Uplink Pilot Allocation in Massive MIMO over Gauss-Markov Fading Channels

Abstract

In a time-division duplex (TDD) massive multiple-input multiple-output (MIMO) system, the number of available orthogonal pilot sequences in each cell is limited. In this paper, by assuming Gauss-Markov fading channels, we consider a massive MIMO system where there are less available orthogonal pilots than the number of users. In order to optimize the long-term performance in estimating the uplink (UL) channels, the base station (BS) judiciously decides the allocation of the available pilots to different users in each training phase. The pilot allocation problem is a partially observable Markov decision process with an exploitation-exploration tradeoff that is difficult to analyze. We thus investigate the problem in the framework of restless multi-armed bandits (RMAB) problems and carry out an indexability analysis for the problem. Furthermore, we solve this problem by using the Whittle's index policy with a low complexity. Numerical results demonstrate the superiority of the Whittle's index policy.