Queue-aware Energy Efficient Control for Dense Wireless Networks

TL;DR

This paper proposes a threshold-based power control policy for dense wireless networks that optimally balances energy efficiency and queue management by approximating a complex stochastic optimization problem with mean-field methods.

Contribution

It introduces a mean-field approximation approach to solve a large-scale MDP for power allocation, resulting in a practical threshold-based policy for dense wireless networks.

Findings

The policy converges to a deterministic control as the number of devices grows.

The derived policy is simple to implement and effective in managing power and queues.

The approach improves energy efficiency while maintaining system performance.

Abstract

We consider the problem of long term power allocation in dense wireless networks. The framework considered in this paper is of interest for machine-type communications (MTC). In order to guarantee an optimal operation of the system while being as power efficient as possible, the allocation policy must take into account both the channel and queue states of the devices. This is a complex stochastic optimization problem, that can be cast as a Markov Decision Process (MDP) over a huge state space. In order to tackle this state space explosion, we perform a mean-field approximation on the MDP. Letting the number of devices grow to infinity the MDP converges to a deterministic control problem. By solving the Hamilton-Jacobi-Bellman Equation, we obtain a well-performing power allocation policy for the original stochastic problem, which turns out to be a threshold-based policy and can then be easily implemented in practice.