Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop MIMO Cooperative Systems

TL;DR

This paper develops a low-complexity, distributive resource control algorithm for delay-sensitive two-hop MIMO cooperative systems, leveraging queue-awareness, auction-based control, and stochastic learning to optimize performance.

Contribution

It introduces a novel linear value function approximation and an auction-based control policy for distributed implementation, with proven convergence and optimality under heavy traffic.

Findings

The proposed algorithm reduces delay and improves throughput.

It achieves near-optimal performance with low complexity.

The method converges almost surely under heavy traffic conditions.

Abstract

In this paper, we consider a queue-aware distributive resource control algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay buffering is an effective way to reduce the intrinsic half-duplex penalty in cooperative systems. The complex interactions of the queues at the source node and the relays are modeled as an average-cost infinite horizon Markov Decision Process (MDP). The traditional approach solving this MDP problem involves centralized control with huge complexity. To obtain a distributive and low complexity solution, we introduce a linear structure which approximates the value function of the associated Bellman equation by the sum of per-node value functions. We derive a distributive two-stage two-winner auction-based control policy which is a function of the local CSI and local QSI only. Furthermore, to estimate the best fit approximation parameter, we propose a distributive online stochastic learning algorithm using stochastic approximation theory. Finally, we establish technical conditions for almost-sure convergence and show that under heavy traffic, the proposed low complexity distributive control is global optimal.