Energy-Aware Wireless Scheduling with Near Optimal Backlog and Convergence Time Tradeoffs

This paper considers a wireless link with randomly arriving data that is queued and served over a time-varying channel. It is known that any algorithm that comes within ε of the minimum average power required for queue stability must incur average queue size at least Omega(log(1/ε)). However, the optimal convergence time is unknown, and prior algorithms give convergence time bounds of O(1/ε2). This paper develops a scheduling algorithm that, for any ε>0, achieves the optimal O(log(1/ε)) average queue size tradeoff with an improved convergence time of O(log(1/ε)/ε). This is shown to be within a logarithmic factor of the best possible convergence time. The method uses the simple drift-plus-penalty technique with an improved convergence time analysis.