Delay-aware BS Discontinuous Transmission Control and User Scheduling for Energy Harvesting Downlink Coordinated MIMO Systems

TL;DR

This paper develops a delay-optimal control and user scheduling framework for energy harvesting downlink coordinated MIMO systems, employing a two-timescale approach with reduced complexity and distributed implementation.

Contribution

It introduces a novel two-timescale control scheme modeled as a POMDP, with structural results and a distributed algorithm ensuring convergence and stability in energy harvesting MIMO networks.

Findings

Distributed control algorithm converges almost surely.

Derived conditions for data queue stability.

Reduced complexity Bellman equation for practical implementation.

Abstract

In this paper, we propose a two-timescale delay-optimal base station Discontinuous Transmission (BS-DTX) control and user scheduling for downlink coordinated MIMO systems with energy harvesting capability. To reduce the complexity and signaling overhead in practical systems, the BS-DTX control is adaptive to both the energy state information (ESI) and the data queue state information (QSI) over a longer timescale. The user scheduling is adaptive to the ESI, the QSI and the channel state information (CSI) over a shorter timescale. We show that the two-timescale delay-optimal control problem can be modeled as an infinite horizon average cost Partially Observed Markov Decision Problem (POMDP), which is well-known to be a difficult problem in general. By using sample-path analysis and exploiting specific problem structure, we first obtain some structural results on the optimal control policy and derive an equivalent Bellman equation with reduced state space. To reduce the complexity and facilitate distributed implementation, we obtain a delay-aware distributed solution with the BS-DTX control at the BS controller (BSC) and the user scheduling at each cluster manager (CM) using approximate dynamic programming and distributed stochastic learning. We show that the proposed distributed two-timescale algorithm converges almost surely. Furthermore, using queueing theory, stochastic geometry and optimization techniques, we derive sufficient conditions for the data queues to be stable in the coordinated MIMO network and discuss various design insights.