Title
Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO versus Cooperative MIMO.
Abstract
This paper considers sparse device activity detection for cellular machine-type communications with non-orthogonal signatures using the approximate message passing algorithm. This paper compares two network architectures, massive multiple-input-multiple-output (MIMO) and cooperative MIMO, in terms of their effectiveness in overcoming inter-cell interference. In the massive MIMO architecture, each base station (BS) detects only the users from its own cell while treating inter-cell interference as noise. In the cooperative MIMO architecture, each BS detects the users from neighboring cells as well; the detection results are then forwarded in the form of a log-likelihood ratio (LLR) to a central unit where final decisions are made. This paper analytically characterizes the probabilities of false alarm and missed detection for both architectures. The numerical results validate the analytic characterization and show that as the number of antennas increases, a massive MIMO system effectively drives the detection error to zero, while as the cooperation size increases, the cooperative MIMO architecture mainly improves the cell-edge user performance. Moreover, this paper studies the effect of LLR quantization to account for the finite-capacity fronthaul. The numerical simulations of a practical scenario suggest that in specific case cooperating three BSs in a cooperative MIMO system achieves about the same cell-edge detection reliability as a non-cooperative massive MIMO system with four times the number of antennas per BS.
Year
DOI
Venue
2019
10.1109/TWC.2019.2920823
IEEE Transactions on Wireless Communications
Keywords
DocType
Volume
MIMO communication,Computer architecture,Microprocessors,Wireless communication,Antennas,Compressed sensing
Journal
abs/1906.09494
Issue
ISSN
Citations 
8
1536-1276
10
PageRank 
References 
Authors
0.46
16
3
Name
Order
Citations
PageRank
Zhilin Chen1423.91
Foad Sohrabi234214.02
Wei Yu36173537.26