A stochastic framework for scheduling in wireless packet access networks

We put forth a unified framework for downlink and uplink scheduling of multiple connections with diverse quality-of-service requirements, where each connection transmits using adaptive modulation and coding over a wireless fading channel. Based on quantized channel state information at the transmitters (Q-CSIT), we derive the information-theoretic optimal downlink and uplink resource allocation/scheduling strategies using tools from convex/nonlinear optimization theory. When the fading statistics are not known a priori, we develop a class of stochastic primal-dual (SPD) algorithms which can dynamically adapt the scheduling policies online. We prove rigorously and confirm by simulations that with affordable complexity, these SPD algorithms asymptotically converge to the optimal scheduling strategies from any initial value.