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CRC-Aided sparse regression codes for Unsourced Random Access

IEEE COMMUNICATIONS LETTERS 2023年第8期27卷 1944-1948页

作者： Cao, Haiwen Xing, Jiongyue Liang, Shansuo Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Huawei Technol Co Ltd Cent Res Inst Theory Lab Labs 2012 Hong Kong Peoples R China

This letter considers a coding scheme for unsourced random access (URA) based on sparse regression codes (SPARCs). In particular, an efficient concatenated coding scheme is proposed, which concatenates SPARCs and cyclic redundancy check-based block Markov superposition transmission (CRC-BMST) codes. A hybrid decoder consisting of a successive cancellation algorithm and a simplified approximated message passing (AMP) algorithm is presented for inner SPARCs, and an improved tree decoder is proposed for outer CRC-BMST codes by introducing a pruning technique. Simulation results show the proposed coding scheme outperforms the coded compressed sensing (CCS) scheme with lower computational complexity.

关键词： Unsourced random access sparse regression codes CRC codes block Markov superposition transmission

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On the Finite Length Performance of sparse regression codes with Peak-Power Limitation

On the Finite Length Performance of Sparse Regression Codes ...

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IEEE Information Theory Workshop (ITW)

作者： Liang, Shansuo Bai, Bo Zhang, Gong Huawei Technol Co Ltd Cent Res Inst Theory Lab Labs 2012 Shenzhen Peoples R China

ISBN: (纸本)9781728159621

This paper concerns practical issues of sparse regression codes (SRCs) with approximate message passing (AMP) decoding. First, Gaussian signaling of SRC incurs a high peak-to-average-power ratio (PAPR) problem. Second, the finite length performance of SRCs is poor at low-to-medium rates and cannot be improved by spatial coupling or power allocation. We confront the two challenges by introducing clipping to SRC. For the encoder, clipping is applied to the Gaussian codeword of SRC for reducing the high PAPR. For the decoder, generalized approximate message passing (GAMP) is used to handle the nonlinear clipping distortion. Interestingly, we observe that clipping with proper thresholds can improve the performance of SRC and the performance gain is large at low rates. Based on the state evolution analysis of GAMP decoding, we provide an explanation for such observation from the curve-matching perspective. In the end, some guidelines are provided for choosing proper clipping thresholds empirically.

关键词： sparse regression codes approximate message passing PAPR clipping state evolution curve-matching

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Spatial Modulation and sparse regression codes 19

Spatial Modulation and Sparse Regression Codes

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19th International Symposium on Wireless Communication Systems (ISWCS)

作者： Al Kishki, Ahmed Kuehn, Volker Univ Rostock Fac Comp Sci & Elect Engn Rostock Germany

ISBN: (纸本)9798350362527;9798350362510

The use of multiple antennas at both transmitter and receiver significantly improves the performance of wireless communication systems. multiple-input multiple-output (MIMO) systems are typically paired with orthogonal frequency-division multiplexing (OFDM) to handle frequency selective (FS) channels in an efficient way. This paper focuses on single-carrier MIMO systems using spatial modulation (SM), which transmits information by selecting a few active antennas from a larger set. Using segmentation at the transmitter, SM systems have the same mathematical structure as spatially coupled sparse regression codes (SC-SPARCs) and additive white Gaussian noise (AWGN) transmission. These codes are known to achieve AWGN capacity with approximate message passing (AMP) decoding. We analyze the performance of SM with AMP decoding for FS channels. This approach handles intersymbol interference without the need for cyclic prefixes. We compare different AMP variants and examine the impact of various system parameters like the channel memory and modulations schemes.

关键词： spatial modulation compressed sensing approximate message passing sparse regression codes spatial coupling multiple antenna systems

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Lossy Compression via sparse regression codes: An Approximate Message Passing Approach

Lossy Compression via Sparse Regression Codes: An Approximat...

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IEEE Information Theory Workshop (ITW)

作者： Wu, Huihui Wang, Wenjie Liang, Shansuo Han, Wei Bai, Bo Huawei Tech Investment Co Ltd Theory Lab 2012 Labs Hong Kong Peoples R China

ISBN: (纸本)9798350301496

This paper presents a low-complexity lossy compression scheme for Gaussian vectors, using sparse regression codes (SRC) and a novel decimated approximate message passing (AMP) encoder. The sparse regression codebook is characterized by a design matrix and each codeword is a linear combination of selected columns of the matrix. In order to enable the convergence of AMP for lossy compression, we incorporate the concept of decimation into the AMP algorithm for the first time. Further, we show that the power allocation technique is beneficial for improving the rate-distortion performance. The computational complexity of the proposed encoding is O(log n) per source sample for a length-n source vector, using a sub-Fourier design matrix. Moreover, the proposed AMP encoder inherently supports successively refinable compression. Simulation results show that the proposed decimated AMP encoder significantly outperforms the existing successive-approximation encoding [1] and approaches the rate-distortion limit in low-rate regime.

关键词： Lossy compression sparse regression codes approximate message passing Gaussian rate-distortion

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Techniques for Improving the Finite Length Performance of sparse Superposition codes

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IEEE TRANSACTIONS ON COMMUNICATIONS 2018年第3期66卷 905-917页

作者： Greig, Adam Venkataramanan, Ramji Univ Cambridge Dept Engn Cambridge CB2 1PZ England

sparse superposition codes are a recent class of codes introduced by Barron and Joseph for efficient communication over the AWGN channel. With an appropriate power allocation, these codes have been shown to be asymptotically capacity-achieving with computationally feasible decoding. However, a direct implementation of the capacity-achieving construction does not give good finite length error performance. In this paper, we consider sparse superposition codes with approximate message passing (AMP) decoding, and describe a variety of techniques to improve their finite length performance. These include an iterative algorithm for SPARC power allocation, guidelines for choosing codebook parameters, and estimating a critical decoding parameter online instead of precomputation. We also show how partial outer codes can be used in conjunction with AMP decoding to obtain a steep waterfall in the error performance curves. We compare the error performance of AMP-decoded sparse superposition codes with coded modulation using LDPC codes from the WiMAX standard.

关键词： sparse regression codes approximate message passing low-complexity decoding finite length performance coded modulation

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The Error Probability of sparse Superposition codes With Approximate Message Passing Decoding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第5期65卷 3278-3303页

作者： Rush, Cynthia Venkataramanan, Ramji Columbia Univ Dept Stat New York NY 10027 USA Univ Cambridge Dept Engn Cambridge CB2 1PZ England

sparse superposition codes, or sparse regression codes, are a recent class of codes for reliable communication over the AWGN channel at rates approaching the channel capacity. Approximate message passing (AMP) decoding, a computationally efficient technique for decoding SPARCs, has been proven to be asymptotically capacity-achieving for the AWGN channel. In this paper, we refine the asymptotic result by deriving a large deviation bound on the probability of an AMP decoding error. This bound gives insight into the error performance of the AMP decoder for large but finite problem sizes, giving an error exponent as well as guidance on how the code parameters should be chosen at finite block lengths. For an appropriate choice of code parameters, we show that for any fixed rate less than the channel capacity, the decoding error probability decays exponentially in n/(log n)(2T), where T, the number of AMP iterations required for successful decoding, is bounded in terms of the gap from capacity.

关键词： sparse regression codes capacity achieving codes error exponent large deviations approximate message passing iterative decoding

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A Fully Bayesian Approach for Massive MIMO Unsourced Random Access

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第8期71卷 4620-4635页

作者： Jiang, Jia-Cheng Wang, Hui-Ming Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Shaanxi Peoples R China

In this paper, we propose a novel fully Bayesian approach for the massive multiple-input multiple-output (MIMO) massive unsourced random access (URA). The payload of each user device is coded by the sparse regression codes (SPARCs) without redundant parity bits. A Bayesian model is established to capture the probabilistic characteristics of the overall system. Particularly, we adopt the core idea of the model-based learning approach to establish a flexible Bayesian channel model to adapt the complex environments. Different from the traditional divide-and-conquer or pilot-based massive MIMO URA strategies, we propose a three-layer message passing (TLMP) algorithm to jointly decode all the information blocks, as well as acquire the massive MIMO channel, which adopts the core idea of the variational message passing and approximate message passing. We verify that our proposed TLMP significantly enhances the spectral efficiency compared with the state-of-the-arts baselines, and is more robust to the possible codeword collisions.

关键词： Index Terms-Unsourced random access sparse regression codes fully Bayesian approach approximate message passing variational message passing massive MIMO

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Unsourced Random Access With Coded Compressed Sensing: Integrating AMP and Belief Propagation

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第4期68卷 2384-2409页

作者： Amalladinne, Vamsi K. Pradhan, Asit Kumar Rush, Cynthia Chamberland, Jean-Francois Narayanan, Krishna R. Qualcomm Technol Inc Qualcomm Res San Diego CA 92121 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA Columbia Univ Dept Stat New York NY 10027 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

sparse regression codes with approximate message passing (AMP) decoding have gained much attention in recent times. The concepts underlying this coding scheme extend to unsourced random access with coded compressed sensing (CCS), as first demonstrated by Fengler, Jung, and Caire. Specifically, their approach employs a concatenated coding framework with an inner AMP decoder followed by an outer tree decoder. In their original implementation, these two components work independently of each other, with the tree decoder acting on the static output of the AMP decoder. This article introduces a novel framework where the inner AMP decoder and the outer decoder operate in tandem, dynamically passing information back and forth to take full advantage of the underlying CCS structure. This scheme necessitates the redesign of the outer code as to enable belief propagation in a computationally tractable manner. The enhanced architecture exhibits significant performance benefits over a range of system parameters. The error performance of the proposed scheme can be accurately predicted through a set of equations known as state evolution of AMP. These findings are supported both analytically and through numerical methods.

关键词： Decoding Encoding Parity check codes Compressed sensing Convergence Payloads Forward error correction Unsourced random access sparse regression codes approximate message passing belief propagation coded compressed sensing concatenated coding

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Capacity-Achieving sparse Superposition codes via Approximate Message Passing Decoding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2017年第3期63卷 1476-1500页

作者： Rush, Cynthia Greig, Adam Venkataramanan, Ramji Yale Univ Dept Stat New Haven CT 06511 USA Columbia Univ Dept Stat New York NY 10027 USA Univ Cambridge Dept Engn Cambridge CB2 1PZ England

sparse superposition codes were recently introduced by Barron and Joseph for reliable communication over the additive white Gaussian noise (AWGN) channel at rates approaching the channel capacity. The codebook is defined in terms of a Gaussian design matrix, and codewords are sparse linear combinations of columns of the matrix. In this paper, we propose an approximate message passing decoder for sparse superposition codes, whose decoding complexity scales linearly with the size of the design matrix. The performance of the decoder is rigorously analyzed and it is shown to asymptotically achieve the AWGN capacity with an appropriate power allocation. Simulation results are provided to demonstrate the performance of the decoder at finite blocklengths. We introduce a power allocation scheme to improve the empirical performance, and demonstrate how the decoding complexity can be significantly reduced by using Hadamard design matrices.

关键词： sparse regression codes capacity-achieving codes AWGN channel coded modulation low-complexity decoding compressed sensing

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Modulated sparse Superposition codes for the Complex AWGN Channel

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第7期67卷 4385-4404页

作者： Hsieh, Kuan Venkataramanan, Ramji Univ Cambridge Dept Engn Cambridge CB2 1PZ England

This paper studies a generalization of sparse superposition codes (SPARCs) for communication over the complex additive white Gaussian noise (AWGN) channel. In a SPARC, the codebook is defined in terms of a design matrix, and each codeword is a generated by multiplying the design matrix with a sparse message vector. In the standard SPARC construction, information is encoded in the locations of the non-zero entries of the message vector. In this paper we generalize the construction and consider modulated SPARCs, where information is encoded in both the locations and the values of the non-zero entries of the message vector. We focus on the case where the non-zero entries take values from a phase-shift keying (PSK) constellation. We propose a computationally efficient approximate message passing (AMP) decoder, and obtain analytical bounds on the state evolution parameters which predict the error performance of the decoder. Using these bounds we show that PSK-modulated SPARCs are asymptotically capacity achieving for the complex AWGN channel, with either spatial coupling or power allocation. We also provide numerical simulation results to demonstrate the error performance at finite code lengths. These results show that introducing modulation to the SPARC design can significantly reduce decoding complexity without sacrificing error performance.

关键词： Decoding AWGN channels Modulation Couplings Resource management Encoding Standards sparse superposition codes sparse regression codes AWGN channel approximate message passing state evolution spatial coupling capacity-achieving codes compressed sensing

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