Learning-Based Joint Channel Prediction and Antenna Selection for Massive MIMO with Partial CSI

Abstract

This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels.

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

“…Since the number of RF chains is smaller than the number of antennas, only an incomplete CSI can be observed and estimated from each pilot transmission. This indicates that acquiring complete CSI eventually leads to extra pilot transmissions and reducing the effective transmission rate [1], [15], [17], [18]. For instance, acquiring complete CSI could occupy more than half of the frame duration for the downlink of a typical time-division duplexing (TDD) massive MIMO system with 64 antennas, 16 RF chains and 4 singleantenna users [15], [19].…”

“…2) Antenna Selection with Incomplete CSI: Recently, researchers have focused on developing AS algorithms which can directly operate under incomplete CSI [1], [15], [17]. In [15], the authors formulated the AS problem as a combinatorial multi-armed bandit problem when only incomplete CSI is available, and proposed an online AS algorithm using Thompson sampling.…”

“…Observation implement the flow steps via normalization layers, invertible convolutional layers and affine coupling layers, where the details of these layers can be found in [1], [30], [32], [33].…”

Fast Optimal Antenna Selection for Massive MIMO

“…The authors therein showed that the Monte-Carlo tree search algorithm achieves better performance than the greedy selection. For the practical scenarios, the authors of [9] proposed a learning based antenna selection to improve the system efficiency with imperfect channel state information (CSI). Moreover, the authors of [10] overcame the nonconvexity by proposing a joint antenna selection and precoding design, which maximizes the system performance to practical constraints.…”