  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 optimal rates determined by the
MDB policy can only be found iteratively over time. For this, we show that the
iterative MDB policy with convergence time remains throughput optimal.
