WO2015090022A1 - 一种资源调度的方法、装置和计算机存储介质 - Google Patents
一种资源调度的方法、装置和计算机存储介质 Download PDFInfo
- Publication number
- WO2015090022A1 WO2015090022A1 PCT/CN2014/080332 CN2014080332W WO2015090022A1 WO 2015090022 A1 WO2015090022 A1 WO 2015090022A1 CN 2014080332 W CN2014080332 W CN 2014080332W WO 2015090022 A1 WO2015090022 A1 WO 2015090022A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- resources
- scheduling
- time window
- prediction
- resource
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 84
- 238000013468 resource allocation Methods 0.000 claims abstract description 18
- 230000007774 longterm Effects 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 12
- 238000009499 grossing Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 208000037918 transfusion-transmitted disease Diseases 0.000 description 9
- 238000001514 detection method Methods 0.000 description 5
- 238000012935 Averaging Methods 0.000 description 4
- 238000013507 mapping Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000010295 mobile communication Methods 0.000 description 2
- 101710165631 Dihydropyrimidine dehydrogenase [NADP(+)] Proteins 0.000 description 1
- 101710183660 NAD-dependent dihydropyrimidine dehydrogenase subunit PreA Proteins 0.000 description 1
- 101710183648 NAD-dependent dihydropyrimidine dehydrogenase subunit PreT Proteins 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/12—Wireless traffic scheduling
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
- H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Definitions
- the present invention relates to wireless communication technologies, and in particular, to a resource scheduling method, apparatus, and computer storage medium. Background technique
- LTE Long Term Evolution
- the downlink structure of the LTE base station system is shown in FIG. 1.
- the baseband subsystem 101 performs processing such as scheduling and modulation on the data from the core network.
- the intermediate frequency processor 102 filters, upconverts, etc. the digital signals received by the baseband subsystem 101.
- DPD digital predistortion
- DAC Digital-to-Analog Converter
- RF link 106 performs analog frequency conversion processing on the analog signal
- PA Power Amplifier
- PA Power Amplifier
- the radio frequency system controller 103 performs control of the various units of the radio frequency system.
- PA is one of the devices with high power consumption of base station equipment in communication system. Reducing the power consumption of power amplifier and improving the efficiency of power amplifier is one of the effective means to improve the efficiency of LTE base station system.
- the traditional power amplifier uses a constant bias voltage. In order to reduce the power consumption of the power amplifier, the bias voltage of the power amplifier can be dynamically adjusted according to the actual output power, which not only ensures the normal operation of the system, but also reduces the power consumption of the power amplifier, and improves the LTE. The efficiency of the base station system.
- the method of symbol power detection and radio frame level adjustment is usually as follows: fast power calculation is performed with OFDM symbol duration as the period, and corresponding power is obtained; according to the set query time, the query time is counted. The maximum symbol power in the segment; the power level is obtained according to the maximum symbol power in the query period; the power amplifier bias voltage is mapped according to the obtained power level; and the voltage regulation information is generated according to the mapped power amplifier bias voltage, and the voltage regulation is performed. The information is transferred to the power module to adjust the bias voltage of the PA.
- the system bandwidth of the LTE system of 20MHz it includes 100 resource blocks (RB, Resource Block) 0 downlink traffic requires 110 RBs, some of the scheduling system is: before a transmission time interval (TTI, Transmission Time Interval) to 100RB is allocated, and 10RB is allocated when the next TTI.
- TTI Transmission Time Interval
- the symbol power when 100 RB is used is much higher than the symbol power when 10 RB is used. If there are too many RBs allocated in one radio frame length (10ms), and the number of RBs allocated in the other 9 times is small, the burst OFDM symbol power is high, and the time is high. The case where the average symbol power is not large within the length.
- the embodiments of the present invention mainly provide a resource scheduling method, a device, and a computer storage medium.
- the base station predicts the maximum amount of resources that can be used for the next scheduling according to the number of historically scheduled resources.
- the resource allocation is performed according to the current service condition of the base station and the maximum upper limit of the resource at the next scheduling.
- the device for resource scheduling provided by the embodiment of the invention includes: a prediction module and a resource allocation module; wherein
- the prediction module is configured to predict, according to the number of historical scheduling resources, a maximum upper limit of resources that can be used for the next scheduling;
- the resource allocation module is configured to perform resource allocation according to a current service condition of the base station and a maximum upper limit of resources in the next scheduling.
- the embodiment of the invention further provides a computer storage medium, wherein a computer program for executing the resource scheduling method of the above embodiment is stored.
- the embodiment of the present invention provides a method, a device, and a computer storage medium for resource scheduling.
- the base station predicts the maximum upper limit of resources that can be used for the next scheduling according to the number of historical scheduling resources, and according to the current service situation of the base station and the resources in the next scheduling.
- the maximum upper limit is used for resource allocation; thus, the number of previous historical scheduling resources can be referred to in the next scheduling, and when the PA bias voltage is adjusted by using symbol power detection and wireless frame level adjustment, the average power is small but OFDM is present.
- the symbol power is high, the problem that the PA bias voltage adjustment range is small or even the PA bias voltage adjustment cannot be performed, the power consumption of the power amplifier is reduced, and the efficiency of the power amplifier is improved.
- FIG. 1 is a schematic structural diagram of a downlink structure of an LTE base station system in the prior art
- FIG. 2 is a schematic flowchart of implementing a resource scheduling method according to an embodiment of the present invention
- FIG. 3 is a schematic structural diagram of a resource scheduling apparatus according to an embodiment of the present invention. detailed description
- the base station predicts that the next scheduling can be used according to the number of historical scheduling resources.
- the maximum upper limit of resources is allocated according to the current service condition of the base station and the maximum upper limit of the resources at the next scheduling.
- the embodiment of the present invention implements a resource scheduling method. As shown in FIG. 2, the method includes the following steps:
- Step 201 The base station predicts, according to the number of historical scheduling resources, a maximum upper limit of resources that can be used for the next scheduling.
- the base station acquires the length of the time window input by the user and the prediction method, and predicts the maximum upper limit of resources that can be used for the next scheduling according to the prediction method and the number of historical scheduling resources in the time window;
- the base station obtains the length of the time window input by the user and the prediction method may be: the base station obtains the length of the time window input by the user from the text box of the network management, and obtains the prediction method input by the user from the option list of the network management;
- the time window includes: a long time window and/or a short time window;
- the length of the short time window is equal to M TTIs, and ⁇ is a positive integer;
- the length of the long time window is equal to the length of the short time window of ⁇ times, and ⁇ is a positive integer;
- the prediction method includes: a moving average method, and/or a trend averaging method, and/or a weighted averaging method, and/or a smoothing index method;
- the corresponding prediction method can be selected according to the change of the number of long-term historical scheduling resources in different regions. For example, when the number of historical scheduling resources varies greatly in a region, the weighted average method may be used, so that the number of historical scheduling resources that are closer to the next scheduling is larger in the prediction, and the next scheduling is performed.
- the number of distant historical scheduling resources accounts for a small weight.
- the maximum upper limit of the resources that can be used for the next scheduling is predicted according to the prediction method and the number of historical scheduling resources in the time window, which may be specifically:
- the base station predicts the short time window according to the prediction method and the number of historical scheduling resources in the short time window Scheduling the number of resources, predicting the number of scheduling resources in the long-term window according to the predicted number of scheduling resources in the short-time window, and according to the predicted number of scheduling resources in the short-time window, the number of scheduling resources in the predicted long-time window, The number of bandwidth resources and the amount of resource compensation predict the maximum upper limit of resources that can be used for the next scheduling;
- the number of the historical scheduling resources is: the number of recently scheduled resources in the time window; and the scheduling resources in the short time window in the number of scheduling resources in the long time window according to the number of scheduling resources in the short time window The number is: The number of scheduled resources in the short-term window that was recently predicted.
- the base station predicts the number of scheduling resources in the short time window according to the prediction method and the number of historical scheduling resources in the short time window, specifically:
- the number of scheduling resources in the short time window is predicted to be the total bandwidth resource number
- the number of scheduling resources in the short time window is predicted ( ⁇ 1) as:
- Ti represents the length of the short-time window
- the resource generally refers to an RB resource;
- the scheduling time indicates a number of times of scheduling, and the total is continuously accumulated as the scheduling continues;
- the total bandwidth resource number is a number of resources included in the system bandwidth, for example, when the system bandwidth When it is 20MHz, the total bandwidth resource is 100 RBs.
- the prediction method is the moving average method
- the predicting the number of scheduling resources in the long time window according to the number of scheduling resources in the short time window is specifically:
- the scheduling resource in the long time window is predicted
- the number is the total number of bandwidth resources
- the predicted scheduling resource number MJ(J2) in the long time window is:
- the predicted number of scheduling resources MJ(J2) in the long-time window is:
- the base station predicts the number of scheduling resources in the short time window according to the prediction method and the number of historical scheduling resources in the short time window, specifically:
- the number of scheduling resources in the short time window is predicted to be the total bandwidth resource number
- ⁇ represents the length of the short-time window
- (t) represents the actual number of scheduled resources in the t-th historical scheduling period
- ( ⁇ represents the weight of the actual number of scheduled resources in the t-th historical scheduling period; if from a short time window
- the number of historical scheduling resources that are closer to the next scheduling can be larger, and the number of historical scheduling resources that are farther away from the next scheduling account for a smaller weight.
- the prediction method is a weighted average method
- the predicting the number of scheduling resources in the long time window according to the number of scheduling resources in the short time window is specifically:
- the number of scheduling resources in the long time window is predicted to be the total bandwidth resource number
- the number of scheduling resources in the long-term window is predicted to be MJ(J2):
- ? ( ) represents the number of scheduling resources in the short time window of the kth prediction
- KL(k) represents the weight of the number of scheduling resources in the short time window of the kth prediction, if according to the order of prediction
- J(1) represents the number of scheduling resources in the short-time window of the latest prediction.
- M represents the integer part of the scheduling time T divided by the length T1 of the short-time window.
- the number of scheduled resources MJ(J2) in the long-term window is predicted to be:
- ? ( ) represents the number of scheduling resources in the short time window of the kth prediction
- KL(k) represents the weight of the number of scheduling resources in the short time window of the kth prediction, if according to the order of prediction
- the first prediction is defined as T2 ⁇ T1
- MS represents the number of scheduling resources in the short-time window of the latest prediction
- J(l) represents the number of scheduling resources in the short-time window of the latest prediction.
- the maximum upper limit of the resources that can be used for the next scheduling is predicted according to the number of scheduled resources in the predicted short time window, the number of scheduled resources in the predicted long time window, the total bandwidth resources, and the resource compensation amount, which are specifically:
- the maximum upper limit R5_£ of the resources that can be used for the next scheduling is predicted to be:
- MJ indicates the number of scheduled resources in the predicted long-term window; indicates the number of full-bandwidth resources; indicates the amount of resource compensation.
- the maximum upper limit R5_£ of the resources that can be used for the next scheduling is predicted to be:
- RB_E mm (MS(Tl) + ARB, RB ALL ) where: ?( ⁇ 1) indicates the number of scheduled resources within the predicted short time window; indicates full bandwidth Number of resources; indicates the amount of resource compensation.
- the resource compensation amount is determined by acquiring user input, and the resource compensation amount may be dynamically adjusted according to the predicted number of scheduling resources in the short time window and the actual scheduling resource number in the corresponding scheduling period;
- the prediction difference value in the short time window is obtained according to the stored number of scheduling resources in the short time window of the previous prediction and the actual number of scheduling resources in the corresponding scheduling period, and the data is short
- the predicted difference in the time window adjusts the amount of resource compensation
- the corresponding scheduling period represents a scheduling period in which the short time window is located, for example, the short time window of the last prediction corresponds to the last scheduling period;
- the obtaining the predicted difference value MWL in the short time window according to the stored number of scheduled resources in the short time window of the previous prediction and the actual scheduling resource amount in the corresponding scheduling period is:
- T1 represents the length of the short time window
- ?() represents the number of scheduling resources in the short time window of the kth prediction, and represents the actual number of scheduled resources in the kth historical scheduling period, if the first time is defined according to the prediction order
- the prediction is T1
- MS represents the number of scheduling resources in the short-time window of the latest prediction
- ⁇ (1) represents the number of resources of the last scheduled actual scheduling, if the first is defined according to the prediction order
- the secondary prediction is 1
- MS(T T1 ) represents the number of scheduling resources in the short-time window of the latest prediction
- ( ⁇ 1) indicates the number of resources that were scheduled for the last scheduling.
- the adjusting the resource compensation amount according to the predicted difference value in the short time window to: when the predicted difference value in the short time window is greater than the predetermined value, reducing the resource compensation amount by a predetermined step; when in the short time window When the predicted difference is less than the predetermined value, the resource compensation amount is increased by a predetermined step size;
- the predetermined value and the predetermined step size may be determined by user input;
- the predetermined value is 4, the predetermined step size is 1, and when the predicted difference value in the short time window is 8, Then reduce the amount of resource compensation by one;
- the predetermined value is 2, and the predetermined step size is 1.
- the resource compensation amount is incremented by one.
- Step 202 Perform resource allocation according to the current service condition of the base station and the maximum upper limit of the resource in the next scheduling.
- resource allocation and resource location allocation are performed according to the current service condition of the base station and the maximum upper limit of the resource;
- the current service situation is a state in which each user equipment (UE, User Equipment) that is prioritized is waiting for scheduling;
- the allocation of the number of resources according to the current service condition of the base station and the maximum upper limit of the resources belongs to the prior art, and the present invention replaces the maximum number of available resources of the system bandwidth by using the maximum upper limit of the resources that can be used predicted in step 201;
- the resource location allocation may be: the base station maps the allocated virtual resources to an actual physical resource location by using a mapping manner;
- the mapping manner includes: distributed and/or centralized.
- the embodiment of the present invention further provides a device for resource scheduling.
- the device is located in a base station scheduler, and includes: a prediction module 301, a resource allocation module 302;
- the prediction module 301 is configured to predict, according to the number of historical scheduling resources, a maximum upper limit of resources that can be used for the next scheduling;
- the resource allocation module 302 is configured to perform resource allocation according to the current service condition of the base station and the maximum upper limit of the resource at the next scheduling;
- the prediction module 301 is configured to acquire a length of a time window input by the user and a prediction method, and predict a maximum upper limit of resources that can be used for the next scheduling according to the prediction method and the number of historical scheduling resources in the time window;
- the prediction module 301 may specifically: obtain a length of a time window input by the user from the text box, and obtain a prediction method input by the user from the option list;
- the time window includes: a long time window and/or a short time window;
- the length of the short time window is equal to M TTIs, and ⁇ is a positive integer;
- the length of the long time window is equal to the length of the short time window of ⁇ times, and ⁇ is a positive integer;
- the prediction method includes: a moving average method, and/or a trend averaging method, and/or a weighted averaging method, and/or a smoothing index method;
- the corresponding prediction method can be selected according to the change of the number of long-term historical scheduling resources in different regions. For example, when the number of historical scheduling resources varies greatly in a region, the weighted average method may be used, so that the number of historical scheduling resources that are closer to the next scheduling is larger in the prediction, and the next scheduling is performed.
- the prediction module 301 may be configured to: predict the number of scheduling resources in the short time window according to the prediction method and the number of historical scheduling resources in the short time window, according to the short time
- the number of scheduling resources in the window predicts the number of scheduling resources in the long-time window, and predicts the next scheduling according to the number of scheduling resources in the short-time window, the number of scheduling resources in the long-time window, the number of full-bandwidth resources, and the amount of resource compensation.
- the number of the historical scheduling resources is: the number of recently scheduled resources in the time window; wherein, the predicting the number of scheduling resources in the long time window according to the number of scheduling resources in the short time window, in the short time window
- the number of scheduling resources is: The number of scheduling resources in the short-term window that is recently predicted.
- the prediction module 301 is specifically configured to: when the scheduling time ⁇ is less than the length T1 of the short time window, predict the number of scheduling resources in the short time window as the total bandwidth resource number;
- T1 represents the length of the short-time window
- (t) represents the actual number of scheduled resources in the t-th historical scheduling period, and if the number of scheduling resources per TTI is accumulated from the right side of the short-time window to the left,
- the resource generally refers to an RB resource;
- the scheduling time indicates a number of times of scheduling, and the total is continuously accumulated as the scheduling continues;
- the total bandwidth resource number is a number of resources included in the system bandwidth, for example, when the system bandwidth When it is 20MHz, the total bandwidth resource is 100 RBs.
- the prediction module 301 is specifically configured to: when the scheduling time T is less than the length T1 of the short time window, predict the number of scheduling resources in the long time window as the total bandwidth resource number;
- the predicted scheduling resource number MJ(J2) in the long time window is:
- the prediction module 301 is specifically configured to: when the scheduling time T is less than the length T1 of the short time window, predict the number of scheduling resources in the short time window as the total bandwidth resource number;
- the number of scheduling resources in the short time window is predicted ((1) is: (X)
- Ti represents the length of the short-time window
- (t) represents the actual number of scheduled resources in the t-th historical scheduling period
- ( ⁇ represents the weight of the actual number of scheduled resources in the t-th historical scheduling period; if from a short time window
- ⁇ (1) indicates the number of resources that were actually scheduled for the last time, if the scheduling of each TTI is accumulated from the left to the right of the short time window.
- the number of historical scheduling resources that are closer to the next scheduling can be larger, and the number of historical scheduling resources that are farther away from the next scheduling account for a smaller weight.
- the prediction module 301 is specifically configured to: when the scheduling time ⁇ is less than the length T1 of the short time window, predict the number of scheduling resources in the long time window as the total bandwidth resource number;
- ?( ) represents the number of scheduling resources in the short time window of the kth prediction
- KLI ⁇ represents the weight of the number of scheduling resources in the short time window of the kth prediction, if according to the prediction
- the order of definition defines the first prediction as M.
- MS represents the number of scheduling resources in the short-time window of the most recent prediction
- J(1) represents the scheduling resources in the short-time window of the latest prediction.
- the number of scheduling resources MJ(J2) in the long-term window is predicted as:
- ?( ) represents the number of scheduling resources in the short time window of the kth prediction
- KLI ⁇ represents the weight of the scheduling resources in the short time window of the kth prediction, if according to the prediction
- J(l) represents the short-time window of the most recent prediction.
- the number, ( ⁇ 2 ⁇ ⁇ 1) represents the weight of the number of scheduled resources in the short-time window of the most recent prediction.
- the prediction module 301 is specifically configured to: When the number of scheduled resources in the predicted long time window is greater than or equal to the number of scheduled resources in the predicted short time window, the maximum upper limit R5_£ of the resources that can be used for the next scheduling is predicted to be:
- RB_E mm (ML(T2) + ARB, RB ML ) where: MJ(J2) represents the number of scheduling resources in the long-term window; represents the total bandwidth resource; A RB represents the resource compensation amount.
- the maximum upper limit R5_£ of the resources that can be used for the next scheduling is predicted to be:
- RB_E mm (MS(Tl) + ARB, RB ALL ) where: ?( ⁇ 1) indicates the number of scheduling resources in a short time window; indicates the number of full bandwidth resources; A RB indicates the amount of resource compensation.
- the resource compensation amount is determined by acquiring user input
- the prediction module 301 is further configured to dynamically adjust according to the predicted number of scheduling resources in the short time window and the actual scheduling resource number in the corresponding scheduling period, which may be: before predicting the number of scheduling resources in the short time window, according to The stored predicted number of scheduling resources in the short time window and the actual scheduling resource number in the corresponding scheduling period obtain the predicted difference in the short time window, and adjust the resource compensation amount according to the predicted difference in the short time window;
- the corresponding scheduling period represents a scheduling period in which the short time window is located, for example, the short time window of the last prediction corresponds to the last scheduling period;
- the predicting difference MWL in the short time window is obtained according to the stored number of scheduling resources in the short time window of the previous prediction and the actual scheduling resource number in the corresponding scheduling period: ( ) — ) I
- T1 represents the length of the short time window
- ?() represents the number of scheduling resources in the short time window of the kth prediction, and represents the actual number of scheduled resources in the kth historical scheduling period, if the first time is defined according to the prediction order
- the prediction is T1
- MS indicates the most recent one.
- ⁇ (1) indicates the number of resources of the last scheduled actual scheduling.
- the adjusting the resource compensation amount according to the predicted difference value in the short time window to: when the predicted difference value in the short time window is greater than the predetermined value, reducing the resource compensation amount by a predetermined step; when in the short time window When the predicted difference is less than the predetermined value, the resource compensation amount is increased by a predetermined step size;
- the predetermined value and the predetermined step size may be determined by user input;
- the predetermined value is 4, the predetermined step size is 1, and when the predicted difference value is 8 in the short time window, the resource compensation amount is decreased by 1;
- the predetermined value is 2, and the predetermined step size is 1.
- the resource compensation amount is increased by 1;
- the resource allocation module 302 is specifically configured to perform resource number allocation and resource location allocation according to the current service condition of the base station and the maximum upper limit of the resource in the next scheduling;
- the current service situation is a state in which each user equipment that is prioritized is waiting for scheduling in the downlink;
- the resource allocation according to the current service condition of the base station and the maximum resource ceiling of the resource belongs to the prior art, and the present invention replaces the system bandwidth available resources by using the maximum upper limit of the resources that can be used predicted by the prediction module 301.
- the resource allocation module 302 is specifically configured to map the allocated virtual resources to actual physical resource locations according to a mapping manner
- the mapping manner includes: distributed and/or centralized.
- the prediction module 301 can be implemented by a central processing unit (CPU) and a memory of the base station scheduler, and the resource allocation module 302 can be implemented by a CPU and a scheduling interface of the base station scheduler.
- the resource scheduling method according to the embodiment of the present invention may also be stored in a computer readable storage medium if it is implemented in the form of a software function module and sold or used as a stand-alone product. Based on such understanding, those skilled in the art will appreciate that embodiments of the present application can be provided as a method, system, or computer program product. Thus, the application can be in the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware.
- the application can be in the form of a computer program product embodied on one or more computer usable storage media having computer usable program code, including but not limited to a USB flash drive, a removable hard drive, a read only memory (ROM, Read-Only Memory), disk storage, CD-ROM, optical storage, etc.
- a USB flash drive a removable hard drive
- a read only memory ROM, Read-Only Memory
- disk storage CD-ROM, optical storage, etc.
- the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
- the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
- These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
- the instructions are provided for implementation in a block or blocks of a flow or a flow and/or a block diagram of the flowchart Functional steps.
- the embodiment of the present invention further provides a computer storage medium, wherein a computer program is stored, and the computer program is used to execute the resource scheduling method of the embodiment of the present invention.
- the base station can refer to the previous historical scheduling resource number in the next scheduling, and when the PA bias voltage adjustment is performed by using the symbol power detection and the wireless frame level adjustment, the average power is prevented from being small but the OFDM symbol power is very high. In the high case, the problem that the PA bias voltage adjustment range is small or even the PA bias voltage adjustment cannot be performed, the power consumption of the power amplifier is reduced, and the efficiency of the power amplifier is improved.
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明公开了一种资源调度的方法,基站根据历史调度资源数预测下一次调度能够使用的资源的最大上限,在下一次调度时根据基站当前业务情况以及所述资源的最大上限进行资源分配;本发明同时还公开了一种资源调度的装置和计算机存储介质,该装置包括预测模块和资源分配模块。
Description
一种资源调度的方法、 装置和计算机存储介质 技术领域
本发明涉及无线通信技术, 尤其涉及一种资源调度的方法、 装置和计 算机存储介质。 背景技术
随着移动通信迅速发展, 长期演进(LTE, Long Term Evolution )移动 通讯技术越来越受到通讯行业的关注。 LTE基站***下行链路结构如图 1 所示, 基带子*** 101 对来自核心网的数据进行调度、 调制等处理; 中频 处理器 102对接收到基带子*** 101的数字信号进行滤波、 上变频等处理; 数字预失真(DPD, Digital Predistortion )处理器 104对接收到中频处理器 102变频后的数字信号进行削峰和数字预失真处理;数字模拟转化器( DAC, Digital-to-Analog Converter ) 105对数字信号进行数字到模拟的转换处理; 射频链路 106 对模拟信号进行模拟变频处理; 功率放大器 (PA, Power Amplifier ) 107对模拟射频信号进行功率放大; 天线 108对放大的模拟信号 进行发射; 射频***控制器 103完成对射频***各个单元的控制。
其中, PA是通信***基站设备功耗较大的器件之一, 降低功放功耗、 提高功放的效率是提升 LTE基站***效率的有效手段之一。 传统的功放釆 用恒定的偏置电压, 为了降低功放功耗, 可以根据实际输出功率动态的调 整功放的偏置电压, 这样既保证了***的正常运行, 又降低了功放功耗, 提升了 LTE基站***的效率。
为了使节能效果达到最优, 理论上根据每个正交频分复用 (OFDM, Orthogonal Frequency Division Multiplexing )符号功率来调整功放偏置电压 可以使节能效果达到最优。 但是符号级调整对射频拉远单元(RRU, Radio
Remote Unit )电源模块的调整速度和 DPD的处理能力要求都很高, 大部分 的电源模块和 DPD都达不到这个要求, 所以一般都釆用符号功率检测、 无 线帧级调整的方式来进行 PA偏置电压的调整。
针对 LTE基带信号时域波动剧烈的特性, 符号功率检测、 无线帧级调 整的方式通常为: 以 OFDM符号时长为周期快速进行功率计算, 获取相应 的功率; 依据设定的查询时间, 统计查询时间段内的最大符号功率; 根据 查询时间段内的最大符号功率来获取功率等级; 根据获取的功率等级映射 出功放偏置电压; 再根据映射出的功放偏置电压生成调压信息, 将调压信 息传送至电源模块, 对 PA的偏置电压进行调整。
对于***带宽为 20MHz 的 LTE ***, 共包括 100个资源块(RB, Resource Block )0 当下行业务需要使用 110RB时, 一些***的调度方式为: 前一个传输时间间隔(TTI, Transmission Time Interval )先分配 100RB, 下 一个 TTI时再分配 10RB。这样,使用 100RB时的符号功率比使用 10RB时 的符号功率就会高很多。 如果在一个无线帧的时间长度(10ms ) 内有一次 分配的 RB数过多, 而其他 9次分配的 RB数都很少时, 就会导致突发的 OFDM符号功率很高, 而所述时间长度内平均符号功率不大的情况。 在这 种情况下, 当使用上述的符号功率检测、 无线帧级调整的方式调整 PA偏置 电压时, 突发的几次 OFDM符号功率就会导致 PA偏置电压调节范围很小 甚至不能进行 PA偏置电压调节的问题。 发明内容
为解决现有存在的技术问题, 本发明实施例主要提供一种资源调度方 法、 装置和计算机存储介质。
本发明实施例的技术方案是这样实现的:
本发明实施例提供的一种资源调度的方法, 该方法包括:
基站根据历史调度资源数预测下一次调度能够使用的资源的最大上
限, 在下一次调度时根据基站当前业务情况以及所述资源的最大上限进行 资源分配。
本发明实施例提供的一种资源调度的装置, 该装置包括: 预测模块、 资源分配模块; 其中,
所述预测模块, 配置为根据历史调度资源数预测下一次调度能够使用 的资源的最大上限;
所述资源分配模块, 配置为在下一次调度时根据基站当前业务情况以 及资源的最大上限进行资源分配。
本发明实施例还提供一种计算机存储介质, 其中存储有计算机程序, 该计算机程序用于执行上述实施例的资源调度的方法。
本发明实施例提供了一种资源调度的方法、 装置和计算机存储介质, 基站根据历史调度资源数预测下一次调度能够使用的资源的最大上限, 在 下一次调度时根据基站当前业务情况以及所述资源的最大上限进行资源分 配; 如此, 能够在下一次调度时参考之前历史调度资源数, 在使用符号功 率检测、 无线帧级调整的方式进行 PA偏置电压调节时, 防止出现平均功率 较小但是存在 OFDM符号功率很高的情况, 解决了 PA偏置电压调节范围 很小甚至不能进行 PA偏置电压调节的问题, 降低了功放功耗, 提高了功放 的效率。 附图说明
图 1为现有技术中 LTE基站***下行链路结构示意图;
图 2为本发明实施例实现资源调度方法的流程示意图;
图 3为本发明实施例实现资源调度装置的结构示意图。 具体实施方式
本发明实施例中, 基站根据历史调度资源数预测下一次调度能够使用
的资源的最大上限, 在下一次调度时根据基站当前业务情况以及所述资源 的最大上限进行资源分配。
下面通过附图及具体实施例对本发明 #丈进一步的详细说明。
本发明实施例实现一种资源调度的方法, 如图 2 所示, 该方法包括以 下几个步骤:
步骤 201:基站根据历史调度资源数预测下一次调度能够使用的资源的 最大上限;
具体的, 基站获取用户输入的时间窗的长度及预测方法, 根据所述预 测方法及时间窗内的历史调度资源数预测下一次调度能够使用的资源的最 大上限;
所述基站获取用户输入的时间窗的长度及预测方法可以是: 基站从网 管的文本框中获取用户输入的时间窗的长度, 并从网管的选项列表中获取 用户输入的预测方法;
所述时间窗包括: 长时间窗和 /或短时间窗;
其中, 所述短时间窗的长度等于 M个的 TTI, Μ为正整数; 长时间窗 的长度等于 Ν倍的短时间窗的长度, Ν为正整数;
所述预测方法包括: 移动平均法、和 /或趋势平均法、和 /或加权平均法、 和 /或平滑指数法;
其中, 可以根据不同地区长期的历史调度资源数变化情况来选择相应 的预测方法。 例如, 当一个地区历史调度资源数之间变化较大时, 则可以 使用加权平均法, 使得在预测时, 距离下一次调度较近的历史调度资源数 占较大的权值, 距离下一次调度较远的历史调度资源数占较小的权值。
所述根据所述预测方法及时间窗内的历史调度资源数预测下一次调度 能够使用的资源的最大上限, 具体可以是:
基站根据预测方法及短时间窗内的历史调度资源数预测短时间窗内的
调度资源数, 根据预测的短时间窗内的调度资源数预测长时间窗内的调度 资源数, 并根据预测的短时间窗内的调度资源数、 预测的长时间窗内的调 度资源数、 全带宽资源数及资源补偿量预测下一次调度能够使用的资源的 最大上限;
其中, 所述历史调度资源数为: 时间窗内最近调度的资源数; 所述根据短时间窗内的调度资源数预测长时间窗内的调度资源数中, 所述短时间窗内的调度资源数为: 最近预测的短时间窗内的调度资源数。
当预测方法为移动平均法时, 所述基站根据预测方法及短时间窗内的 历史调度资源数预测短时间窗内的调度资源数, 具体为:
当调度时间 T小于短时间窗的长度 T1时,预测短时间窗内的调度资源 数为全带宽资源数;
当调度时间 T大于或者等于短时间窗的长度 T1时,则预测短时间窗内 的调度资源数 ?(Γ1)为:
∑SS(t)
MS{T\) = ^
T1
其中: Ti表示短时间窗的长度; (t)表示第 t个历史调度周期实际调 度资源数, 如果从短时间窗的右边往左边进行累加每个 ΤΉ的调度资源数 的话, 当 t=l 时, ^(1)表示上一次调度实际调度的资源数, 如果从短时间 窗的左边往右边进行累加每个 TTI的调度资源数的话, t=Tl, (Γ1)表示上 一次调度实际调度的资源数。
这里,所述资源一般是指 RB资源;所述调度时间表示进行调度的次数, 随着调度的继续不断的累加; 所述全带宽资源数为***带宽所包括的资源 数, 例如, 当***带宽为 20MHz时, 全带宽资源数为 100个 RB。
当预测方法为移动平均法时, 所述根据短时间窗内的调度资源数预测 长时间窗内的调度资源数, 具体为:
当调度时间 T小于短时间窗的长度 T1时,预测长时间窗内的调度资源
数为全带宽资源数;
其中: ?( )表示第 k次预测的短时间窗内的调度资源数,如果按照预 测的先后次序定义第一次预测为 M的话, 当 k=l时, MS )表示最近一次 预测的短时间窗内的调度资源数, 如果按照预测的先后次序定义第一次预 测为 1的话, 当 k=M时, ?(ΓΜ)表示最近一次预测的短时间窗内的调度资 源数; Μ表示调度时间 Τ除以短时间窗的长度 T1的整数部分。
其中: ?( )表示第 k次预测的短时间窗内的调度资源数,如果按照预 测的先后次序定义第一次预测为 T2 + T1的话, 当 k=l时, MS )表示最近 一次预测的短时间窗内的调度资源数, 如果按照预测的先后次序定义第一 次预测为 1的话, 当 k=T2 ÷ Tl时, ^(^2^)表示最近一次预测的短时间窗 内的调度资源数。
当预测方法为加权平均法时, 所述基站根据预测方法及短时间窗内的 历史调度资源数预测短时间窗内的调度资源数, 具体为:
当调度时间 T小于短时间窗的长度 T1时,预测短时间窗内的调度资源 数为全带宽资源数;
其中: τι表示短时间窗的长度, (t)表示第 t个历史调度周期实际调 度资源数, (Ο表示第 t个历史调度周期实际调度资源数所占的权值; 如 果从短时间窗的右边往左边进行累加每个 ΤΉ 的调度资源数的话, 当 t=l 时, ^(1)表示上一次调度实际调度的资源数, 如果从短时间窗的左边往右 边进行累加每个 TTI的调度资源数的话, t=Tl, (Γ1)表示上一次调度实际 调度的资源数。
通过该公式, 能够实现距离下一次调度较近的历史调度资源数占较大 的权值, 距离下一次调度较远的历史调度资源数占较小的权值。
当预测方法为加权平均法时, 所述根据短时间窗内的调度资源数预测 长时间窗内的调度资源数, 具体为:
当调度时间 Τ小于短时间窗的长度 T1时,预测长时间窗内的调度资源 数为全带宽资源数;
当调度时间 Τ大于或等于短时间窗的长度 T1小于长时间窗的长度 Τ2 时, 则预测长时间窗内的调度资源数 MJ(J2)为:
f^ iMSiT^ KLik))
ML{T2) =
∑KL(k)
其中: ?( )表示第 k次预测的短时间窗内的调度资源数, KL(k)表示 第 k次预测的短时间窗内的调度资源数所占的权值, 如果按照预测的先后 次序定义第一次预测为 M的话, 当 k=l时, MS )表示最近一次预测的短 时间窗内的调度资源数, J(1)表示最近一次预测的短时间窗内的调度资源 数所占的权值,如果按照预测的先后次序定义第一次预测为 1的话,当 k=M 时, Μ (ΓΜ)表示最近一次预测的短时间窗内的调度资源数, J(M)表示最
近一次预测的短时间窗内的调度资源数所占的权值; M表示调度时间 T除 以短时间窗的长度 T1的整数部分。
当调度时间 T大于或者等于长时间窗的长度 T2时,则预测长时间窗内 的调度资源数 MJ(J2)为:
∑ (MS(Tk) x KL(k))
ML{T2) = ^ T2÷T1
∑ KL(k)
其中: ?( )表示第 k次预测的短时间窗内的调度资源数, KL(k)表示 第 k次预测的短时间窗内的调度资源数所占的权值, 如果按照预测的先后 次序定义第一次预测为 T2 ÷ T1的话, 当 k=l时, MS )表示最近一次预测 的短时间窗内的调度资源数, J(l)表示最近一次预测的短时间窗内的调度 资源数所占的权值, 如果按照预测的先后次序定义第一次预测为 1 的话, 当 k=T2 ÷ Tl时, ^^2÷n)表示最近一次预测的短时间窗内的调度资源数, ^Ζ(Γ2 ÷ Γ1)表示最近一次预测的短时间窗内的调度资源数所占的权值。
所述根据预测的短时间窗内的调度资源数、 预测的长时间窗内的调度 资源数、 全带宽资源数及资源补偿量预测下一次调度能够使用的资源的最 大上限, 具体为:
当预测的长时间窗内的调度资源数大于或者等于预测的短时间窗内的 调度资源数时, 则预测下一次调度能够使用的资源的最大上限 R5_£为:
RB E = min (ML(T2) +ARB, RBALL)
其中: MJ(J2)表示预测的长时间窗内的调度资源数; 表示全带宽 资源数; 表示资源补偿量。
当预测的长时间窗内的调度资源数小于预测的短时间窗内的调度资源 数时, 则预测下一次调度能够使用的资源的最大上限 R5_£为:
RB_E = mm (MS(Tl) + ARB, RBALL) 其中: ?(Γ1)表示预测的短时间窗内的调度资源数; 表示全带宽
资源数; 表示资源补偿量。
其中, 所述资源补偿量通过获取用户输入确定, 并且资源补偿量可以 根据预测的短时间窗内的调度资源数及相应调度周期实际调度资源数进行 动态调整; 具体可以是:
在预测短时间窗内的调度资源数之前, 根据存储的之前预测的短时间 窗内的调度资源数及相应调度周期内实际调度资源数获得短时间窗内的预 测差值, 并才艮据短时间窗内的预测差值对资源补偿量进行调整;
所述相应调度周期表示短时间窗所处的调度周期, 例如, 上一次预测 的短时间窗与上一次的调度周期相对应;
所述根据存储的之前预测的短时间窗内的调度资源数及相应调度周期 实际调度资源数获得短时间窗内的预测差值 MWL为:
( )— ) I
ARBL
71
其中: T1表示短时间窗的长度; ?( )表示第 k次预测的短时间窗内 的调度资源数, 表示第 k个历史调度周期实际调度资源数, 如果按照 预测的先后次序定义第一次预测为 T1的话, 当 k=l时, MS )表示最近一 次预测的短时间窗内的调度资源数; ^(1)表示上一次调度实际调度的资源 数,如果按照预测的先后次序定义第一次预测为 1的话,当 k=Tl时, MS(TT1) 表示最近一次预测的短时间窗内的调度资源数; (Γ1)表示上一次调度实际 调度的资源数。
所述根据短时间窗内的预测差值对资源补偿量进行调整为: 当短时间 窗内的预测差值大于预定值时, 则将资源补偿量减小预定步长; 当短时间 窗内的预测差值小于预定值时, 则将资源补偿量增加预定步长;
其中, 所述预定值和所述预定步长可以通过用户输入确定;
例如, 预定值为 4, 预定步长为 1, 当短时间窗内的预测差值为 8时,
则将资源补偿量减 1 ;
例如, 预定值为 2, 预定步长为 1, 当短时间窗内的预测差值为 1时, 则将资源补偿量加 1。
步骤 202:在下一次调度时根据基站当前业务情况以及资源的最大上限 进行资源分配;
具体的, 在下一次调度时, 根据基站当前业务情况以及资源的最大上 限进行资源数分配和资源位置分配;
所述当前业务情况为经过优先级排序后的各用户设备 (UE, User Equipment ) 下行等待调度的状态;
所述根据基站当前业务情况以及资源的最大上限进行资源数分配属于 现有技术, 本发明使用步骤 201 中预测的能够使用的资源的最大上限对系 统带宽可用资源数进行替换;
所述资源位置分配可以是: 基站将所分配的虚拟资源通过映射方式映 射到实际的物理资源位置;
其中, 所述映射方式包括: 分布式和 /或集中式。
为了实现上述方法, 本发明实施例还提供一种资源调度的装置, 如图 3 所示, 该装置位于基站调度器包括: 预测模块 301、 资源分配模块 302; 其 中,
所述预测模块 301,配置为根据历史调度资源数预测下一次调度能够使 用的资源的最大上限;
所述资源分配模块 302,配置为在下一次调度时根据基站当前业务情况 以及所述资源的最大上限进行资源分配;
所述预测模块 301,具体配置为获取用户输入的时间窗的长度及预测方 法, 根据所述预测方法及时间窗内的历史调度资源数预测下一次调度能够 使用的资源的最大上限;
所述预测模块 301,具体可以是从文本框中获取用户输入的时间窗的长 度, 并从选项列表中获取用户输入的预测方法;
所述时间窗包括: 长时间窗和 /或短时间窗;
其中, 所述短时间窗的长度等于 M个的 TTI, Μ为正整数; 长时间窗 的长度等于 Ν倍的短时间窗的长度, Ν为正整数;
所述预测方法包括: 移动平均法、和 /或趋势平均法、和 /或加权平均法、 和 /或平滑指数法;
其中, 可以根据不同地区长期的历史调度资源数变化情况来选择相应 的预测方法。 例如, 当一个地区历史调度资源数之间变化较大时, 则可以 使用加权平均法, 使得在预测时, 距离下一次调度较近的历史调度资源数 占较大的权值, 距离下一次调度较远的历史调度资源数占较小的权值; 所述预测模块 301, 具体可以是: 根据预测方法及短时间窗内的历史调 度资源数预测短时间窗内的调度资源数, 根据短时间窗内的调度资源数预 测长时间窗内的调度资源数, 并根据短时间窗内的调度资源数、 长时间窗 内的调度资源数、 全带宽资源数及资源补偿量预测下一次调度能够使用的 资源的最大上限;
其中, 所述历史调度资源数为: 时间窗内最近调度的资源数; 其中, 所述根据短时间窗内的调度资源数预测长时间窗内的调度资源 数中, 所述短时间窗内的调度资源数为: 最近预测的短时间窗内的调度资 源数。
当预测方法为移动平均法时, 所述预测模块 301具体配置为: 当调度时间 Τ小于短时间窗的长度 T1时,预测短时间窗内的调度资源 数为全带宽资源数;
当调度时间 Τ大于或者等于短时间窗的长度 T1时,则预测短时间窗内 的调度资源数 ?(Γ1)为:
∑SS(t)
MS(Tl) 其中: Tl表示短时间窗的长度; (t)表示第 t个历史调度周期实际调 度资源数, 如果从短时间窗的右边往左边进行累加每个 TTI的调度资源数 的话, 当 t=l 时, (ι)表示上一次调度实际调度的资源数, 如果从短时间 窗的左边往右边进行累加每个 TTI的调度资源数的话, t=Tl, (Γ1)表示上 一次调度实际调度的资源数。
这里,所述资源一般是指 RB资源;所述调度时间表示进行调度的次数, 随着调度的继续不断的累加; 所述全带宽资源数为***带宽所包括的资源 数, 例如, 当***带宽为 20MHz时, 全带宽资源数为 100个 RB。
当预测方法为移动平均法时, 所述预测模块 301具体配置为: 当调度时间 T小于短时间窗的长度 T1时,预测长时间窗内的调度资源 数为全带宽资源数;
其中: ?( )表示第 k次预测的短时间窗内的调度资源数,如果按照预 测的先后次序定义第一次预测为 M的话, 当 k=l时, MS )表示最近一次 预测的短时间窗内的调度资源数, 如果按照预测的先后次序定义第一次预 测为 1的话, 当 k=M时, ?(ΓΜ)表示最近一次预测的短时间窗内的调度资 源数; Μ表示调度时间 Τ除以短时间窗的长度 T1的整数部分。
当调度时间 Τ大于或者等于长时间窗的长度 Τ2时,则预测长时间窗内 的调度资源数 MJ(J2)为:
其中: ?( )表示第 k次预测的短时间窗内的调度资源数,如果按照预 测的先后次序定义第一次预测为 T2 + T1的话, 当 k=l时, MS )表示最近 一次预测的短时间窗内的调度资源数, 如果按照预测的先后次序定义第一 次预测为 1的话, 当 k=T2 ÷ Tl时, ^(^2^)表示最近一次预测的短时间窗 内的调度资源数。
当预测方法为加权平均法时, 所述预测模块 301具体配置为: 当调度时间 T小于短时间窗的长度 T1时,预测短时间窗内的调度资源 数为全带宽资源数;
当调度时间 T大于或者等于短时间窗的长度 T1时,则预测短时间窗内的 调度资源数 ?(Γ1)为: ( X )
MS{T\) =
∑KS(t)
ί 1
其中: Ti表示短时间窗的长度, (t)表示第 t个历史调度周期实际调 度资源数, (Ο表示第 t个历史调度周期实际调度资源数所占的权值; 如 果从短时间窗的右边往左边进行累加每个 ΤΉ 的调度资源数的话, 当 t=l 时, ^(1)表示上一次调度实际调度的资源数, 如果从短时间窗的左边往右 边进行累加每个 TTI的调度资源数的话, t=Tl, (Γ1)表示上一次调度实际 调度的资源数。
通过该公式, 能够实现距离下一次调度较近的历史调度资源数占较大 的权值, 距离下一次调度较远的历史调度资源数占较小的权值。
当预测方法为加权平均法时, 所述预测模块 301具体配置为: 当调度时间 Τ小于短时间窗的长度 T1时,预测长时间窗内的调度资源 数为全带宽资源数;
当调度时间 Τ大于或等于短时间窗的长度 T1小于长时间窗的长度 Τ2时,
则预测长时间窗内的调度资源数 MJ(J2)为:
f^iMSiT^ KLik))
ML{T2) =
∑KL(k) 其中: ?( )表示第 k次预测的短时间窗内的调度资源数, KLI^表示 第 k次预测的短时间窗内的调度资源数所占的权值, 如果按照预测的先后 次序定义第一次预测为 M的话, 当 k=l时, MS )表示最近一次预测的短 时间窗内的调度资源数, J(1)表示最近一次预测的短时间窗内的调度资源 数所占的权值,如果按照预测的先后次序定义第一次预测为 1的话,当 k=M 时, Μ (ΓΜ)表示最近一次预测的短时间窗内的调度资源数, J(M)表示最 近一次预测的短时间窗内的调度资源数所占的权值; M表示调度时间 T除 以短时间窗的长度 T1的整数部分。
当调度时间 T大于或者等于长时间窗的长度 T2时,则预测长时间窗 内的调度资源数 MJ(J2)为:
Γ2÷Γ1
∑ (MS(Tk)xKL(k))
ML{T2) = ^ T2÷T1
∑ KL(k) 其中, ?( )表示第 k次预测的短时间窗内的调度资源数, KLI^表示 第 k次预测的短时间窗内的调度资源数所占的权值, 如果按照预测的先后 次序定义第一次预测为 T2÷T1的话, 当 k=l时, MS )表示最近一次预测 的短时间窗内的调度资源数, J(l)表示最近一次预测的短时间窗内的调度 资源数所占的权值, 如果按照预测的先后次序定义第一次预测为 1 的话, 当 k=T2÷Tl时, ^^2÷n)表示最近一次预测的短时间窗内的调度资源数, (Γ2÷Γ1)表示最近一次预测的短时间窗内的调度资源数所占的权值。
所述预测模块 301具体还配置为:
当预测的长时间窗内的调度资源数大于或者等于预测的短时间窗内的 调度资源数时, 则预测下一次调度能够使用的资源的最大上限 R5_£为:
RB_E = mm (ML(T2) + ARB, RBML) 其中: MJ(J2)表示长时间窗内的调度资源数; 表示全带宽资源数; A RB表示资源补偿量。
当预测的长时间窗内的调度资源数小于预测的短时间窗内的调度资源 数时, 则预测下一次调度能够使用的资源的最大上限 R5_£为:
RB_E = mm (MS(Tl) + ARB, RBALL) 其中: ?(Γ1)表示短时间窗内的调度资源数; 表示全带宽资源数; A RB表示资源补偿量。
其中, 所述资源补偿量通过获取用户输入确定;
所述预测模块 301, 具体还配置为根据预测的短时间窗内的调度资源数 及相应调度周期实际调度资源数进行动态调整, 具体可以是: 在预测短时 间窗内的调度资源数之前, 根据存储的之前预测的短时间窗内的调度资源 数及相应调度周期实际调度资源数获得短时间窗内的预测差值, 并根据短 时间窗内的预测差值对资源补偿量进行调整;
所述相应调度周期表示短时间窗所处的调度周期, 例如, 上一次预测 的短时间窗与上一次的调度周期相对应;
所述根据存储的之前预测的短时间窗内的调度资源数及相应调度周期 实际调度资源数获得短时间窗内的预测差值 MWL为: ( )— ) I
ARBL
71
其中: T1表示短时间窗的长度; ?( )表示第 k次预测的短时间窗内 的调度资源数, 表示第 k个历史调度周期实际调度资源数, 如果按照 预测的先后次序定义第一次预测为 T1的话, 当 k=l时, MS )表示最近一
次预测的短时间窗内的调度资源数; ^(1)表示上一次调度实际调度的资源 数,如果按照预测的先后次序定义第一次预测为 1的话,当 k=Tl时, MS(TT1) 表示最近一次预测的短时间窗内的调度资源数; (Γ1)表示上一次调度实际 调度的资源数。
所述根据短时间窗内的预测差值对资源补偿量进行调整为: 当短时间 窗内的预测差值大于预定值时, 则将资源补偿量减小预定步长; 当短时间 窗内的预测差值小于预定值时, 则将资源补偿量增加预定步长;
其中, 所述预定值和所述预定步长可以通过用户输入确定;
例如, 预定值为 4, 预定步长为 1, 当短时间窗内预测差值为 8时, 则 将资源补偿量减 1 ;
例如, 预定值为 2, 预定步长为 1, 当短时间窗内预测差值为 1时, 则将 资源补偿量加 1 ;
所述资源分配模块 302, 具体配置为在下一次调度时,根据基站当前业 务情况以及资源的最大上限进行资源数分配和资源位置分配;
所述当前业务情况为经过优先级排序后的各用户设备下行等待调度的 状态;
所述根据基站当前业务情况以及资源的最大资源上限进行资源分配属 于现有技术, 本发明使用预测模块 301 中预测的能够使用的资源的最大上 限对***带宽可用资源数进行替换。
所述资源分配模块 302, 具体配置为将所分配的虚拟资源根据映射方式 映射到实际的物理资源位置;
其中, 所述映射方式包括: 分布式和 /或集中式。
所述预测模块 301可以由基站调度器的中央处理器 (CPU, Central Processing Unit )和存储器实现,所述资源分配模块 302可以由基站调度器的 CPU和调度接口实现。
本发明实施例所述资源调度的方法如果以软件功能模块的形式实现并 作为独立的产品销售或使用时, 也可以存储在一个计算机可读取存储介质 中。 基于这样的理解, 本领域内的技术人员应明白, 本申请的实施例可提 供为方法、 ***、 或计算机程序产品。 因此, 本申请可釆用完全硬件实施 例、 完全软件实施例、 或结合软件和硬件方面的实施例的形式。 而且, 本 申请可釆用在一个或多个其中包含有计算机可用程序代码的计算机可用存 储介质上实施的计算机程序产品的形式,所述存储介质包括但不限于 U盘、 移动硬盘、只读存储器( ROM, Read-Only Memory )、磁盘存储器、 CD-ROM, 光学存储器等。
本申请是根据本申请实施例的方法、 装置、 和计算机程序产品的流程 图和 /或方框图来描述的。 应理解可由计算机程序指令实现流程图和 /或 方框图中的每一流程和 /或方框、 以及流程图和 /或方框图中的流程和 / 或方框的结合。 可提供这些计算机程序指令到通用计算机、 专用计算机、 嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器, 使得 在流程图一个流程或多个流程和 /或方框图一个方框或多个方框中指定的 功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理 设备以特定方式工作的计算机可读存储器中, 使得存储在该计算机可读存 储器中的指令产生包括指令装置的制造品, 该指令装置实现在流程图一个 流程或多个流程和 /或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备 上, 使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机 实现的处理, 从而在计算机或其他可编程设备上执行的指令提供用于实现 在流程图一个流程或多个流程和 /或方框图一个方框或多个方框中指定的
功能的步骤。
相应的, 本发明实施例还提供一种计算机存储介质, 其中存储有计算 机程序, 该计算机程序用于执行本发明实施例的资源调度的方法。
以上所述, 仅为本发明的较佳实施例而已, 并非用于限定本发明的保 护范围。 工业实用性
通过以上实施例, 基站能够在下一次调度时参考之前历史调度资源数, 在使用符号功率检测、 无线帧级调整的方式进行 PA偏置电压调节时, 防止 出现平均功率较小但是存在 OFDM符号功率很高的情况, 解决了 PA偏置 电压调节范围很小甚至不能进行 PA偏置电压调节的问题, 降低了功放功 耗, 提高了功放的效率。
Claims
1、 一种资源调度的方法, 该方法包括:
基站根据历史调度资源数预测下一次调度能够使用的资源的最大上 限, 在下一次调度时根据基站当前业务情况以及所述资源的最大上限进行 资源分配。
2、 根据权利要求 1所述的方法, 其中, 所述根据历史调度资源数预测 下一次调度能够使用的资源的最大上限为: 基站获取用户输入的时间窗的 长度及预测方法, 根据所述预测方法及时间窗内的历史调度资源数预测下 一次调度能够使用的资源的最大上限。
3、 根据权利要求 2所述的方法, 其中, 所述历史调度资源数为时间窗 内最近调度的资源数; 所述时间窗包括: 短时间窗和 /或长时间窗。
4、 根据权利要求 3所述的方法, 其中, 所述预测方法包括: 移动平均 法、 和 /或趋势平均法、 和 /或加权平均法、 和 /或平滑指数法。
5、 根据权利要求 3或 4所述的方法, 其中, 所述根据所述预测方法及 时间窗内的历史调度资源数预测下一次调度能够使用的资源的最大上限 为:
根据预测方法及短时间窗内的历史调度资源数预测短时间窗内的调度 资源数;
根据预测的短时间窗内的调度资源数预测长时间窗内的调度资源数; 根据预测的短时间窗内的调度资源数、 预测的长时间窗内的调度资源 数、 全带宽资源数及资源补偿量预测下一次调度能够使用的资源的最大上 限。
6、 根据权利要求 5所述的方法, 其中, 所述资源补偿量根据预测的短 时间窗内的调度资源数及相应调度周期实际调度资源数进行动态调整。
7、 一种资源调度的装置, 该装置包括: 预测模块、 资源分配模块; 其
中,
所述预测模块, 配置为根据历史调度资源数预测下一次调度能够使用 的资源的最大上限;
所述资源分配模块, 配置为在下一次调度时根据基站当前业务情况以 及资源的最大上限进行资源分配。
8、 根据权利要求 7所述的装置, 其中, 所述预测模块, 配置为获取用 户输入的时间窗的长度及预测方法, 根据所述预测方法及时间窗内的历史 调度资源数预测下一次调度能够使用的资源的最大上限。
9、 根据权利要求 8所述的装置, 其中, 所述历史调度资源数为时间窗 内最近调度的资源数; 所述时间窗包括: 短时间窗和 /或长时间窗。
10、 根据权利要求 8所述的装置, 其中, 所述预测方法包括: 移动平 均法、 和 /或趋势平均法、 和 /或加权平均法、 和 /或平滑指数法。
11、 根据权利要求 9或 10所述的装置, 其中, 所述预测模块配置为: 根据预测方法及短时间窗内的历史调度资源数预测短时间窗内的调度 资源数;
根据预测的短时间窗内的调度资源数预测长时间窗内的调度资源数; 根据预测的短时间窗内的调度资源数、 预测的长时间窗内的调度资源 数、 全带宽资源数及资源补偿量预测下一次调度能够使用的资源的最大上 限。
12、 根据权利要求 11所述的装置, 其中, 所述预测模块, 还配置为根 据预测的短时间窗内的调度资源数及相应调度周期实际调度资源数对资源 补偿量进行动态调整。
13、 一种计算机存储介质, 其中存储有计算机程序, 该计算机程序用 于执行权利要求 1至 6任一项的资源调度的方法。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP14871815.8A EP3086611B1 (en) | 2013-12-18 | 2014-06-19 | Resource scheduling method and device, and computer storage medium |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310700217.9 | 2013-12-18 | ||
CN201310700217.9A CN104735786B (zh) | 2013-12-18 | 2013-12-18 | 一种资源调度的方法和装置 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2015090022A1 true WO2015090022A1 (zh) | 2015-06-25 |
Family
ID=53402045
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2014/080332 WO2015090022A1 (zh) | 2013-12-18 | 2014-06-19 | 一种资源调度的方法、装置和计算机存储介质 |
Country Status (3)
Country | Link |
---|---|
EP (1) | EP3086611B1 (zh) |
CN (1) | CN104735786B (zh) |
WO (1) | WO2015090022A1 (zh) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106685515A (zh) * | 2017-01-05 | 2017-05-17 | 清华大学 | 空间信息网络中卫星资源的分配方法和装置 |
CN110889636A (zh) * | 2019-12-02 | 2020-03-17 | 中国银行股份有限公司 | 一种银行实物资源的调度方法及装置 |
CN113850428A (zh) * | 2021-09-24 | 2021-12-28 | 联想(北京)有限公司 | 作业调度的预测处理方法、装置和电子设备 |
Families Citing this family (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106358297B (zh) * | 2015-07-14 | 2020-10-16 | ***通信集团公司 | 基于可配置结构帧的业务传输方法及装置、网元设备、终端 |
CN105912436A (zh) * | 2015-09-17 | 2016-08-31 | 乐视网信息技术(北京)股份有限公司 | 基于指数平滑预测的***计算资源预测方法及装置 |
CN107343023B (zh) * | 2017-05-25 | 2020-01-10 | 北京奇艺世纪科技有限公司 | 一种Mesos管理集群中的资源分配方法、装置及电子设备 |
CN107368372B (zh) * | 2017-07-25 | 2021-02-23 | 苏州浪潮智能科技有限公司 | 一种基于云海os平台的资源展示方法与装置 |
WO2019116083A1 (en) | 2017-12-14 | 2019-06-20 | Telefonaktiebolaget Lm Ericsson (Publ) | Dynamic adjustment of workload forecast |
CN108738151B (zh) * | 2018-05-18 | 2020-08-28 | 北京航空航天大学 | 一种基于粗略信息的无线预测资源分配方法 |
CN108900980B (zh) * | 2018-09-21 | 2020-01-10 | 北京邮电大学 | 一种在异构网络中基于移动性预测的资源分配优化方法 |
CN110784929B (zh) * | 2019-09-05 | 2021-06-15 | 腾讯科技(深圳)有限公司 | 一种接入资源分配方法、装置、设备及*** |
CN111405319B (zh) | 2020-03-31 | 2021-07-23 | 北京达佳互联信息技术有限公司 | 带宽确定方法、装置、电子设备和存储介质 |
CN112804757B (zh) * | 2020-12-30 | 2023-05-02 | 联想未来通信科技(重庆)有限公司 | 一种频域资源分配类型的切换方法、装置及可读存储介质 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102075944A (zh) * | 2010-12-07 | 2011-05-25 | 南京邮电大学 | 认知网络中基于多Agent协作的可用资源管理***及方法 |
CN102685907A (zh) * | 2011-03-08 | 2012-09-19 | 中兴通讯股份有限公司 | 一种两跳无线中继通信***中传输资源调度的方法及*** |
CN103178990A (zh) * | 2011-12-20 | 2013-06-26 | ***通信集团青海有限公司 | 一种网络设备性能监控方法及网络管理*** |
CN103327582A (zh) * | 2012-03-23 | 2013-09-25 | 华为技术有限公司 | 功放电压调整方法及基站 |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6792273B1 (en) * | 1998-12-18 | 2004-09-14 | Telefonaktiebolaget Lm Ericsson (Publ) | Method and apparatus for resource reservation in a mobile radio communication system |
ATE386409T1 (de) * | 1998-12-18 | 2008-03-15 | Ericsson Telefon Ab L M | Verfahren und vorrichtung zur reservierung von netzresourcen in einem mobilen funkkommunikationssystem |
CN1277192C (zh) * | 2003-09-30 | 2006-09-27 | 联想(北京)有限公司 | 一种日志分析***及基于该***的分析方法 |
FI20055370A0 (fi) * | 2005-06-30 | 2005-06-30 | Nokia Corp | Resurssien allokointimenetelmä, viestintäjärjestelmä, verkkoelementti, moduuli, tietokoneohjelmatuote ja tietokoneohjelman jakeluväline |
EP2439988B1 (en) * | 2010-10-08 | 2014-06-25 | Alcatel Lucent | Optimizing power consumption of a base station |
-
2013
- 2013-12-18 CN CN201310700217.9A patent/CN104735786B/zh active Active
-
2014
- 2014-06-19 EP EP14871815.8A patent/EP3086611B1/en active Active
- 2014-06-19 WO PCT/CN2014/080332 patent/WO2015090022A1/zh active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102075944A (zh) * | 2010-12-07 | 2011-05-25 | 南京邮电大学 | 认知网络中基于多Agent协作的可用资源管理***及方法 |
CN102685907A (zh) * | 2011-03-08 | 2012-09-19 | 中兴通讯股份有限公司 | 一种两跳无线中继通信***中传输资源调度的方法及*** |
CN103178990A (zh) * | 2011-12-20 | 2013-06-26 | ***通信集团青海有限公司 | 一种网络设备性能监控方法及网络管理*** |
CN103327582A (zh) * | 2012-03-23 | 2013-09-25 | 华为技术有限公司 | 功放电压调整方法及基站 |
Non-Patent Citations (1)
Title |
---|
See also references of EP3086611A4 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106685515A (zh) * | 2017-01-05 | 2017-05-17 | 清华大学 | 空间信息网络中卫星资源的分配方法和装置 |
CN106685515B (zh) * | 2017-01-05 | 2019-10-22 | 清华大学 | 空间信息网络中卫星资源的分配方法和装置 |
CN110889636A (zh) * | 2019-12-02 | 2020-03-17 | 中国银行股份有限公司 | 一种银行实物资源的调度方法及装置 |
CN113850428A (zh) * | 2021-09-24 | 2021-12-28 | 联想(北京)有限公司 | 作业调度的预测处理方法、装置和电子设备 |
Also Published As
Publication number | Publication date |
---|---|
CN104735786A (zh) | 2015-06-24 |
EP3086611A1 (en) | 2016-10-26 |
EP3086611A4 (en) | 2016-12-14 |
EP3086611B1 (en) | 2018-11-28 |
CN104735786B (zh) | 2019-12-03 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2015090022A1 (zh) | 一种资源调度的方法、装置和计算机存储介质 | |
CN111630928B (zh) | 用于随机接入期间数据传输中的msg3的多个tbs | |
JP7020525B2 (ja) | 基地局、User Equipment、及びこれらの方法 | |
KR102449517B1 (ko) | 쇼트 전송 시간 간격 전송의 파워 제어 방법, 장치, 시스템 및 저장 매체 | |
WO2017193996A1 (zh) | 一种上行共享信道的导频传输方法及相关设备 | |
JP5730974B2 (ja) | 送信スケジューリング最適化方法およびワイヤレスユーザ機器デバイス | |
JP6523278B2 (ja) | 端末装置、基地局装置、および通信方法 | |
JP6750114B2 (ja) | 短縮送信時間間隔パターンに対する無線周波数動作の設定制限 | |
WO2018127132A1 (zh) | 一种信号配置方法、装置及存储介质 | |
CN108282433B (zh) | 一种上行信号发送方法、接收方法、终端及基站 | |
WO2012142911A1 (zh) | 一种功率控制的方法和设备 | |
WO2016180077A1 (zh) | 上行功率控制方法、终端、基站及***、计算机存储介质 | |
US10368361B2 (en) | Adaptive communication resource allocation in a wireless network | |
JP2009118016A (ja) | 移動通信システムで使用される基地局及び方法 | |
CN107872892A (zh) | 一种无线资源分配方法及装置 | |
WO2015164315A1 (en) | Methods and devices for calculation of uplink transmission power | |
WO2013067929A1 (zh) | 一种资源分配的方法及基站 | |
US11528685B2 (en) | Uplink control information transmission method and related apparatus | |
US20200015204A1 (en) | Communication Method, Terminal Device and Network Device | |
JP2020533818A (ja) | 上り伝送方法及び端末デバイス | |
WO2021108975A1 (en) | Updating configuration for early data transmission | |
JP2011244105A (ja) | 基地局及び無線リソースの割り当て方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 14871815 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
REEP | Request for entry into the european phase |
Ref document number: 2014871815 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2014871815 Country of ref document: EP |