A Restless MAB-Based Index Policy for UL Pilot Allocation in Massive MIMO over Gauss-Markov Fading Channels

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, we consider a massive MIMO system where the number of available orthogonal pilot sequences is less than the number of users. In order to optimize the long-term performance in estimating the uplink (UL) channels, the base station (BS) needs to judiciously decide the allocation of the available pilot sequences to different users in each training phase. In this paper, the pilot allocation problem is modeled as a partially observable Markov decision process with an exploitation-exploration tradeoff under the Gauss-Markov fading channels. We then investigate this problem within the restless multi-armed bandit (RMAB) framework and put forward a low-complexity method to derive the Whittle's index approximately. A simple index policy is then proposed to allocate the orthogonal pilots in a massive MIMO system. Numerical results demonstrate the superiority of the proposed index policy.