Multiuser MIMO Opportunistic Beamforming Communications: State-of-the-Art and Perspectives

Sun, Wen-Bin; Meng, Weixiao; Guo, Ji-Chong; Li, Cheng

doi:10.1109/mwc.2022.2000479

Cited by 4 publications

(2 citation statements)

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“…However, the full potential of massive MIMO requires a huge number of dedicated radio frequency (RF) chains for every antenna, which results in not only increased capital expenditure (CAPEX) but also higher system energy consumption [3]- [5]. In practical massive MIMO systems, it is more cost-effective and energy-efficient to employ a number of RF chains less than the number of antennas, while the full spatial multiplexing gain can be preserved via antenna selection (AS) [4], [5]. Being a key component of hybrid signal processing techniques, AS aims to select the best subset of antennas for data transmission to reduce hardware cost and power consumption without losing the full potentials of antenna arrays [3].…”

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“…Being a key component of hybrid signal processing techniques, AS aims to select the best subset of antennas for data transmission to reduce hardware cost and power consumption without losing the full potentials of antenna arrays [3]. During the last decades, the AS problem has been extensively studied in the presence of complete channel state information (CSI) (sometimes refer to full CSI), and it has been shown that AS can provide similar spectral efficiency with a lower energy consumption compared to the case without AS [4], [5].…”

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Risk-Aware Antenna Selection for Multiuser Massive MIMO Under Incomplete CSI

He,

Vu,

Hoang

et al. 2024

IEEE Trans. Wireless Commun.

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This paper investigates the antenna selection problem in massive multiple-input multiple-out (MIMO) systems under incomplete channel state information (CSI), with a particular interest on risk-aware planning subjected to practical constraints such as transmit power budgets and quality of services (QoS). Due to a very large number of antennas, obtaining complete channel measurements becomes a cost-prohibitive, energy-inefficient and spectral-inefficient task. To reduce pilot overhead, incomplete CSI and antenna selection (AS) are expected in practical massive MIMO systems. However, most existing AS algorithms heavily rely on the complete CSI, which imposes a high probability of violating the practical constraints in the scenarios of our interests. Motivated by this, we propose a joint channel prediction and antenna selection framework (JCPAS) which efficiently performs AS and is robust against the incomplete CSI and practical constraints. The proposed framework comprises i) a channel tracker which estimates the channel dynamics based on historical incomplete observations, and ii) a risk-aware Monte Carlo tree search (RA-MCTS) algorithm which utilizes the estimated channel dynamics to select antennas in a risk-aware manner. Simulation results show that the proposed RA-MCTS not only achieves much lower energy consumption compared to the existing typical algorithms, but also significantly reduces the probability of violating the practical constraints.Index Terms-Massive MIMO, antenna selection, incomplete CSI, machine learning, risk-aware planning, Monte Carlo tree search I. INTRODUCTIONM ASSIVE multiple-input multiple-out (MIMO) has became the key technology to support the continuous development of future wireless network applications [2]- [5]. By deploying a very large number of antennas at the base station (BS), massive MIMO is capable of significantly improving the spectral efficiency via spatial multiplexing gain Manuscript

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