Augmenting Max-Weight with Explicit Learning for Wireless Scheduling with Switching Costs
For wireless network operators, this work provides a theoretically grounded method to reduce energy costs in small-cell networks with switching costs, addressing a known bottleneck in stability analysis.
This paper addresses energy cost minimization in small-cell wireless networks with switching costs while ensuring queue stability. It develops a learning and base station activation algorithm with slow dynamics combined with a fast Max-Weight channel scheduler, achieving near-optimal average energy costs and queue stability.
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.