Reference : User selection for massive MIMO under line-of-sight propagation
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
User selection for massive MIMO under line-of-sight propagation
Chaves, Rafael da Silva mailto [Federal University of Rio de Janeiro]
Cetin, Ediz mailto [Macquarie University]
Lima, Markus V. S. mailto [Federal University of Rio de Janeiro]
Alves Martins, Wallace mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
IEEE Open Journal of the Communications Society
Institute of Electrical and Electronics Engineers (IEEE)
New York
United States - New York
[en] Massive MIMO ; favorable propagation ; user selection ; line-of-sight channel ; inter-channel interference
[en] This paper provides a review of user selection algorithms for massive multiple-input multiple-output (MIMO) systems under the line-of-sight (LoS) propagation model. Although the LoS propagation is extremely important to some promising technologies, like in millimeter-wave communications, massive MIMO systems are rarely studied under this propagation model. This paper fills this gap by providing a comprehensive study encompassing several user selection algorithms, different linear precoders and simulation setups, and also considers the effect of partial channel state information (CSI). One important result is the existence of practical cases in which the LoS propagation model may lead to significant levels of interference among users within a cell; these cases are not satisfactorily addressed by the existing user selection algorithms. Motivated by this issue, a new user selection algorithm based on inter-channel interference (ICI) called ICI-based selection (ICIBS) is proposed. Unlike other techniques, the ICIBS accounts for the ICI in a global manner, thus yielding better results, especially in cases where there are many users interfering with each other. In such scenarios, simulation results show that when compared to the competing algorithms, the proposed approach provided an improvement of at least 10.9% in the maximum throughput and 7.7% in the 95%-probability throughput when half of the users were selected.
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