Augmenting Max-Weight with Explicit Learning for Wireless Scheduling with Switching Costs

TL;DR

This paper proposes a novel wireless scheduling algorithm that combines explicit learning with Max-Weight, effectively managing switching costs and ensuring queue stability while minimizing energy costs in small-cell networks.

Contribution

It introduces a new approach integrating learning with Max-Weight scheduling to handle switching costs, overcoming limitations of traditional methods.

Findings

Achieves near-optimal energy costs with queue stability.

Demonstrates convergence of co-evolving dynamics using Markov chain analysis.

Effectively manages switching costs in wireless network scheduling.

Abstract

In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to switch off dynamically a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach -- a Max-Weight algorithm along with a Lyapunov-based stability argument -- does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.