CN103067320B - Mesh MANET channel adaptive equalization device - Google Patents
Mesh MANET channel adaptive equalization device Download PDFInfo
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- CN103067320B CN103067320B CN201210582470.4A CN201210582470A CN103067320B CN 103067320 B CN103067320 B CN 103067320B CN 201210582470 A CN201210582470 A CN 201210582470A CN 103067320 B CN103067320 B CN 103067320B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
- H04L25/03012—Arrangements for removing intersymbol interference operating in the time domain
- H04L25/03019—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception
- H04L25/03057—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception with a recursive structure
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
- H04L2025/03433—Arrangements for removing intersymbol interference characterised by equaliser structure
- H04L2025/03439—Fixed structures
- H04L2025/03445—Time domain
- H04L2025/03471—Tapped delay lines
- H04L2025/03484—Tapped delay lines time-recursive
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
- H04L2025/03592—Adaptation methods
- H04L2025/03598—Algorithms
- H04L2025/03611—Iterative algorithms
- H04L2025/03636—Algorithms using least mean square [LMS]
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- Computer Networks & Wireless Communication (AREA)
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- Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
Abstract
The invention discloses a kind of Mesh MANET channel adaptive equalization device, it is made up of linear equalizer and DFF, linear equalizer comprises delay unit, tap coefficient unit, adder and sampling decision device, tap coefficient unit is made up of noise generation module and multiplier, the output of each tap coefficient unit connects adder, and the equilibrium of adder exports and is connected with sampling decision device; DFF comprises forward-direction filter and feedback filter, feedback filter comprises tap coefficient unit and delay unit, the equilibrium output of adder is connected with the input of delay unit, the output of delay unit is connected with the input of tap coefficient unit, and the output of each tap coefficient unit all connects adder.The time-varying characteristics of real-time tracking communication channel of the present invention, overcome intersymbol interference, can effectively avoid producing distorted signals in message transmitting procedure or producing error code at receiving terminal, improve the communication quality of Mesh MANET.
Description
Technical field
The present invention relates to a kind of Mesh MANET channel adaptive equalization device.
Background technology
Along with the develop rapidly of science and technology, the continuous propelling of information age, means of communication is also to diversified development.But still there are some drawbacks in the means of communication that in fact, current people generally adopt.Here is deficiency and the shortcoming of several means of communication and their existence generally used at present:
(1) cellular mobile communication networks
1. infrastructure is relied on: need between mobile terminal could realize communication by fixed base stations, base station is connected with key switching network by Wireline, adds communications cost;
2. mobile terminal does not possess routing function, and mobile terminal can only carry out data transmit-receive by fixed base stations, uses constraint larger;
3. star topology, certain link failure, service on a large scale will be interrupted, and Survivabilities of Networks is poor;
4. build, expand, maintenance cost is high;
5. when mobile terminal is static, message transmission rate can reach 2Mbit/s, but during mobile terminal high-speed mobile, message transmission rate only has 144kbit/s.
(2) trunked communication system
1. similar with cellular mobile communication networks, belong to the network having connection, rely on infrastructure;
2. be generally dedicated network, based on speech business.
(3) WLAN (wireless local area network) WLAN
1. mobile node is equipped with wireless network card, is connected, depends on the network infrastructure of similar base station or access point by AP access point with fixed network;
2. concerning network layer, be single-hop networks, can not forwarding data;
3. can realize high-speed communication (802.11b:11M or 802.11a:54M) in limited covering scope (hundreds of rice), but coverage is comparatively limited.
(4) VSAT satellite communication system
1. coverage is the widest, but cost is high, transmission bandwidth is limited, transmission delay is large.
(5) communication in moving
1. rely on satellite, the rainy day or cloud layer thick and heavy in, or just easily to lose efficacy in the place that particular surroundings and existence are blocked, occurred communication failure;
2. antenna is too heavy, uses and carries all very inconvenient, must be placed on the mobile device such as automobile, steamer;
What 3. there is one period of long period seeks star process, and cannot drop into application fast, the use of a lot of occasion is all restricted.
In sum, current existing communication network be mostly based on the reliable and stable communications infrastructure on, once these communications infrastructures are destroyed, conventional means of communication is all no longer feasible.And be exactly often in such, keep communication reliably to seem particularly important.Trunked communication system is except relying on infrastructure, and the network bandwidth also has considerable restraint, uses narrow-band technologies more, about bandwidth 30K, about message transmission rate 16Kbps, this just makes its data transmission capabilities greatly limited.Therefore, under some special occasions, existing means of communication can't meet the demand of communication, and maximum problem is exactly that survival ability is too poor, is easily destroyed fast.
Mesh self-organized network communication system has following characteristic:
1) non-stop layer: in MANET, the status of all nodes is all equality, it is a peer to peer network, node can add at any time and leave network, when arbitrary relay nodes disconnects, communication terminal or the repeater of Automatic-searching minimum distance make up, the fault of any node all can not affect the normal operation of whole network, has very strong survivability.
2) self-organizing: the laying of network or expansion are without the need to relying on any default Base communication facility, just can form an independently network expansion communication work after node start quickly and automatically, communication efficiency is high and the cost built, expand, safeguard and use is low.
3) multihop routing: when node will communicate with the node outside its coverage, the multi-hop that can pass through intermediate node (communication terminal or repeater) forwards and can realize.
4) dynamic topology: wireless self-assembly system allows the topological structure of dynamic change oneself, topology of networks constantly can change along with the change of handheld terminal to adapt to needs of conversing.
5) smart terminal: communication terminal is portable handsets or vehicle-mounted machine, carries with easy to use; In order to conserve energy, each communication terminal can select best working method automatically, and it only keeps in touch to reduce communication energy consumption with nearest node.
6) communication quality: it is strong that MANET has adaptive capacity to environment, and can with outbound communication, obtain abundant data service; Its transfer of data have speed high, be with wide, time delay is little and network coverage is wide etc. feature.
In the wireless communication system of Mesh MANET, reliability is a very important index.Band is limit and in the channel of time diffusion, the intersymbol interference caused due to multi-path influence can make the signal of transmission produce distortion, thus easily produces error code in the receiving end, and equilibrium overcomes a kind of technology of intersymbol interference just.Due to randomness and the time variation of wireless channel, just require the time-varying characteristics of the tracking communication channel that equalizer can be real-time, this equalizer is called adaptive equalizer.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, a kind of time-varying characteristics of real-time tracking communication channel are provided, overcome intersymbol interference, avoid the Mesh MANET channel adaptive equalization device producing distorted signals in message transmitting procedure or produce error code at receiving terminal.
The object of the invention is to be achieved through the following technical solutions: Mesh MANET channel adaptive equalization device, it is made up of linear equalizer and DFF, pass through model selection, automatically switch between linear equalization and decision feedback equalization, and automatically select the algorithm of best results;
Described linear equalizer comprises 2N delay unit, 2N+1 tap coefficient unit, an adder and a sampling decision device, tap coefficient unit is made up of noise generation module and multiplier, input signal is connected with the input of the first delay unit and the first tap coefficient unit respectively, one tunnel of the first delay unit exports and is connected with the input of the second delay unit, another road exports and is connected with the second tap coefficient unit, the output of final stage delay unit is connected with the input of final stage tap coefficient unit, the output of each tap coefficient unit connects adder, the equilibrium of adder exports and is connected with sampling decision device,
Described DFF comprises forward-direction filter and feedback filter, the structure of forward-direction filter is identical with the structure of linear equalizer, feedback filter comprises at least one tap coefficient unit delay unit identical with quantity with it, the equilibrium output of adder is connected with the input of delay unit, the output of delay unit is connected with the input of tap coefficient unit, and the output of each tap coefficient unit all connects adder.
Linear equalizer of the present invention is linear LMS equalizer.
Further, linear LMS equalizer comprises weight setting unit, delay unit, tap coefficient unit, adder, decision unit and mistake in computation feedback unit, input signal respectively with delay unit, weight setting unit is connected, delay unit is all connected with tap coefficient unit with the output of weight setting unit, the output of tap coefficient unit connects adder, one tunnel of adder exports direct output signal output, second tunnel of adder is exported to be inputted with a road of mistake in computation feedback unit by decision unit and training unit successively and is connected, 3rd tunnel output of adder inputs with another road of mistake in computation feedback unit and is connected, the output connection weight setting unit of mistake in computation feedback unit.
DFF of the present invention is decision-feedback LMS equalizer.
Further, decision-feedback LMS equalizer comprises weight setting unit, forward direction delay unit, forward taps coefficient elements, delay of feedback unit, feedback tap coefficient elements, adder, decision unit and mistake in computation feedback unit, input signal respectively with forward direction delay unit, weight setting unit is connected, forward direction delay unit is all connected with forward taps coefficient elements with the output of weight setting unit, the output of forward taps coefficient elements connects adder, one tunnel of adder exports direct output signal output, second tunnel of adder is exported and is connected with training unit by decision unit, one tunnel of training unit exports and connects mistake in computation feedback unit, another road of training unit exports and is connected with the input of delay of feedback unit, delay of feedback unit is connected with feedback tap coefficient elements, the output of feedback tap coefficient elements all connects adder, 3rd tunnel of adder exports and is connected with mistake in computation feedback unit, the output connection weight setting unit of mistake in computation feedback unit.
The invention has the beneficial effects as follows:
(1) time-varying characteristics of real-time tracking communication channel, overcome intersymbol interference, can effectively avoid producing distorted signals in message transmitting procedure or producing error code at receiving terminal, improve the communication quality of Mesh MANET;
(2) based on the communication channel adaptive equalization of LMS algorithm, LMS algorithm is algorithms most in use, and structure is simple, and operand is moderate, and stability does not rely on input data and only relevant to step-length, is better than RLS algorithm to the tracking characteristics of time varying channel.
Accompanying drawing explanation
Fig. 1 is linear equalizer structural representation;
Fig. 2 is Structure of Decision-feedback Equalization schematic diagram;
Fig. 3 is linear LMS equaliser structure schematic diagram;
Fig. 4 is decision-feedback LMS equaliser structure schematic diagram.
Embodiment
Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail, but protection scope of the present invention is not limited to the following stated.
Mesh MANET channel adaptive equalization device, it is made up of linear equalizer and DFF, by model selection, automatically switches between linear equalization and decision feedback equalization, and automatically selects the algorithm of best results.
As shown in Figure 1, linear equalizer comprises 2N delay unit, 2N+1 tap coefficient unit, an adder and a sampling decision device, tap coefficient unit is made up of noise generation module and multiplier, input signal is connected with the input of the first delay unit and the first tap coefficient unit respectively, one tunnel of the first delay unit exports and is connected with the input of the second delay unit, another road exports and is connected with the second tap coefficient unit, the output of final stage delay unit is connected with the input of final stage tap coefficient unit, the output of each tap coefficient unit connects adder, the equilibrium of adder exports and is connected with sampling decision device.Input signal, after delay unit, respectively with each respective taps multiplication (i.e. linear, additive), is then added in adder, finally delivers to sampling decision device.
Linear equalizer can be realized by FIR filter, and the currency of received signal and past value do that linear superposition generates and as exporting by linear equalizer by filter coefficient.
When channel distortion is serious so that linear equalizer not easily processes, when having degree of depth frequency spectrum to decline in channel, linear equalizer can not obtain satisfied effect, just need this time to adopt nonlinear equalizer, nonlinear equalizer comprises DFF, maximum likelihood sign equalizer and maximum-likelihood sequence estimation equalizer.
As shown in Figure 2, DFF comprises forward-direction filter and feedback filter, the structure of forward-direction filter is identical with the structure of linear equalizer, feedback filter comprises at least one tap coefficient unit delay unit identical with quantity with it, the equilibrium output of adder is connected with the input of delay unit, the output of delay unit is connected with the input of tap coefficient unit, and the output of each tap coefficient unit all connects adder.
The basic ideas of DFF are once detect and after judging a signal code, predict and eliminate the intersymbol interference that this symbol brings before just can continuing symbol after sensing.
The filter become when adaptive equalizer is one, its parameter needs constantly adjustment.General adaptive algorithm, by control errors, makes cost function minimize by error signal e, and the weight namely iteratively upgrading equalizer is tending towards minimum to make cost function.In actual applications, coefficient of equalizing wave filter can be determined by various algorithm.These algorithms mainly contain: zero forcing algorithm, LMS algorithm, RLS algorithm etc.Wherein the criterion of LMS algorithm makes the mean square error between the desired output of equalizer and real output value minimum.By carrying out following formula iterative operation to find optimum or close optimum filter weight: new weight=original weight+current input vector of constant * predicated error *; Wherein, predicated error=pre-enter desired value-real output value.In order to better follow the tracks of the real-time change of channel, the training sequence that the transmission of system cycle is known, balancedly estimates channel, constantly adjusts filter coefficient, makes mean square error minimum.
As shown in Figure 3, linear equalizer adopts linear LMS equalizer.Linear LMS equalizer comprises weight setting unit, delay unit, tap coefficient unit, adder, decision unit and mistake in computation feedback unit, input signal respectively with delay unit, weight setting unit is connected, delay unit is all connected with tap coefficient unit with the output of weight setting unit, the output of tap coefficient unit connects adder, one tunnel of adder exports direct output signal output, second tunnel of adder is exported to be inputted with a road of mistake in computation feedback unit by decision unit and training unit successively and is connected, 3rd tunnel output of adder inputs with another road of mistake in computation feedback unit and is connected, the output connection weight setting unit of mistake in computation feedback unit.
As shown in Figure 4, DFF is decision-feedback LMS equalizer.Decision-feedback LMS equalizer comprises weight setting unit, forward direction delay unit, forward taps coefficient elements, delay of feedback unit, feedback tap coefficient elements, adder, decision unit and mistake in computation feedback unit, input signal respectively with forward direction delay unit, weight setting unit is connected, forward direction delay unit is all connected with forward taps coefficient elements with the output of weight setting unit, the output of forward taps coefficient elements connects adder, one tunnel of adder exports direct output signal output, second tunnel of adder is exported and is connected with training unit by decision unit, one tunnel of training unit exports and connects mistake in computation feedback unit, another road of training unit exports and is connected with the input of delay of feedback unit, delay of feedback unit is connected with feedback tap coefficient elements, the output of feedback tap coefficient elements all connects adder, 3rd tunnel of adder exports and is connected with mistake in computation feedback unit, the output connection weight setting unit of mistake in computation feedback unit.
Owing to becoming when channel is, make a start and periodically send training sequence, with the change helping equalizer to follow the tracks of channel.In MESH, the frame head of every frame data can comprise the pseudo random sequence (training sequence) of a regular length.Equalizer has training mode and tracing mode two kinds of operating states.At the work initial stage, the tap coefficient of sef-adapting filter is initialized as null vector.Data send into adaptive equalizer after receiving process by receiver, and the tap coefficient of equalizer automatically adjusts under the control errors of Received signal strength and training sequence.After the iteration of certain number of times, the filter factor of equalizer also no longer significantly can change close to the optimum value that can obtain, and this state of equalizer is called as convergence.Now equalizer is in tracing mode (being also decision pattern), and under this pattern, the tap coefficient of equalizer automatically adjusts under the control errors of Received signal strength and signal constellation (in digital modulation) figure.After equalizer is placed on frame synchronization, equalizer automatically switches in training mode and tracing mode according to frame synchronizing signal and training sequence length.
Zero forcing algorithm is exaggerated noise, is difficult to adapt to deep fading's channel.RLS algorithm performance is better, but algorithm is complicated, and operand is large, is not suitable for integrated, and its stability dependency is in input data.LMS is the algorithm commonly used the most, and structure is simple, and operand is moderate, and stability does not rely on input data and only relevant with step-length, good than RLS algorithm to the tracking characteristics of time varying channel yet, thus in MESH choice for use LMS algorithm.
Claims (5)
1.Mesh MANET channel adaptive equalization device, is characterized in that: it is made up of linear equalizer and DFF, by model selection, automatically switches between linear equalization and decision feedback equalization, and automatically select the algorithm of best results;
Described linear equalizer comprises 2N delay unit, 2N+1 tap coefficient unit, an adder and a sampling decision device, tap coefficient unit is made up of noise generation module and multiplier, input signal is connected with the input of the first delay unit and the first tap coefficient unit respectively, one tunnel of the first delay unit exports and is connected with the input of the second delay unit, another road exports and is connected with the second tap coefficient unit, the output of final stage delay unit is connected with the input of final stage tap coefficient unit, the output of each tap coefficient unit connects adder, the equilibrium of adder exports and is connected with sampling decision device, input signal is after delay unit, and respectively with each respective taps multiplication, i.e. linear, additive, is then added in adder, finally delivers to sampling decision device,
Linear equalizer is realized by FIR filter, and the currency of received signal and past value do that linear superposition generates and as exporting by linear equalizer by filter coefficient; When channel distortion is serious so that linear equalizer not easily processes, when having degree of depth frequency spectrum to decline in channel, adopt nonlinear equalizer, nonlinear equalizer comprises DFF, maximum likelihood sign equalizer and maximum-likelihood sequence estimation equalizer;
Described DFF comprises forward-direction filter and feedback filter, the structure of forward-direction filter is identical with the structure of linear equalizer, feedback filter comprises at least one tap coefficient unit delay unit identical with quantity with it, the equilibrium output of adder is connected with the input of delay unit, the output of delay unit is connected with the input of tap coefficient unit, and the output of each tap coefficient unit all connects adder; The basic ideas of DFF are once detect and after judging a signal code, just predict before continuous symbol after sensing and eliminate the intersymbol interference that this symbol brings;
The filter become when adaptive equalizer is one, its parameter needs constantly adjustment, and adaptive algorithm, by control errors, makes cost function minimize by error signal e, and the weight namely iteratively upgrading equalizer is tending towards minimum to make cost function; In actual applications, coefficient of equalizing wave filter is determined by following algorithm: zero forcing algorithm, LMS algorithm, RLS algorithm, and wherein the criterion of LMS algorithm makes the mean square error between the desired output of equalizer and real output value minimum; By carrying out following formula iterative operation to find optimum or close optimum filter weight: new weight=original weight+current input vector of constant * predicated error *; Wherein, predicated error=pre-enter desired value-real output value; In order to better follow the tracks of the real-time change of channel, system cycle ground sends known training sequence, and equilibrium is estimated channel, constantly adjusts filter coefficient, makes mean square error minimum;
Owing to becoming when channel is, make a start and periodically send training sequence, with the change helping equalizer to follow the tracks of channel, in MESH, the frame head of every frame data comprises pseudo random sequence and the training sequence of a regular length, equalizer has training mode and tracing mode two kinds of operating states, at the work initial stage, the tap coefficient of sef-adapting filter is initialized as null vector, data send into adaptive equalizer after receiving process by receiver, the tap coefficient of equalizer automatically adjusts under the control errors of Received signal strength and training sequence, after the iteration of certain number of times, the filter factor of equalizer also no longer significantly can change close to the optimum value that can obtain, this state of equalizer is called as convergence, now equalizer is in tracing mode, under this pattern, the tap coefficient of equalizer automatically adjusts under the control errors of Received signal strength and signal constellation (in digital modulation) figure, after equalizer is placed on frame synchronization, equalizer automatically switches in training mode and tracing mode according to frame synchronizing signal and training sequence length.
2. Mesh MANET channel adaptive equalization device according to claim 1, is characterized in that: described linear equalizer is linear LMS equalizer.
3. Mesh MANET channel adaptive equalization device according to claim 2, it is characterized in that: described linear LMS equalizer comprises weight setting unit, delay unit, tap coefficient unit, adder, decision unit and mistake in computation feedback unit, input signal respectively with delay unit, weight setting unit is connected, delay unit is all connected with tap coefficient unit with the output of weight setting unit, the output of tap coefficient unit connects adder, one tunnel of adder exports direct output signal output, second tunnel of adder is exported to be inputted with a road of mistake in computation feedback unit by decision unit and training unit successively and is connected, 3rd tunnel output of adder inputs with another road of mistake in computation feedback unit and is connected, the output connection weight setting unit of mistake in computation feedback unit.
4. Mesh MANET channel adaptive equalization device according to claim 1, is characterized in that: described DFF is decision-feedback LMS equalizer.
5. Mesh MANET channel adaptive equalization device according to claim 4, it is characterized in that: described decision-feedback LMS equalizer comprises weight setting unit, forward direction delay unit, forward taps coefficient elements, delay of feedback unit, feedback tap coefficient elements, adder, decision unit and mistake in computation feedback unit, input signal respectively with forward direction delay unit, weight setting unit is connected, forward direction delay unit is all connected with forward taps coefficient elements with the output of weight setting unit, the output of forward taps coefficient elements connects adder, one tunnel of adder exports direct output signal output, second tunnel of adder is exported and is connected with training unit by decision unit, one tunnel of training unit exports and connects mistake in computation feedback unit, another road of training unit exports and is connected with the input of delay of feedback unit, delay of feedback unit is connected with feedback tap coefficient elements, the output of feedback tap coefficient elements all connects adder, 3rd tunnel of adder exports and is connected with mistake in computation feedback unit, the output connection weight setting unit of mistake in computation feedback unit.
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| PCT/CN2013/071597 WO2014101341A1 (en) | 2012-12-28 | 2013-02-09 | Mesh ad-hoc network channel adaptive equalizer |
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| CN105450566B (en) * | 2014-09-30 | 2019-04-30 | 展讯通信（上海）有限公司 | Equalization methods and balanced device |
| WO2016090562A1 (en) * | 2014-12-09 | 2016-06-16 | 华为技术有限公司 | Method for setting equalization device and equalization device |
| CN105827557A (en) * | 2016-05-24 | 2016-08-03 | 桂林市思奇通信设备有限公司 | Time-domain equalizer based on MIMO (Multiple-Input Multiple-Output) |
| CN108616276A (en) * | 2016-12-12 | 2018-10-02 | 中国航空工业集团公司西安航空计算技术研究所 | Simulation decision feedback equalization circuit for high speed SerDes |
| US10313165B2 (en) * | 2017-03-08 | 2019-06-04 | Credo Technology Group Limited | Finite impulse response analog receive filter with amplifier-based delay chain |
| CN107733821A (en) * | 2017-09-26 | 2018-02-23 | 北京集创北方科技股份有限公司 | Channel compensating method and device |
| CN113992485B (en) * | 2021-10-27 | 2023-05-30 | 西安微电子技术研究所 | Decision feedback equalization circuit and high-speed signal channel transmission system |
| CN114710212B (en) * | 2022-05-06 | 2023-10-31 | 成都天奥测控技术有限公司 | IQ correction method, module, device and system based on decision feedback |
| CN115987727B (en) * | 2023-03-21 | 2023-09-26 | 荣耀终端有限公司 | Signal transmission method and device |
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| CN1659840A (en) * | 2002-06-04 | 2005-08-24 | 高通股份有限公司 | Receiver with a decision feedback equalizer and a linear equalizer |
| CN101098416A (en) * | 2006-06-29 | 2008-01-02 | 上海高清数字科技产业有限公司 | Time domain self-adaptive equalizer and decision feedback filter included therein |
| CN102790734A (en) * | 2011-05-18 | 2012-11-21 | 中国科学院声学研究所 | Linear adaptive equalizer based on channel estimation |
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| CN1659840A (en) * | 2002-06-04 | 2005-08-24 | 高通股份有限公司 | Receiver with a decision feedback equalizer and a linear equalizer |
| CN101098416A (en) * | 2006-06-29 | 2008-01-02 | 上海高清数字科技产业有限公司 | Time domain self-adaptive equalizer and decision feedback filter included therein |
| CN102790734A (en) * | 2011-05-18 | 2012-11-21 | 中国科学院声学研究所 | Linear adaptive equalizer based on channel estimation |
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