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China Communications 2023年第1期20卷 66-78页

作者： Haoyang Li Bin Li Tingting Zhang Yuan Feng Nan Wu School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China China Research and Development Academy of Machinery Equipment Beijing 100089China

Orthogonal Time Frequency Space(OTFS)signaling with index modulation(IM)is a promising transmission scheme characterized by high transmission efficiency for high mobility *** this paper,we study the receiver for coded OTFS-IM ***,we construct the corresponding factor graph,on which the structured prior incorporating activation pattern constraint and channel coding is *** we develop a iterative receiver via structured prior-based hybrid belief propagation(BP)and expectation propagation(EP)algorithm,named as StrBP-EP,for the coded OTFS-IM *** reduce the computational complexity of discrete distribution introduced by structured prior,Gaussian approximation conducted by EP is *** further reduce the complexity,we derive two variations of the proposed algorithm by using some *** results validate the superior performance of the proposed algorithm.

关键词： OTFS index modulation message passing belief propagation expectation propagation

Iterative multiuser detection for high spectral efficiency reverse-link of multibeam satellite via expectation propagation

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Journal of Communications and Information Networks 2016年第3期1卷 32-41页

作者： LIN Xincong WU Sheng NI Zuyao KUANG Linling HUANG Defeng SUN Baosheng Department of Aerospace Engineering Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China School of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth WA 6009Australia Beijing Space Information Relay and Transmission Technology Research Center Beijing 100094China

Multibeam satellite communications employing full frequency reuse have the potential to increase spectral ***,they suffer from severe inter-beam *** expectation propagation based message passing algorithm is proposed for decoding multi-user transmissions in the reverse link of multi-beam satellite communications with full frequency *** with an iterative MMSE(Minimum Mean Square Error)interference cancellation algorithm,the proposed algorithm reduces the cubic complexity to square complexity in the number of interfering *** results show that the proposed algorithm outperforms the iterative MMSE algorithm slightly in terms of bit error rate when the energy per bit to noise power spectral density ratio is *** performance of both algorithms is the same for other cases.

关键词： expectation propagation full frequency reuse factor graph iterative multiuser detection multibeam satellite communication message passing

Efficient Recovery of Structured Sparse Signals via Approximate Message Passing with Structured Spike and Slab Prior

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China Communications 2018年第6期15卷 1-17页

作者： Xiangming Meng Sheng Wu Michael Riis ANDersen Jiang Zhu Zuyao Ni Huawei Technologies Co.Ltd. Shanghai 201206China Tsinghua Space Center Tsinghua UniversityBeijing 100084China Department of Computer Science Aalto University P.O.Box 15400FI-00076Finland Ocean College Zhejiang UniversityZhoushan 316021China

Due to limited volume, weight and power consumption, micro-satellite has to reduce data transmission and storage capacity by image compression when performs earth observation missions. However, the quality of images may be unsatisfied. This paper considers the problem of recovering sparse signals by exploiting their unknown sparsity pattern. To model structured sparsity, the prior correlation of the support is encoded by imposing a transformed Gaussian process on the spike and slab probabilities. Then, an efficient approximate message-passing algorithm with structured spike and slab prior is derived for posterior inference, which, combined with a fast direct method, reduces the computational complexity significantly. Further, a unified scheme is developed to learn the hyperparameters using expectation maximization(EM) and Bethe free energy optimization. Simulation results on both synthetic and real data demonstrate the superiority of the proposed algorithm.

关键词： compressed sensing structuredsparsity spike and slab prior approximate message passing expectation propagation

Gridless Variational Bayesian Inference of Line Spectral from Quantized Samples

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China Communications 2021年第10期18卷 77-95页

作者： Jiang Zhu Qi Zhang Xiangming Meng The Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of EducationZhoushan 316021China Ocean College Zhejiang UniversityZhoushan 316021China Institute for Physics of Intelligence Graduate School of ScienceThe University of Tokyo7-3-1HongoTokyo 113-0033Japan

Efficient estimation of line spectral from quantized samples is of significant importance in information theory and signal processing,e.g.,channel estimation in energy efficient massive MIMO systems and direction of arrival *** goal of this paper is to recover the line spectral as well as its corresponding parameters including the model order,frequencies and amplitudes from heavily quantized *** this end,we propose an efficient gridless Bayesian algorithm named VALSE-EP,which is a combination of the high resolution and low complexity gridless variational line spectral estimation(VALSE)and expectation propagation(EP).The basic idea of VALSE-EP is to iteratively approximate the challenging quantized model of line spectral estimation as a sequence of simple pseudo unquantized models,where VALSE is ***,to obtain a benchmark of the performance of the proposed algorithm,the Cram′er Rao bound(CRB)is ***,numerical experiments on both synthetic and real data are performed,demonstrating the near CRB performance of the proposed VALSE-EP for line spectral estimation from quantized samples.

关键词： variational Bayesian inference expectation propagation quantization line spectral estimation mmse gridless

Vector Approximate Message Passing with Sparse Bayesian Learning for Gaussian Mixture Prior

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China Communications 2023年第5期20卷 57-69页

作者： Chengyao Ruan Zaichen Zhang Hao Jiang Jian Dang Liang Wu Hongming Zhang National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories No.9 Mozhou East RoadNanjing 211111China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Compressed sensing(CS)aims for seeking appropriate algorithms to recover a sparse vector from noisy linear ***,various Bayesian-based algorithms such as sparse Bayesian learning(SBL)and approximate message passing(AMP)based algorithms have been *** SBL,it has accurate performance with robustness while its computational complexity is high due to matrix *** AMP,its performance is guaranteed by the severe restriction of the measurement matrix,which limits its application in solving CS *** overcome the drawbacks of the above algorithms,in this paper,we present a low complexity algorithm for the single linear model that incorporates the vector AMP(VAMP)into the SBL structure with expectation maximization(EM).Specifically,we apply the variance auto-tuning into the VAMP to implement the E step in SBL,which decrease the iterations that require to converge compared with VAMP-EM algorithm when using a Gaussian mixture(GM)*** results show that the proposed algorithm has better performance with high robustness under various cases of difficult measurement matrices.

关键词： sparse Bayesian learning approximate message passing compressed sensing expectation propagation