This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst inte...This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.展开更多
The orthogonal time frequency space(OTFS)modulation has emerged as a promis⁃ing modulation scheme for wireless communications in high-mobility scenarios.An efficient detector is of paramount importance to harvesting t...The orthogonal time frequency space(OTFS)modulation has emerged as a promis⁃ing modulation scheme for wireless communications in high-mobility scenarios.An efficient detector is of paramount importance to harvesting the time and frequency diversities promised by OTFS.Recently,some message passing based detectors have been developed by exploiting the features of the OTFS channel matrices.In this paper,we provide an overview of some re⁃cent message passing based OTFS detectors,compare their performance,and shed some light on potential research on the design of message passing based OTFS receivers.展开更多
In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation...In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation and symbol detection leverage the framework of expectation propagation(EP)and belief propagation(BP)with the aid of Gaussian approximation,respectively.Furthermore,to reduce the computation complexity involved in channel estimation,the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula.Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%,compared with the traditional linear minimum mean square error(LMMSE)algorithm,and can approach to the bit error rate(BER)performance bound of perfectly known channel state information within 0.1 dB.展开更多
The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,th...The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.展开更多
The uplink of mobile satellite communication(MSC) system with hundreds of spot beams is essentially a multiple-input multiple-output(MIMO) channel. Dual-turbo iterative detection and decoding as a kind of MIMO receive...The uplink of mobile satellite communication(MSC) system with hundreds of spot beams is essentially a multiple-input multiple-output(MIMO) channel. Dual-turbo iterative detection and decoding as a kind of MIMO receiver, which exchanges soft extrinsic information between a soft-in soft-out(SISO) detector and an SISO decoder in an iterative fashion, is an efficient method to reduce the uplink inter-beam-interference(IBI),and so the receiving bit error rate(BER).We propose to replace the linear SISO detector of traditional dual-turbo iterative detection and decoding with the AMP detector for the low-density parity-check(LDPC) coded multibeam MSC uplink. This improvement can reduce the computational complexity and achieve much lower BER.展开更多
Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)systems.However,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complex...Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)systems.However,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complexity,resulting in slow convergence or high complexity.To address this issue,a low-complexity Approximate Message Passing(AMP)detection algorithm with Deep Neural Network(DNN)(denoted as AMP-DNN)is investigated in this paper.Firstly,an efficient AMP detection algorithm is derived by scalarizing the simplification of Belief Propagation(BP)algorithm.Secondly,by unfolding the obtained AMP detection algorithm,a DNN is specifically designed for the optimal performance gain.For the proposed AMP-DNN,the number of trainable parameters is only related to that of layers,regardless of modulation scheme,antenna number and matrix calculation,thus facilitating fast and stable training of the network.In addition,the AMP-DNN can detect different channels under the same distribution with only one training.The superior performance of the AMP-DNN is also verified by theoretical analysis and experiments.It is found that the proposed algorithm enables the reduction of BER without signal prior information,especially in the spatially correlated channel,and has a lower computational complexity compared with existing state-of-the-art methods.展开更多
The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexit...The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexity iterative message passing scheme is proposed for joint channel estima- tion, equalization and decoding. Two kinds of schedules （parallel and serial） are adopted in message updates to produce two algorithms with different latency. The computational complexity per iteration of the proposed algorithms grows only linearly with the channel length, which is a significantly de- crease compared to the optimal maximum a posteriori （MAP） detection with the exponential com- plexity. Computer simulations demonstrate the effectiveness of the proposed schemes in terms of bit error rate performance.展开更多
卫星物联网是6G实现万物智联的关键所在,而其频谱资源和星上载荷的双重受限性,给海量用户的接入效能提升带来严峻挑战。针对稀疏码多址接入(SCMA,sparse code multiple access)星载接收机多用户检测效率低下问题,考虑迭代过程中码字发...卫星物联网是6G实现万物智联的关键所在,而其频谱资源和星上载荷的双重受限性,给海量用户的接入效能提升带来严峻挑战。针对稀疏码多址接入(SCMA,sparse code multiple access)星载接收机多用户检测效率低下问题,考虑迭代过程中码字发送概率的差异性,提出一种基于状态位置信息的对数域消息传递算法(SPI-Log-MPA,state position information based log message passing algorithm)。该算法根据用户码字状态位置的变化情况,在迭代检测过程中通过减少不可靠码字、提前对稳定用户进行解码、设立奖惩机制对非稳定用户进行解码等措施,显著提升了检测效率。在此基础上,对阶段设置与状态位置信息矩阵两方面进行优化,提出两阶段的改进算法,进一步加快了收敛速度。复杂度分析与仿真结果表明,所提算法在保证误码率性能的前提下具有更低的计算复杂度。展开更多
基金supported by the National Key Laboratory of Wireless Communications Foundation,China (IFN20230204)。
文摘This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.
基金supported by the National Natural Science Foundation of Chi⁃na(61901417,U1804152,61801434)Science and Technology Re⁃search Project of Henan Province(212102210556,212102210566,212400410179).
文摘The orthogonal time frequency space(OTFS)modulation has emerged as a promis⁃ing modulation scheme for wireless communications in high-mobility scenarios.An efficient detector is of paramount importance to harvesting the time and frequency diversities promised by OTFS.Recently,some message passing based detectors have been developed by exploiting the features of the OTFS channel matrices.In this paper,we provide an overview of some re⁃cent message passing based OTFS detectors,compare their performance,and shed some light on potential research on the design of message passing based OTFS receivers.
文摘In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation and symbol detection leverage the framework of expectation propagation(EP)and belief propagation(BP)with the aid of Gaussian approximation,respectively.Furthermore,to reduce the computation complexity involved in channel estimation,the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula.Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%,compared with the traditional linear minimum mean square error(LMMSE)algorithm,and can approach to the bit error rate(BER)performance bound of perfectly known channel state information within 0.1 dB.
基金supported by National Key Research and Development Program of China under Grants 2021YFB1600500,2021YFB3201502,and 2022YFB3207704Natural Science Foundation of China(NSFC)under Grants U2233216,62071044,61827901,62088101 and 62201056+1 种基金supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62supported by Beijing Nova Program,Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009.
文摘The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.
基金supported by the National Natural Science Foundation of China under Grants 61320106003 and 61401095the Civil Aerospace Technologies Research Project under Grant D010109The Fundamental Research Funds for the Central Universities under Grant YZZ17009
文摘The uplink of mobile satellite communication(MSC) system with hundreds of spot beams is essentially a multiple-input multiple-output(MIMO) channel. Dual-turbo iterative detection and decoding as a kind of MIMO receiver, which exchanges soft extrinsic information between a soft-in soft-out(SISO) detector and an SISO decoder in an iterative fashion, is an efficient method to reduce the uplink inter-beam-interference(IBI),and so the receiving bit error rate(BER).We propose to replace the linear SISO detector of traditional dual-turbo iterative detection and decoding with the AMP detector for the low-density parity-check(LDPC) coded multibeam MSC uplink. This improvement can reduce the computational complexity and achieve much lower BER.
基金supported by Major Project of Science and Technology Research Program of Chongqing Education Commission of China(Grant No.KJZD-M201900601)China Postdoctoral Science Foundation(Grant No.2021MD703932)Project Supported by Engineering Research Center of Mobile Communications,Ministry of Education,China(Grant No.cqupt-mct-202006)。
文摘Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)systems.However,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complexity,resulting in slow convergence or high complexity.To address this issue,a low-complexity Approximate Message Passing(AMP)detection algorithm with Deep Neural Network(DNN)(denoted as AMP-DNN)is investigated in this paper.Firstly,an efficient AMP detection algorithm is derived by scalarizing the simplification of Belief Propagation(BP)algorithm.Secondly,by unfolding the obtained AMP detection algorithm,a DNN is specifically designed for the optimal performance gain.For the proposed AMP-DNN,the number of trainable parameters is only related to that of layers,regardless of modulation scheme,antenna number and matrix calculation,thus facilitating fast and stable training of the network.In addition,the AMP-DNN can detect different channels under the same distribution with only one training.The superior performance of the AMP-DNN is also verified by theoretical analysis and experiments.It is found that the proposed algorithm enables the reduction of BER without signal prior information,especially in the spatially correlated channel,and has a lower computational complexity compared with existing state-of-the-art methods.
基金Supported by the National Natural Science Foundation of China(61201181)Specialized Research Fund for the Doctoral Program of Higher Education(20121101120020)the Co-innovation Laboratory of Aerospace Broadband Network Technology
文摘The problem of soft-input so,output （ SISO ） detection for time-varying frequency-selec- tive fading channels is considered. Based on a suitably-designed factor graph and the sum-product al- gorithm, a low-complexity iterative message passing scheme is proposed for joint channel estima- tion, equalization and decoding. Two kinds of schedules （parallel and serial） are adopted in message updates to produce two algorithms with different latency. The computational complexity per iteration of the proposed algorithms grows only linearly with the channel length, which is a significantly de- crease compared to the optimal maximum a posteriori （MAP） detection with the exponential com- plexity. Computer simulations demonstrate the effectiveness of the proposed schemes in terms of bit error rate performance.
文摘卫星物联网是6G实现万物智联的关键所在,而其频谱资源和星上载荷的双重受限性,给海量用户的接入效能提升带来严峻挑战。针对稀疏码多址接入(SCMA,sparse code multiple access)星载接收机多用户检测效率低下问题,考虑迭代过程中码字发送概率的差异性,提出一种基于状态位置信息的对数域消息传递算法(SPI-Log-MPA,state position information based log message passing algorithm)。该算法根据用户码字状态位置的变化情况,在迭代检测过程中通过减少不可靠码字、提前对稳定用户进行解码、设立奖惩机制对非稳定用户进行解码等措施,显著提升了检测效率。在此基础上,对阶段设置与状态位置信息矩阵两方面进行优化,提出两阶段的改进算法,进一步加快了收敛速度。复杂度分析与仿真结果表明,所提算法在保证误码率性能的前提下具有更低的计算复杂度。
