Learning to Compute Ergodic Rate for Multi-cell Scheduling in Massive MIMO

Shi, Junchao, Wang, Wenjin, Yi, Xinping ORCID: 0000-0001-5163-2364, Wang, Jiaheng, Gao, Xiqi, Liu, Qing and Li, Geoffrey (2020) Learning to Compute Ergodic Rate for Multi-cell Scheduling in Massive MIMO. IEEE Transactions on Wireless Communications, 20 (2). pp. 785-797.

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Official URL: http://dx.doi.org/10.1109/twc.2020.3028365

Abstract

In this article, we investigate multi-cell scheduling for massive multiple-input-multiple-output (MIMO) communications with only statistical channel state information (CSI). The objective of multi-cell scheduling is to activate a subset of users so as to maximize the ergodic sum rate subject to per-cell total transmit power constraint. By adopting beam division multiple access based on the statistical CSI, i.e., channel-coupling matrix (CCM), we simplify multi-cell scheduling as a power control problem in the beam domain, by which the ergodic sum rate is maximized. To reduce the computational burden on finding the ergodic sum rate, we propose a learning-to-compute strategy, which directly computes the complex ergodic rate function from CCMs via a deep neural network. Specifically, by modeling the probability density function of the ordered eigenvalues of the Hermitian CCM matrices as exponential family distributions, a properly designed hybrid neural network makes the ergodic rate computation feasible. With the learning-to-compute strategy, the online computational complexity of multi-cell scheduling is substantially reduced compared with the existing Monte Carlo or deterministic equivalent (DE) based methods while maintaining nearly the same performance.

Item Type:	Article
Uncontrolled Keywords:	Processor scheduling, Massive MIMO, Resource management, Neural networks, Covariance matrices, Scheduling, Wireless communication, Beam division multiple access, massive MIMO, neural network, statistical CSI, mulit-cell scheduling
Depositing User:	Symplectic Admin
Date Deposited:	12 Oct 2020 08:34
Last Modified:	17 Mar 2024 10:14
DOI:	10.1109/TWC.2020.3028365
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3103974