Sparsity-Exploiting Blind Receiver Algorithms for Unsourced Multiple Access in MIMO and Massive MIMO Channels

Liu, Jiaai; Wang, Xiaodong

doi:10.1109/tcomm.2021.3116239

Cited by 20 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the simulations and applications, the log-sum algorithm is applied to a classic compressed sensing problem, namely, sparse signal recovery, which can be indicated as (1). Our aim is to evaluate the convergence, robustness, and effectiveness of the proposed algorithm via these simulations.…”

Section: Simulations and Applicationsmentioning

confidence: 99%

“…The sparse problems encountered in many domains of scientific research and engineering practice have attracted extensive attention in recent years, such as MmWave Massive MIMO channel estimation [1]- [3], machine learning [4], [5], jammer detection [6], [7], image processing [8]- [12]. The canonical form of this problems can be expressed as…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Iterative Threshold Algorithm of Log-Sum Regularization for Sparse Problem

Zhou

Liu

Zhang

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

The log-sum regularization has been always drawing widespread attention in the field of sparse problem. However, it brings about a non-convex, non-smooth, and non-Lipschitz optimization problem that is difficult to tackle. To overcome the problem, an iterative threshold algorithm of log-sum regularization is proposed in this paper. Firstly, by deducing the derivative mathematical expression of log-sum function, a property theorem about solution for log-sum regularization is established. Secondly, based on the above theorem, the optimal setting rules of the compromising parameters are elaborated, and the iterative logsum threshold algorithm is proposed. Thirdly, under the situation that the compromising parameters of log-sum regularization are relatively small, it can be proven that the proposed algorithm converges to a local minimizer of log-sum regularization. Finally, a series of simulations are implemented to examine performance of the algorithm, and the results exhibit that the proposed algorithm outperforms the state-of-the-art algorithms in terms of iterations and precision.

show abstract

Section: Simulations and Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Iterative Threshold Algorithm of Log-Sum Regularization for Sparse Problem

Zhou

Liu

Zhang

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

show abstract

“…Fengler et al in [13] combine a non-Bayesian sparse recovery algorithm with an outer code aimed at message disambiguation to facilitate MIMO URA; the performance of this algorithm can be improved using successive cancellation list decoding, as shown in [14]. Liu and Wang borrow ideas from slot-based transmissions, and they propose a receiver that merges simultaneous orthogonal matching pursuit (S-OMP) and bilnd channel estimation [15]. The same authors also propose an iterative receiver based on sparse Tanner graph; therein, each iteration can recover at most three codewords [16].…”

Section: A Related Workmentioning

confidence: 99%

FASURA: A Scheme for Quasi-Static Massive MIMO Unsourced Random Access Channels

Gkagkos¹,

Narayanan²,

Jean-Francois³

et al. 2022

Preprint

View full text Add to dashboard Cite

This article considers the massive MIMO unsourced random access problem on a quasi-static Rayleigh fading channel. Given a fixed message length and a prescribed number of channel uses, the objective is to construct a coding scheme that minimizes the energy-per-bit subject to a fixed probability of error. The proposed scheme differs from other state-of-the-art schemes in that it blends activity detection, single-user coding, pilot-aided and temporary decisions-aided iterative channel estimation and decoding, minimum-mean squared error (MMSE) estimation, and successive interference cancellation (SIC). We show that an appropriate combination of these ideas can substantially outperform state-of-the-art coding schemes when the number of active users is more than 100, making this the best performing scheme known for this regime.

show abstract