Throughput Optimal Distributed Control of Stochastic Wireless Networks

The Maximum Differential Backlog (MDB) control policy of Tassiulas and Ephremides has been shown to adaptively maximize the stable throughput of multi-hop wireless networks with random traffic arrivals and queueing. The practical implementation of the MDB policy in wireless networks with mutually interfering links, however, requires the development of distributed optimization algorithms. Within the context of CDMA-based multi-hop wireless networks, we develop a set of node-based scaled gradient projection power control algorithms which solves the MDB optimization problem in a distributed manner using low communication overhead. As these algorithms require time to converge to a neighborhood of the optimum, the implementation of the MDB policy must be done with delayed queue state information. For this, we show that the MDB policy with delayed queue state information remains throughput optimal.