In this paper, we investigate the performance of adaptive modulation （AM） orthogonal frequency division multiplexing （OFDM） system in underwater acoustic （UWA） communications. The aim is to solve the problem of ...In this paper, we investigate the performance of adaptive modulation （AM） orthogonal frequency division multiplexing （OFDM） system in underwater acoustic （UWA） communications. The aim is to solve the problem of large feedback overhead for channel state information （CSI） in every subcarrier. A novel CSI feedback scheme is proposed based on the theory of compressed sensing （CS）. We propose a feedback from the receiver that only feedback the sparse channel parameters. Additionally, prediction of the channel state is proposed every several symbols to realize the AM in practice. We describe a linear channel prediction algorithm which is used in adaptive transmission. This system has been tested in the real underwater acoustic channel. The linear channel prediction makes the AM transmission techniques more feasible for acoustic channel communications. The simulation and experiment show that significant improvements can be obtained both in bit error rate （BER） and throughput in the AM scheme compared with the fixed Quadrature Phase Shift Keying （QPSK） modulation scheme. Moreover, the performance with standard CS outperforms the Discrete Cosine Transform （DCT） method.展开更多
In this paper,we give a systematic description of the 1st Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AI Work Group.Firstly,the framework of ful...In this paper,we give a systematic description of the 1st Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AI Work Group.Firstly,the framework of full channel state information(F-CSI)feedback problem and its corresponding channel dataset are provided.Then the enhancing schemes for DL-based F-CSI feedback including i)channel data analysis and preprocessing,ii)neural network design and iii)quantization enhancement are elaborated.The final competition results composed of different enhancing schemes are presented.Based on the valuable experience of 1stWAIC,we also list some challenges and potential study areas for the design of AI-based wireless communication systems.展开更多
This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AIWork Group,where the framework of the eigenvector...This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AIWork Group,where the framework of the eigenvector-based channel state information(CSI)feedback problem is firstly provided.Then a basic Transformer backbone for CSI feedback referred to EVCsiNet-T is proposed.Moreover,a series of potential enhancements for deep learning based(DL-based)CSI feedback including i)data augmentation,ii)loss function design,iii)training strategy,and iv)model ensemble are introduced.The experimental results involving the comparison between EVCsiNet-T and traditional codebook methods over different channels are further provided,which show the advanced performance and a promising prospect of Transformer on DL-based CSI feedback problem.展开更多
In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of...In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.展开更多
In Unmanned Aerial Vehicle(UAV)-assisted millimeter Wave(mmWave)systems,Channel State Information(CSI)feedback is critical for the selection of modulation schemes,resource management,beamforming,etc.However,traditiona...In Unmanned Aerial Vehicle(UAV)-assisted millimeter Wave(mmWave)systems,Channel State Information(CSI)feedback is critical for the selection of modulation schemes,resource management,beamforming,etc.However,traditional CSI feedback methods lead to significant feedback overhead and energy consumption of the UAV transmitter,therefore shortening the system operation time.To tackle these issues,inspired by superimposed feedback and Integrated Sensing and Communications(ISAC),a Line of Sight(LoS)sensing-based superimposed CSI feedback scheme is proposed.Specifically,on the UAV transmitter side,the Ground-to-UAV(G2U)CSI is superimposed on the UAV-to-Ground(U2G)data to feed back to the ground Base Station(gBS).At the gBS,the dedicated LoS Sensing Network(LoS-SenNet)is designed to sense the U2G CSI in LoS and NLoS scenarios.With the sensed result of LoS-SenNet,the determined G2U CSI from the initial feature extraction will work as the priori information to guide the subsequent operation.Specifically,for the G2U CSI in NLoS,a CSI Recovery Network(CSI-RecNet)and superimposed interference cancellation are developed to recover the G2U CSI and U2G data.As for the LoS scenario,a dedicated LoS Aid Network(LoS-Aid Net)is embedded before the CSI-RecNet and the block of superimposed interference cancellation to highlight the feature of the G2U CSI.Compared with other methods of superimposed CSI feedback,simulation results demonstrate that the proposed feedback scheme effectively improves the recovery accuracy of the G2U CSI and U2G data.Besides,against parameter variations,the proposed feedback scheme presents its robustness.展开更多
Massive MIMO is one of tile enabling technologies tbr beyond 4G and 5G systems due to its ability to provide beamforming gain and reduce interference Dual-polarized antenna is widely adopted to accommodate a large num...Massive MIMO is one of tile enabling technologies tbr beyond 4G and 5G systems due to its ability to provide beamforming gain and reduce interference Dual-polarized antenna is widely adopted to accommodate a large number of antenna elements in limited space. However, current CSI（channel state information） feedback schemes developed in LTE for conventional MIMO systems are not efficient enough for massive MIMO systems since the overhead increases almost linearly with the number of antenna. Moreover, the codebook for massive MIMO will be huge and difficult to design with the LTE methodology. This paper proposes a novel CSI feedback scheme named layered Multi-paths Information based CSI Feedback （LMPIF）, which can achieve higher spectrum efficiency for dual-polarized antenna system with low feedback overhead. The MIMO channel is decomposed into long term components （multipath directions and amplitudes） and short term components （multipath phases）. The relationship between the two components and the optimal precoder is derived in closed form. To reduce the overhead, different granularities in feedback time have been applied for the long term components and short term components Link and system level simulation results prove that LMPIF can improve performance considerably with low CSI feedback overhead.展开更多
A novel downlink channel state information(CSI)feedback scheme is proposed for the closed-loopbeamforming system.In the proposed scheme,mobile terminal(MT)superposes the uplink pilot on thereceived downlink pilot,form...A novel downlink channel state information(CSI)feedback scheme is proposed for the closed-loopbeamforming system.In the proposed scheme,mobile terminal(MT)superposes the uplink pilot on thereceived downlink pilot,forms the hybrid pilot(HP),and then transmits the HP to base station(BS)viathe uplink pilot channel.Because downlink CSI can be recovered from HP at BS side without consumingextra uplink bandwidth,the proposed scheme can achieve zero-payload CSI feedback,effectively solvingthe traditional bottleneck problems,i.e.,the heavy burden for transmitting CSI.Moreover,both MT'scomplexity and feedback delays can be reduced since the downlink channel needs not to be estimated atMT any more.Simulations verify that the proposed scheme can achieve the better MSE performance forthe uplink channel estimation than the traditional scheme,and the cost for the zero-payload CSI feedbackis some acceptable loss of feedback precision.展开更多
In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and b...In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .展开更多
With the rapid development of the Internet of vehicles(IoV),vehicle to everything(V2X)has strict requirements for ultra-reliable and low latency communications(URLLC),and massive multiinput multi-output(MIMO)channel s...With the rapid development of the Internet of vehicles(IoV),vehicle to everything(V2X)has strict requirements for ultra-reliable and low latency communications(URLLC),and massive multiinput multi-output(MIMO)channel state information(CSI)feedback can effectively support URLLC communication in 5G vehicle to infrastructure(V2I)scenarios.Existing research applies deep learning(DL)to CSI feedback,but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect.However,the actual channel still has the influence of additive noise and nonlinear effects,especially in the high-speed V2I scene,the channel characteristics are more complex and time-varying.In response to the above problems,this paper proposes a CSI intelligent feedback network model for V2I scenarios,named residual mixnet(RM-Net).The network learns the channel characteristics in the V2I scenario at the vehicle user(User Equipment,UE),compresses the CSI and sends it to the channel;the roadside base station(Base Station,BS)receives the data and learns the compressed data characteristics,and then restore the original CSI.The system simulation results show that the RM-Net training speed is fast,requires fewer training samples,and its performance is significantly better than the existing DL-based CSI feedback algorithm.It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise.At the same time,the network still has good performance under high compression ratio and low signal-to-noise ratio(SNR).展开更多
Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditiona...Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.展开更多
针对车联网场景下多入多出-正交时频空(Multiple-Input Multiple-Output-Orthogonal Time Frequency Space,MIMO-OTFS)系统的信道状态信息(Channel State Information,CSI)反馈问题,提出了一种面向时延-多普勒(Delay-Dopler,DD)域CSI反...针对车联网场景下多入多出-正交时频空(Multiple-Input Multiple-Output-Orthogonal Time Frequency Space,MIMO-OTFS)系统的信道状态信息(Channel State Information,CSI)反馈问题,提出了一种面向时延-多普勒(Delay-Dopler,DD)域CSI反馈的时间差分架构Transformer网络(Time-differencing Architecture Delay-Doppler Transformer Network,TA-DD-TransNet),引入分时反馈机制,将残差信息建模与压缩反馈相结合。网络结构融合Transformer的全局建模能力与卷积神经网络的局部特征提取优势,在保持CSI重构精度的同时显著降低了反馈比特数与计算复杂度。在不同车速、信噪比及非完美信道估计条件下的仿真实验结果表明,所提方法在归一化均方误差(Normalized Mean Squared Error,NMSE)和余弦相似度指标上均优于CsiNet、CsiNet+和BCsiNet。在60 km/h、30 dB信噪比、1/4压缩率下,TA-DD-TransNet的NMSE约-27 dB,余弦相似度达0.96。复杂度分析显示,TA-DD-TransNet在1/4压缩率下的编码器和解码器浮点运算次数分别为1.809×10^(7)和2.281×10^(7),参数量均为8.4×10~6左右,显著低于CsiNet+。所提方法能满足车联网中对高可靠低时延通信的实际需求。展开更多
通过压缩信道状态信息(Channel Status Information,CSI)传输码字降低大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的CSI反馈开销,可以有效减少计算资源的使用和信息传输时间的消耗。针对如何使用轻量化模型准确估计...通过压缩信道状态信息(Channel Status Information,CSI)传输码字降低大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的CSI反馈开销,可以有效减少计算资源的使用和信息传输时间的消耗。针对如何使用轻量化模型准确估计低压缩比条件下CSI反馈的问题,通过设计的轻量化迭代交叉网络(Iterative Cross Network,ICNet)模型,在用户端使用设计的迭代压缩模块压缩CSI反馈,基站端使用设计的迭代重建模块估计CSI反馈,以较高的准确率和较低的时间消耗估计了低压缩比条件下的CSI反馈。在COST2100模型生成的数据样本下评估了ICNet在低压缩比条件下的鲁棒性,实验表明,在较小的1/64压缩比条件下,ICNet的归一化均方误差比次优值降低了8.48%,ICNet的参数量降低了35%左右。展开更多
为应对大规模多输入多输出(Multiple⁃input multiple⁃output,MIMO)系统中信道状态信息(Channel state information,CSI)反馈开销的日益增长,基于深度学习的CSI反馈网络(如Transformer网络)受到了广泛的关注,是一种非常有应用前景的智能...为应对大规模多输入多输出(Multiple⁃input multiple⁃output,MIMO)系统中信道状态信息(Channel state information,CSI)反馈开销的日益增长,基于深度学习的CSI反馈网络(如Transformer网络)受到了广泛的关注,是一种非常有应用前景的智能传输技术。为此,本文提出了一种基于数据聚类的CSI反馈Transformer网络的简化方法,采用基于聚类的近似矩阵乘法(Approximate matrix multiplication,AMM)技术,以降低反馈过程中Transformer网络的计算复杂度。本文主要对Transformer网络的全连接层计算(等效为矩阵乘法),应用乘积量化(Product quantization,PQ)和MADDNESS等简化方法,分析了它们对计算复杂度和系统性能的影响,并针对神经网络数据的特点进行了算法优化。仿真结果表明,在适当的参数调整下,基于MADDNESS方法的CSI反馈网络性能接近精确矩阵乘法方法,同时可大幅降低计算复杂度。展开更多
In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of ex...In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information(CSI)feedback methods.Specifically,a bidirectional position attention module(BPAM)was designed in the BPANet to improve the network performance.The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information,thereby enhancing the feature representation of the CSI matrix.Furthermore,channel attention is decomposed into two one-dimensional(1D)feature encoding processes effectively reducing computational costs.Simulation results demonstrate that,compared with the existing representative method complex input lightweight neural network(CLNet),BPANet reduces computational complexity by an average of 19.4%and improves accuracy by an average of 7.1%.Additionally,it performs better in terms of running time delay and cosine similarity.展开更多
基金financially supported by the Research Fund for the Visiting Scholar Program by the China Scholarship Council(Grant No.2011631504)the Fundamental Research Funds for the Central Universities(Grant No.201112G020)+1 种基金the National Natural Science Foundation of China(Grant No.41176032)China Scholarship Council
文摘In this paper, we investigate the performance of adaptive modulation （AM） orthogonal frequency division multiplexing （OFDM） system in underwater acoustic （UWA） communications. The aim is to solve the problem of large feedback overhead for channel state information （CSI） in every subcarrier. A novel CSI feedback scheme is proposed based on the theory of compressed sensing （CS）. We propose a feedback from the receiver that only feedback the sparse channel parameters. Additionally, prediction of the channel state is proposed every several symbols to realize the AM in practice. We describe a linear channel prediction algorithm which is used in adaptive transmission. This system has been tested in the real underwater acoustic channel. The linear channel prediction makes the AM transmission techniques more feasible for acoustic channel communications. The simulation and experiment show that significant improvements can be obtained both in bit error rate （BER） and throughput in the AM scheme compared with the fixed Quadrature Phase Shift Keying （QPSK） modulation scheme. Moreover, the performance with standard CS outperforms the Discrete Cosine Transform （DCT） method.
文摘In this paper,we give a systematic description of the 1st Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AI Work Group.Firstly,the framework of full channel state information(F-CSI)feedback problem and its corresponding channel dataset are provided.Then the enhancing schemes for DL-based F-CSI feedback including i)channel data analysis and preprocessing,ii)neural network design and iii)quantization enhancement are elaborated.The final competition results composed of different enhancing schemes are presented.Based on the valuable experience of 1stWAIC,we also list some challenges and potential study areas for the design of AI-based wireless communication systems.
文摘This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AIWork Group,where the framework of the eigenvector-based channel state information(CSI)feedback problem is firstly provided.Then a basic Transformer backbone for CSI feedback referred to EVCsiNet-T is proposed.Moreover,a series of potential enhancements for deep learning based(DL-based)CSI feedback including i)data augmentation,ii)loss function design,iii)training strategy,and iv)model ensemble are introduced.The experimental results involving the comparison between EVCsiNet-T and traditional codebook methods over different channels are further provided,which show the advanced performance and a promising prospect of Transformer on DL-based CSI feedback problem.
基金This work was supported by the Key Research and Development Project of Shaanxi Province under Grant no.2019ZDLGY07-07.
文摘In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.
基金the support of the Sichuan Science and Technology Program,China(Nos.2021JDRC0003,2023YFG0316,and 2021YFG0064)the Demonstration Project of Chengdu Major Science and Technology Application,China(No.2020-YF09-00048-SN)+1 种基金the Special Funds of Industry Development of Sichuan Province,China(No.zyf-2018-056)the Industry-University Research Innovation Fund of China University(No.2021ITA10016/cxy0743)。
文摘In Unmanned Aerial Vehicle(UAV)-assisted millimeter Wave(mmWave)systems,Channel State Information(CSI)feedback is critical for the selection of modulation schemes,resource management,beamforming,etc.However,traditional CSI feedback methods lead to significant feedback overhead and energy consumption of the UAV transmitter,therefore shortening the system operation time.To tackle these issues,inspired by superimposed feedback and Integrated Sensing and Communications(ISAC),a Line of Sight(LoS)sensing-based superimposed CSI feedback scheme is proposed.Specifically,on the UAV transmitter side,the Ground-to-UAV(G2U)CSI is superimposed on the UAV-to-Ground(U2G)data to feed back to the ground Base Station(gBS).At the gBS,the dedicated LoS Sensing Network(LoS-SenNet)is designed to sense the U2G CSI in LoS and NLoS scenarios.With the sensed result of LoS-SenNet,the determined G2U CSI from the initial feature extraction will work as the priori information to guide the subsequent operation.Specifically,for the G2U CSI in NLoS,a CSI Recovery Network(CSI-RecNet)and superimposed interference cancellation are developed to recover the G2U CSI and U2G data.As for the LoS scenario,a dedicated LoS Aid Network(LoS-Aid Net)is embedded before the CSI-RecNet and the block of superimposed interference cancellation to highlight the feature of the G2U CSI.Compared with other methods of superimposed CSI feedback,simulation results demonstrate that the proposed feedback scheme effectively improves the recovery accuracy of the G2U CSI and U2G data.Besides,against parameter variations,the proposed feedback scheme presents its robustness.
基金supported by the National High-Tech R&D Program(863 Program 2015AA01A705)
文摘Massive MIMO is one of tile enabling technologies tbr beyond 4G and 5G systems due to its ability to provide beamforming gain and reduce interference Dual-polarized antenna is widely adopted to accommodate a large number of antenna elements in limited space. However, current CSI（channel state information） feedback schemes developed in LTE for conventional MIMO systems are not efficient enough for massive MIMO systems since the overhead increases almost linearly with the number of antenna. Moreover, the codebook for massive MIMO will be huge and difficult to design with the LTE methodology. This paper proposes a novel CSI feedback scheme named layered Multi-paths Information based CSI Feedback （LMPIF）, which can achieve higher spectrum efficiency for dual-polarized antenna system with low feedback overhead. The MIMO channel is decomposed into long term components （multipath directions and amplitudes） and short term components （multipath phases）. The relationship between the two components and the optimal precoder is derived in closed form. To reduce the overhead, different granularities in feedback time have been applied for the long term components and short term components Link and system level simulation results prove that LMPIF can improve performance considerably with low CSI feedback overhead.
基金Supported by the National Natural Science Foundation of China ( No. 60872048)the National Major Program of Science and Technology ( No.2008ZX03003-004 2009ZX03003-009)
文摘A novel downlink channel state information(CSI)feedback scheme is proposed for the closed-loopbeamforming system.In the proposed scheme,mobile terminal(MT)superposes the uplink pilot on thereceived downlink pilot,forms the hybrid pilot(HP),and then transmits the HP to base station(BS)viathe uplink pilot channel.Because downlink CSI can be recovered from HP at BS side without consumingextra uplink bandwidth,the proposed scheme can achieve zero-payload CSI feedback,effectively solvingthe traditional bottleneck problems,i.e.,the heavy burden for transmitting CSI.Moreover,both MT'scomplexity and feedback delays can be reduced since the downlink channel needs not to be estimated atMT any more.Simulations verify that the proposed scheme can achieve the better MSE performance forthe uplink channel estimation than the traditional scheme,and the cost for the zero-payload CSI feedbackis some acceptable loss of feedback precision.
文摘In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .
基金This work was supported by the National Natural Science Foundation of China(No.61501066)Natural Science Foundation of Chongqing(No.cstc2019jcyj-msxmX0017).
文摘With the rapid development of the Internet of vehicles(IoV),vehicle to everything(V2X)has strict requirements for ultra-reliable and low latency communications(URLLC),and massive multiinput multi-output(MIMO)channel state information(CSI)feedback can effectively support URLLC communication in 5G vehicle to infrastructure(V2I)scenarios.Existing research applies deep learning(DL)to CSI feedback,but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect.However,the actual channel still has the influence of additive noise and nonlinear effects,especially in the high-speed V2I scene,the channel characteristics are more complex and time-varying.In response to the above problems,this paper proposes a CSI intelligent feedback network model for V2I scenarios,named residual mixnet(RM-Net).The network learns the channel characteristics in the V2I scenario at the vehicle user(User Equipment,UE),compresses the CSI and sends it to the channel;the roadside base station(Base Station,BS)receives the data and learns the compressed data characteristics,and then restore the original CSI.The system simulation results show that the RM-Net training speed is fast,requires fewer training samples,and its performance is significantly better than the existing DL-based CSI feedback algorithm.It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise.At the same time,the network still has good performance under high compression ratio and low signal-to-noise ratio(SNR).
基金supported in part by the Natural Science Foundation of China under Grant Nos.U2468201 and 62221001ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240420002。
文摘Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.
文摘针对车联网场景下多入多出-正交时频空(Multiple-Input Multiple-Output-Orthogonal Time Frequency Space,MIMO-OTFS)系统的信道状态信息(Channel State Information,CSI)反馈问题,提出了一种面向时延-多普勒(Delay-Dopler,DD)域CSI反馈的时间差分架构Transformer网络(Time-differencing Architecture Delay-Doppler Transformer Network,TA-DD-TransNet),引入分时反馈机制,将残差信息建模与压缩反馈相结合。网络结构融合Transformer的全局建模能力与卷积神经网络的局部特征提取优势,在保持CSI重构精度的同时显著降低了反馈比特数与计算复杂度。在不同车速、信噪比及非完美信道估计条件下的仿真实验结果表明,所提方法在归一化均方误差(Normalized Mean Squared Error,NMSE)和余弦相似度指标上均优于CsiNet、CsiNet+和BCsiNet。在60 km/h、30 dB信噪比、1/4压缩率下,TA-DD-TransNet的NMSE约-27 dB,余弦相似度达0.96。复杂度分析显示,TA-DD-TransNet在1/4压缩率下的编码器和解码器浮点运算次数分别为1.809×10^(7)和2.281×10^(7),参数量均为8.4×10~6左右,显著低于CsiNet+。所提方法能满足车联网中对高可靠低时延通信的实际需求。
基金supported by the National Natural Science Foundation of China(12005108)the Shandong Provincial Natural Science Foundation Youth Project(ZR2020QF016)the National Natural Science Foundation of China(U2006222)。
文摘In frequency division duplex(FDD)massive multiple-input multiple-output(MIMO)systems,a bidirectional positional attention network(BPANet)was proposed to address the high computational complexity and low accuracy of existing deep learning-based channel state information(CSI)feedback methods.Specifically,a bidirectional position attention module(BPAM)was designed in the BPANet to improve the network performance.The BPAM captures the distribution characteristics of the CSI matrix by integrating channel and spatial dimension information,thereby enhancing the feature representation of the CSI matrix.Furthermore,channel attention is decomposed into two one-dimensional(1D)feature encoding processes effectively reducing computational costs.Simulation results demonstrate that,compared with the existing representative method complex input lightweight neural network(CLNet),BPANet reduces computational complexity by an average of 19.4%and improves accuracy by an average of 7.1%.Additionally,it performs better in terms of running time delay and cosine similarity.
