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

This paper considers a wireless link with randomly arriving data that are 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 Ω(log(1/ε)). However, the optimal convergence time is unknown. This paper develops a scheduling algorithm that, for any ε > 0, achieves the optimal O(log(1/ε)) average queue size tradeoff with a convergence time of O(log(1/ε)/ε). An example system is presented for which all algorithms require convergence time at least Ω(1/ε), and so the proposed algorithm is within a logarithmic factor of the optimal convergence time. The method uses the simple drift-plus-penalty technique with an improved convergence time analysis.