the problem that the WiFi data packet destroys the ZigBee data packet at the ZigBee receiving end.
相应地,本发明还提供一种实现异构网络共存的***,用于实现上述方法的一个方面和多个方面。例如,该***包括:信道状态检测单元,WiFi发送端基于采集的环境样本利用设定的第一空闲信道检测阈值判断信道状态;异种信号识别单元:在判断信道处于空闲状态的情况下,在所设置的分布式帧间间隙时间内,将所述环境样本输入到预训练的神经网络模型以识别是否存在异种信号;共存协议单元:在识别出存在异种信号的情况下, WiFi发送端将第一空闲信道检测阈值降低至第二空闲信道检测阈值,并结束所述分布式帧间间隙时间,进入退避过程,且在退避时隙利用第二空闲信道检测阈值检测信道状态,进而根据检测结果确定WiFi数据包的传输时机。本发明能够应用于实际部署的异构网络,也可用于实验室仿真、网络性能测试设备等。Correspondingly, the present invention also provides a system for realizing coexistence of heterogeneous networks, which is used to realize one aspect or multiple aspects of the above method. For example, the system includes: a channel state detection unit, where the WiFi sending end uses a set first idle channel detection threshold to determine the channel state based on the collected environmental samples; a heterogeneous signal identification unit: when it is determined that the channel is in an idle state, the In the set distributed inter-frame gap time, input the environment sample into the pre-trained neural network model to identify whether there is a heterogeneous signal; coexistence protocol unit: in the case of identifying the existence of a heterogeneous signal, the WiFi sender The idle channel detection threshold is reduced to the second idle channel detection threshold, the distributed inter-frame gap time is ended, the backoff process is entered, and the second idle channel detection threshold is used to detect the channel state in the backoff time slot, and then the WiFi is determined according to the detection result. The transmission timing of the packet. The present invention can be applied to actually deployed heterogeneous networks, and can also be used for laboratory simulation, network performance testing equipment, and the like.
为进一步验证本发明的效果,分别验证了异种信号识别和共存机制的有效性。In order to further verify the effect of the present invention, the effectiveness of the heterogeneous signal identification and coexistence mechanisms are respectively verified.
具体地,对于异种信号识别,从USRP N210和TelosB搭建的实验平台中获取无线协议数据,经由装载Intel Graphics 620的显卡和Intel Corei7-8550U的中央处理器对训练集进行离线训练获得卷积神经网络模型,再将测试集送入模型中得到如图8(a)所示的识别准确率和图8(b)所示的混淆矩阵(样本点为64),通过性能测试,得到如图8(c)所示的不同样本点数对应的运行时间。Specifically, for heterogeneous signal recognition, the wireless protocol data is obtained from the experimental platform built by USRP N210 and TelosB, and the convolutional neural network is obtained by offline training on the training set via the graphics card loaded with Intel Graphics 620 and the central processing unit of Intel Corei7-8550U. model, and then send the test set into the model to obtain the recognition accuracy shown in Figure 8(a) and the confusion matrix shown in Figure 8(b) (sample points are 64). c) The running times corresponding to different sample points are shown.
由图8(a)可知,所有测试集数据均得到99.9%以上的识别准确率,其中验证了使用相同数据集生成的不同样本点数对应的准确率,由于样本点数越小,相同数据集经由划分短帧操作所得的短帧向量数越多,所以对应的准确率相对较高。此外,在划分短帧操作时,在多个短帧中仅选取1个短帧,对应的不同样本点数所得的短帧数一致,如图8(a)所示,样本点数越高,频谱分辨率越大,对应的识别准确率就更高。图8(b)是样本点数是64个点时对应的混淆矩阵,ZigBee标签预测值和真实值一致,意味着所有ZigBee信号均被识别出来,同时也有0.0187%的噪声信号会被误认为是ZigBee信号,经分析是部分WiFi信号所致(WiFi信号的标签也是Noise,因为只关心是否存在ZigBee信号),但是占比过小,对性能的影响可忽略不计。图8(c)是模型运行每个短帧的平均时间,样本点数越长对应的运行时间就越大,样本点数是64个点时平均识别时间是66微秒,若采用专用神经网络芯片如TPU,速度将提升10倍以上,便可缩短至6us。实验证明,本发明利用卷积神经网络模型进行异种信号识别,完全符合设计目标,性能极优。It can be seen from Figure 8(a) that all the test set data obtained a recognition accuracy of more than 99.9%, which verified the accuracy of different sample points generated using the same data set. Since the smaller the number of sample points, the same data set is divided by The more short frame vectors obtained by the short frame operation, the higher the corresponding accuracy. In addition, when dividing short frames, only one short frame is selected from multiple short frames, and the number of short frames obtained by corresponding different sample points is the same, as shown in Figure 8(a), the higher the number of sample points, the higher the frequency spectrum The larger the resolution, the higher the corresponding recognition accuracy. Figure 8(b) is the corresponding confusion matrix when the number of sample points is 64 points. The predicted value of ZigBee label is consistent with the actual value, which means that all ZigBee signals are recognized, and 0.0187% of noise signals will be mistaken for ZigBee The signal, after analysis, is caused by part of the WiFi signal (the label of the WiFi signal is also Noise, because it only cares whether there is a ZigBee signal), but the proportion is too small, and the impact on the performance is negligible. Figure 8(c) is the average time for the model to run each short frame. The longer the number of sample points, the longer the corresponding running time. When the number of sample points is 64 points, the average recognition time is 66 microseconds. If a special neural network chip is used, such as TPU, the speed will be increased by more than 10 times, which can be shortened to 6us. Experiments show that the present invention uses the convolutional neural network model to identify heterogeneous signals, which fully complies with the design objectives and has excellent performance.
进一步地,验证了共存协议性能,采用仿真方式进行验证。仿真采用 ns-3库,仿真拓扑如图9所示,大体设置与信号采集实验一致,只是由于ns-3库的原因,若ZigBee收发节点距离小于2米易出现持续抢占信道的现象。故将默认距离设置成5米。仿真实验以WiFi的吞吐量作为性能指标,而由于ZigBee发送数据少,速率低,以吞吐量衡量不合适,因此优选采用PRR(PacketReceiveRate,数据包接收率)作衡量指标,
Further, the performance of the coexistence protocol is verified, and the verification is carried out by means of simulation. The simulation uses the ns-3 library, and the simulation topology is shown in Figure 9. The general settings are consistent with the signal acquisition experiment, but due to the ns-3 library, if the distance between the ZigBee transceiver nodes is less than 2 meters, the phenomenon of continuous channel preemption may occur. Therefore, the default distance is set to 5 meters. In the simulation experiment, the throughput of WiFi is used as the performance indicator, and since ZigBee sends less data and the rate is low, it is not suitable to measure the throughput. Therefore, it is preferable to use PRR (PacketReceiveRate, packet reception rate) as the measurement indicator.
具体的共存协议仿真过程是,首先修改WiFi发送端设置,验证各种WiFi标准发送速率下本发明的共存协议的性能。由图10(a)可知,相对于标准协议,本发明设计的协议为ZigBee的PRR带来两倍以上的提升,意味着该协议设计对ZigBee协议的收益极大。发送速率低时,WiFi吞吐量较标准协议有些许损失,发送速率较高时,带来的WiFi吞吐量的损失微乎其微。The specific coexistence protocol simulation process is as follows: firstly, modify the settings of the WiFi transmitter to verify the performance of the coexistence protocol of the present invention under various WiFi standard transmission rates. It can be seen from Fig. 10(a) that, compared with the standard protocol, the protocol designed by the present invention brings more than twice the improvement of the PRR of ZigBee, which means that the protocol design brings great benefits to the ZigBee protocol. When the sending rate is low, the WiFi throughput is slightly lost compared to the standard protocol. When the sending rate is high, the loss of WiFi throughput is minimal.
图10(b)是不同WiFi数据包长度的性能,此时WiFi数据传输速率已调回默认54Mbps。由图可知,包长越大,吞吐量越大,而本协议对WiFi吞吐量几乎无影响,并且采用本发明的共存机制协议将提升两倍以上的PRR,性能卓越。Figure 10(b) shows the performance of different WiFi data packet lengths. At this time, the WiFi data transmission rate has been adjusted back to the default 54Mbps. It can be seen from the figure that the larger the packet length, the larger the throughput, and this protocol has little effect on the WiFi throughput, and the use of the coexistence mechanism protocol of the present invention will increase the PRR by more than two times, with excellent performance.
接下来,改变实验拓扑结构,通过修改ZigBee设备之间的距离d
z,ZigBee发送节点与WiFi发送设备之间的距离d
wz,并观察对应的性能指标变化,得到如图11的不同拓扑距离的协议性能。
Next, change the experimental topology structure, by modifying the distance d z between ZigBee devices, the distance d wz between the ZigBee sending node and the WiFi sending device, and observe the changes of the corresponding performance indicators, get the different topological distances as shown in Figure 11 Protocol performance.
当d
z在2m以内时,接收端信噪比足够大,PRR接近100%;增至3m时采用本发明提出的协议能使PRR从90%升至100%;而增大d
z至4m以上,PRR开始急剧下降,采用本发明将提升近2倍的PRR。由于ZigBee发送端与WiFi发送端距离始终没变,因此吞吐量恒定,本发明的协议对吞吐量的影响极小。
When d z is within 2m, the signal-to-noise ratio of the receiving end is large enough, and the PRR is close to 100%; when it increases to 3m, the PRR can be increased from 90% to 100% by using the protocol proposed by the present invention; while increasing d z to more than 4m , the PRR begins to drop sharply, and the PRR will be increased by nearly 2 times by using the present invention. Since the distance between the ZigBee sender and the WiFi sender remains unchanged, the throughput is constant, and the protocol of the present invention has little impact on the throughput.
尽管当d
wz在2m以内时,若采用本发明的协议,ZigBee将持续占用信道,导致WiFi吞吐量降至0。所幸现实中WiFi发射端处于通常位于高处,极少与ZigBee发送端近距离接触的机会。而当d
wz是3m以上时,本发明提出的协议将使PRR提升两倍左右,WiFi吞吐量近乎不变。
Although when the d wz is within 2m, if the protocol of the present invention is adopted, ZigBee will continue to occupy the channel, resulting in the WiFi throughput dropping to 0. Fortunately, in reality, the WiFi transmitter is usually located at a high place, and there is little chance of close contact with the ZigBee transmitter. When d wz is more than 3m, the protocol proposed in the present invention will increase the PRR by about two times, and the WiFi throughput will be almost unchanged.
综上所述,本发明首次提出了将深度学***合理的信道争用策略。To sum up, the present invention proposes to apply deep learning to ZigBee signal recognition for the first time. With the help of the convolutional neural network model, the hidden features of a small number of ZigBee signal samples with low signal-to-noise ratio can be automatically extracted to realize the recognition of weak ZigBee signals at the microsecond level. The present invention applies the convolutional neural network model to wireless protocol identification, and provides a brand-new idea of heterogeneous signal identification. Further, the present invention designs a MAC layer coexistence protocol in combination with the deep learning identification results, and solves the problem of cross-protocol interference caused by power asymmetry by simply modifying the CCA threshold of the DCF mechanism adopted by WiFi, starting from the interference side, and realizing WiFi and ZigBee. The protocol is more fair and reasonable channel contention strategy.
相较现有的异种信号识别方案,本发明提出的基于深度学习的异种信号识别方案获得高达99.9%的识别准确率,且无需高频谱分辨率,实现微秒级别的极短时延。现有的共存机制设计方案或是需要额外的节点铺设,或是需要同时修改发射端与接收端,而本发明提出的共存协议设计,结合现有的DCF机制,仅需修改WiFi发射端,与当前商用设备兼容性高,更便于大规模推广和部署。Compared with the existing heterogeneous signal identification scheme, the deep learning-based heterogeneous signal identification scheme proposed by the present invention achieves a recognition accuracy rate as high as 99.9%, does not require high spectral resolution, and achieves extremely short delay at the microsecond level. The existing coexistence mechanism design scheme either requires additional nodes to be laid, or needs to modify the transmitter and the receiver at the same time, while the coexistence protocol design proposed by the present invention, combined with the existing DCF mechanism, only needs to modify the WiFi transmitter, and The current commercial equipment has high compatibility and is more convenient for large-scale promotion and deployment.
本发明可以是***、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质,其上载有用于使处理器实现本发明的各个方面的计算机可读程序指令。The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present invention.
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是――但不限于――电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括:便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身,诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如,通过光纤电缆的光脉冲)、或者通过电线传输的电信号。A computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above. Computer-readable storage media, as used herein, are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备,或者通过网络、例如因特网、局域网、广域网和/ 或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令,并转发该计算机可读程序指令,以供存储在各个计算/处理设备中的计算机可读存储介质中。The computer readable program instructions described herein may be downloaded to various computing/processing devices from a computer readable storage medium, or to an external computer or external storage device over a network such as the Internet, a local area network, a wide area network and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from a network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
用于执行本发明操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码,所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等,以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中,远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机,或者,可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中,通过利用计算机可读程序指令的状态信息来个性化定制电子电路,例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA),该电子电路可以执行计算机可读程序指令,从而实现本发明的各个方面。The computer program instructions for carrying out the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages. Source or object code, written in any combination, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as the "C" language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through the Internet connect). In some embodiments, custom electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), can be personalized by utilizing state information of computer readable program instructions. Computer readable program instructions are executed to implement various aspects of the present invention.
这里参照根据本发明实施例的方法、装置(***)和计算机程序产品的流程图和/或框图描述了本发明的各个方面。应当理解,流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合,都可以由计算机可读程序指令实现。Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器,从而生产出一种机器,使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时,产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中,这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作,从而,存储有指令的 计算机可读介质则包括一个制造品,其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer or other programmable data processing apparatus to produce a machine that causes the instructions when executed by the processor of the computer or other programmable data processing apparatus , resulting in means for implementing the functions/acts specified in one or more blocks of the flowchart and/or block diagrams. These computer readable program instructions can also be stored in a computer readable storage medium, these instructions cause a computer, programmable data processing apparatus and/or other equipment to operate in a specific manner, so that the computer readable medium on which the instructions are stored includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上,使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤,以产生计算机实现的过程,从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。Computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executing on a computer, other programmable data processing apparatus, or other device to implement the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.
附图中的流程图和框图显示了根据本发明的多个实施例的***、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上,流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分,所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中,方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如,两个连续的方框实际上可以基本并行地执行,它们有时也可以按相反的顺序执行,这依所涉及的功能而定。也要注意的是,框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合,可以用执行规定的功能或动作的专用的基于硬件的***来实现,或者可以用专用硬件与计算机指令的组合来实现。对于本领域技术人员来说公知的是,通过硬件方式实现、通过软件方式实现以及通过软件和硬件结合的方式实现都是等价的。The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more functions for implementing the specified logical function(s) executable instructions. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation in hardware, implementation in software, and implementation in a combination of software and hardware are all equivalent.
以上已经描述了本发明的各实施例,上述说明是示例性的,并非穷尽性的,并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下,对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择,旨在最好地解释各实施例的原理、实际应用或对市场中的技术改进,或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。本发明的范围由所附权利要求来限定。Various embodiments of the present invention have been described above, and the foregoing descriptions are exemplary, not exhaustive, and not limiting of the disclosed embodiments. Numerous modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (9)
- 一种实现异构网络共存的方法,包括以下步骤:A method for realizing coexistence of heterogeneous networks, comprising the following steps:步骤S1:WiFi发送端基于采集的环境样本利用设定的第一空闲信道检测阈值判断信道状态;Step S1: The WiFi sending end uses the set first idle channel detection threshold to determine the channel state based on the collected environment samples;步骤S2:在判断信道处于空闲状态的情况下,在所设置的分布式帧间间隙时间内,将所述环境样本输入到预训练的神经网络模型以识别是否存在异种信号;Step S2: in the case of judging that the channel is in an idle state, within the set distributed inter-frame gap time, input the environment sample into a pre-trained neural network model to identify whether there is a heterogeneous signal;步骤S3:在识别出存在异种信号的情况下,WiFi发送端将第一空闲信道检测阈值降低至第二空闲信道检测阈值,并结束所述分布式帧间间隙时间,进入退避过程,且在退避时隙利用第二空闲信道检测阈值检测信道状态,进而根据检测结果确定WiFi数据包的传输时机。Step S3: In the case of identifying the existence of heterogeneous signals, the WiFi transmitter reduces the first idle channel detection threshold to the second idle channel detection threshold, ends the distributed inter-frame gap time, enters the backoff process, and is in the backoff process. The time slot uses the second idle channel detection threshold to detect the channel state, and then determines the transmission timing of the WiFi data packet according to the detection result.
- 根据权利要求1所述的方法,其中,步骤S3包括以下子步骤:The method according to claim 1, wherein step S3 comprises the following sub-steps:设置异种信号存在标志位并传达至WiFi的媒质接入控制层;Set the heterogeneous signal existence flag and communicate it to the medium access control layer of WiFi;WiFi的媒质接入控制层将第一空闲信道检测阈值降低至第二空闲信道检测阈值,WiFi发送端结束所述分布式帧间间隙时间,进入退避过程,并从竞争窗口中随机选择一个数值作随机回退计数器的值,对于退避时间所包含的每个退避时隙均进行空闲信道检测,当检测到信道繁忙时,挂起随机回退计数器,推迟WiFi数据包的传输。The medium access control layer of WiFi reduces the first idle channel detection threshold to the second idle channel detection threshold, and the WiFi sender ends the distributed inter-frame gap time, enters the backoff process, and randomly selects a value from the contention window as the For the value of the random backoff counter, idle channel detection is performed for each backoff time slot included in the backoff time. When it is detected that the channel is busy, the random backoff counter is suspended to delay the transmission of WiFi data packets.
- 根据权利要求1所述的方法,其中,训练所述神经网络模型包括以下步骤:The method of claim 1, wherein training the neural network model comprises the steps of:构建样本数据集,所述样本数据集表征采集信号的频谱特征和异种信号存在性标签之间的对应关系,所采集的信号包括纯净的ZigBee信号数据、纯净的WiFi信号,以及包含ZigBee信号、底噪和WiFi信号的数据;A sample data set is constructed, and the sample data set represents the corresponding relationship between the spectral characteristics of the collected signal and the existence label of the heterogeneous signal, and the collected signal includes pure ZigBee signal data, pure WiFi signal, and includes ZigBee signal, background noise and WiFi signal data;对所述样本数据集进行向量化操作;performing a vectorization operation on the sample data set;利用向量化的样本数据集训练神经网络模型,获得满足设定优化目标的神经网络模型。Use the vectorized sample data set to train the neural network model to obtain the neural network model that satisfies the set optimization goal.
- 根据权利要求3所述的方法,其中,所述构建样本数据集包括:The method of claim 3, wherein said constructing a sample data set comprises:将采集的原始样本数据流以每1024个样本点分帧得到长帧,并对每个长帧进行快速傅里叶变换获得频谱图;Divide the collected original sample data stream into frames at every 1024 sample points to obtain long frames, and perform fast Fourier transform on each long frame to obtain a spectrogram;从频谱图中提取出信号带宽和中心频率作为频谱特征;Extract the signal bandwidth and center frequency from the spectrogram as spectral features;将1024个样本点的长帧再分为64个样本点的短帧,将长帧的标签扩充16倍作为短帧的标签。The long frame of 1024 sample points is subdivided into short frames of 64 sample points, and the label of the long frame is expanded by 16 times as the label of the short frame.
- 根据权利要求4所述的方法,其中,在执行分短帧操作之前,还包括移除信号边界长帧及对应标签。The method according to claim 4, wherein before performing the operation of dividing the short frame, it further comprises removing the signal boundary long frame and the corresponding label.
- 根据权利要求4所述的方法,其中,对所述样本数据集进行向量化操作包括:The method of claim 4, wherein performing a vectorization operation on the sample data set comprises:将所述样本数据集表示为四维向量形式N example×Dim IQ×Dim value×Dim channel,其中Nexample代表短帧数量,Dim IQ=2表示IQ两个通道的数据,Dim value=64表示每个短帧仅含64个样本点,Dim channel=1表示图像处理中代表黑白图像的值。 The sample data set is represented as a four-dimensional vector form N example ×Dim IQ ×Dim value ×Dim channel , where Nexample represents the number of short frames, Dim IQ =2 represents the data of two IQ channels, and Dim value =64 represents each short frame. The frame contains only 64 sample points, and Dim channel = 1 represents the value representing the black and white image in image processing.
- 根据权利要求1所述的方法,其中,所述神经网络模型依次包括:用于保存边界信息的零填充层;用于提取信号隐藏特征的卷积层,并采用线性整流函数进行非线性化;用于丢弃神经元的Dropout层;用于将多维数据一维化的压平层;以及用作输出层的两个全连接层。The method according to claim 1, wherein the neural network model sequentially comprises: a zero-padding layer for storing boundary information; a convolution layer for extracting hidden features of the signal, and using a linear rectification function for nonlinearization; A Dropout layer for dropping neurons; a flattening layer for making multidimensional data one-dimensional; and two fully connected layers used as output layers.
- 一种实现异构网络共存的***,包括:A system for realizing coexistence of heterogeneous networks, including:信道状态检测单元:WiFi发送端基于采集的环境样本利用设定的第一空闲信道检测阈值判断信道状态;Channel state detection unit: the WiFi sending end uses the set first idle channel detection threshold to determine the channel state based on the collected environment samples;异种信号识别单元:在判断信道处于空闲状态的情况下,在所设置的分布式帧间间隙时间内,将所述环境样本输入到预训练的神经网络模型以识别是否存在异种信号;Heterogeneous signal identification unit: in the case of judging that the channel is in an idle state, within the set distributed inter-frame gap time, the environment sample is input into the pre-trained neural network model to identify whether there is a heterogeneous signal;共存协议单元:在识别出存在异种信号的情况下,WiFi发送端将第一空闲信道检测阈值降低至第二空闲信道检测阈值,并结束所述分布式帧间间隙时间,进入退避过程,且在退避时隙利用第二空闲信道检测阈值检测信道状态,进而根据检测结果确定WiFi数据包的传输时机。Coexistence protocol unit: In the case of identifying the presence of heterogeneous signals, the WiFi transmitter reduces the first idle channel detection threshold to the second idle channel detection threshold, ends the distributed inter-frame gap time, enters the backoff process, and The backoff time slot uses the second idle channel detection threshold to detect the channel state, and then determines the transmission timing of the WiFi data packet according to the detection result.
- 一种计算机可读存储介质,其上存储有计算机程序,其中,该程序被处理器执行时实现根据权利要求1所述的方法的步骤。A computer-readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the steps of the method of claim 1 .
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---|---|---|---|---|
CN114584675A (en) * | 2022-05-06 | 2022-06-03 | 中国科学院深圳先进技术研究院 | Self-adaptive video enhancement method and device |
CN115022147A (en) * | 2022-05-30 | 2022-09-06 | 深圳大学 | Method for realizing cross-protocol coexistence based on WiFi payload coding |
CN115132231A (en) * | 2022-08-31 | 2022-09-30 | 安徽讯飞寰语科技有限公司 | Voice activity detection method, device, equipment and readable storage medium |
CN118070204A (en) * | 2024-04-23 | 2024-05-24 | 山东理工职业学院 | Unmanned aerial vehicle power data anomaly identification method and device based on neural network |
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080137620A1 (en) * | 2006-12-07 | 2008-06-12 | Kiyon, Inc. | System and Method for Timeslot and Channel Allocation |
WO2014028656A2 (en) * | 2012-08-17 | 2014-02-20 | Illinois Tool Works Inc. | Wireless communication network for control of industrial equipment in harsh environments |
CN104902545A (en) * | 2015-05-27 | 2015-09-09 | 厦门盈趣科技股份有限公司 | Coexisting method for Zigbee and WiFi (Wireless Fidelity) |
CN108934046A (en) * | 2017-05-23 | 2018-12-04 | 中兴通讯股份有限公司 | A kind of method and device for reducing WiFi and ZigBee and interfering with each other |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102183294B (en) * | 2011-03-15 | 2013-03-13 | 浙江大学 | Environment noise wireless real-time monitoring system based on ZigBee technology |
US8666319B2 (en) * | 2011-07-15 | 2014-03-04 | Cisco Technology, Inc. | Mitigating effects of identified interference with adaptive CCA threshold |
US10361835B2 (en) * | 2017-06-19 | 2019-07-23 | Mitsubishi Electric Research Laboratories, Inc. | System for coexistence of Wi-Fi HaLow network and low-rate wireless personal area network (LR-WPAN) |
CN107682830B (en) * | 2017-09-27 | 2019-10-18 | 清华大学 | A kind of communication means and device of ZigBee to WiFi |
CN110197127B (en) * | 2019-05-06 | 2022-10-18 | 安徽继远软件有限公司 | Wireless signal detection and electromagnetic interference classification system and method based on deep learning |
-
2020
- 2020-08-18 CN CN202010830054.6A patent/CN111935721B/en active Active
- 2020-09-21 WO PCT/CN2020/116547 patent/WO2022036801A1/en active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080137620A1 (en) * | 2006-12-07 | 2008-06-12 | Kiyon, Inc. | System and Method for Timeslot and Channel Allocation |
WO2014028656A2 (en) * | 2012-08-17 | 2014-02-20 | Illinois Tool Works Inc. | Wireless communication network for control of industrial equipment in harsh environments |
CN104902545A (en) * | 2015-05-27 | 2015-09-09 | 厦门盈趣科技股份有限公司 | Coexisting method for Zigbee and WiFi (Wireless Fidelity) |
CN108934046A (en) * | 2017-05-23 | 2018-12-04 | 中兴通讯股份有限公司 | A kind of method and device for reducing WiFi and ZigBee and interfering with each other |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114584675A (en) * | 2022-05-06 | 2022-06-03 | 中国科学院深圳先进技术研究院 | Self-adaptive video enhancement method and device |
CN115022147A (en) * | 2022-05-30 | 2022-09-06 | 深圳大学 | Method for realizing cross-protocol coexistence based on WiFi payload coding |
CN115022147B (en) * | 2022-05-30 | 2023-07-25 | 深圳大学 | Method for realizing cross-protocol coexistence based on WiFi payload coding |
CN115132231A (en) * | 2022-08-31 | 2022-09-30 | 安徽讯飞寰语科技有限公司 | Voice activity detection method, device, equipment and readable storage medium |
CN115132231B (en) * | 2022-08-31 | 2022-12-13 | 安徽讯飞寰语科技有限公司 | Voice activity detection method, device, equipment and readable storage medium |
CN118070204A (en) * | 2024-04-23 | 2024-05-24 | 山东理工职业学院 | Unmanned aerial vehicle power data anomaly identification method and device based on neural network |
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CN111935721A (en) | 2020-11-13 |
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