APSIPA Transactions on Signal and Information Processing > Vol 9 > Issue 1

Discreteness and group sparsity aware detection for uplink overloaded MU-MIMO systems

Ryo Hayakawa, Osaka University, Japan, rhayakawa@sys.es.osaka-u.ac.jp , Ayano Nakai-Kasai, Kyoto University, Japan, Kazunori Hayashi, Kyoto University, Japan
 
Suggested Citation
Ryo Hayakawa, Ayano Nakai-Kasai and Kazunori Hayashi (2020), "Discreteness and group sparsity aware detection for uplink overloaded MU-MIMO systems", APSIPA Transactions on Signal and Information Processing: Vol. 9: No. 1, e21. http://dx.doi.org/10.1017/ATSIP.2020.19

Publication Date: 06 Oct 2020
© 2020 Ryo Hayakawa, Ayano Nakai-Kasai and Kazunori Hayashi
 
Subjects
 
Keywords
Overloaded MU-MIMOSignal detectionDiscretenessGroup sparsityConvex optimization
 

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In this article:
I. INTRODUCTION 
II. SYSTEM MODEL 
III. PROPOSED SIGNAL DETECTION METHOD 
IV. SIMULATION RESULTS 
V. CONCLUSION 

Abstract

This paper proposes signal detection methods for frequency domain equalization (FDE) based overloaded multiuser multiple input multiple output (MU-MIMO) systems for uplink Internet of things (IoT) environments, where a lot of IoT terminals are served by a base station having less number of antennas than that of IoT terminals. By using the fact that the transmitted signal vector has the discreteness and the group sparsity, we propose a convex discreteness and group sparsity aware (DGS) optimization problem for the signal detection. We provide an optimization algorithm for the DGS optimization on the basis of the alternating direction method of multipliers (ADMM). Moreover, we extend the DGS optimization into weighted DGS (W-DGS) optimization and propose an iterative approach named iterative weighted DGS (IW-DGS), where we iteratively solve the W-DGS optimization problem with the update of the parameters in the objective function. We also discuss the computational complexity of the proposed IW-DGS and show that we can reduce the order of the complexity by using the structure of the channel matrix. Simulation results show that the symbol error rate (SER) performance of the proposed method is close to that of the oracle zero forcing (ZF) method, which perfectly knows the activity of each IoT terminal.

DOI:10.1017/ATSIP.2020.19