Throughput Optimal Distributed Power Control of Stochastic Wireless Networks

The maximum differential backlog (MDB), or “backpressure” control policy of Tassiulas and Ephremides has been shown to adaptively maximize the stable throughput of multihop 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 code-division multiple-access (CDMA)-based multihop wireless networks, we develop a set of node-based scaled gradient projection power control algorithms which solves the MDB optimization problem based on the high-signal-to-interference-plus-noise ratio (SINR) approximation of link capacities using low communication overhead. We investigate the impact of the high-SINR approximation and the nonnegligible convergence time required by the power control algorithms on the throughput region achievable by the iterative MDB policy. We show that the policy can achieve at least the stability region induced by the high-SINR capacity region.