Optimal Distributed Scheduling under Time-Varying Conditions: A Fast-CSMA Algorithm with Applications

Recently, low-complexity and distributed Carrier Sense Multiple Access (CSMA)-based scheduling algorithms have attracted extensive interest due to their throughput-optimal characteristics in general network topologies. However, these algorithms are not well-suited for time-varying environments (i.e., serving real-time traffic under time-varying channel conditions in wireless networks) for two reasons: (1) the mixing time of the underlying CSMA Markov Chain grows with the size of the network, which, for large networks, generates unacceptable delay for deadline-constrained traffic; (2) since the dynamic CSMA parameters are influenced by the arrival and channel state processes, the underlying CSMA Markov Chain may not converge to a steady-state under strict deadline constraints and fading channel conditions. In this paper, we attack the problem of distributed scheduling for time-varying environments. Specifically, we propose a Fast-CSMA (FCSMA) policy in fully-connected topologies, which converges much faster than the existing CSMA algorithms and thus yields significant advantages for time-varying applications. Then, we design optimal policies based on FCSMA techniques in two challenging and important scenarios in wireless networks for scheduling inelastic traffic with/without channel state information (CSI) over wireless fading channels.