Scheduling in mobile ad hoc networks with topology and channel-state uncertainty

We study throughput-optimal scheduling in mobile ad hoc networks with time-varying (fading) channels. Traditional back-pressure algorithms (based on the work by Tassiulas and Ephremides) require instantaneous network state (topology, queues-lengths, and fading channel-state) in order to make scheduling/routing decisions. However, such instantaneous network-wide (global) information is hard to come by in practice, especially when mobility induces a time-varying topology. With information delays and a lack of global network state, different mobile nodes have differing views of the network, thus inducing uncertainty and inconsistency across mobile nodes in their topology knowledge and network state information. In such a setting, we first characterize the throughput-optimal rate region and develop a back-pressure-like scheduling algorithm, which we show is throughput-optimal. Then, by randomly partitioning the geographic region spatially into disjoint and interference-free sub-areas, and sharing delayed topology and network state information only among nearby mobile nodes, we develop a localized low-complexity scheduling algorithm. The algorithm uses instantaneous local information (the queue length, channel state and current position at a mobile node) along with delayed network state information from nearby nodes (i.e., from nodes that were within a nearby geographic region as opposed to network-wide information). The proposed algorithm is shown to be near-optimal, where the geographic distance over which delayed network-state information is shared determines the provable lower bound on the achievable throughput.