Joint Scheduling and Routing for Ad-hoc Networks Under Channel State Uncertainty

We determine a joint link activation and routing policy that maximizes the stable throughput region of time-varying wireless ad-hoc networks with multiple commodities. In practice, the state of the channel process from the time it is observed till the time a transmission actually takes place can be significantly different. With this in mind, we introduce a stationary policy that takes scheduling and routing decisions based on a possibly inaccurate estimate of the true channel state. We show optimality of this policy within a broad class of link activation processes under certain mild conditions. In particular, processes in this class may be induced almost by any policy, possibly non-stationary, even anticipative and aware of the entire sample paths of the arrival, estimated and true channel processes, provided that it has no knowledge on the current true channel state, besides that available through its estimate.

[1]  Leandros Tassiulas Scheduling and performance limits of networks with constantly changing topology , 1997 .

[2]  Leandros Tassiulas,et al.  Scheduling and performance limits of networks with constantly changing topology , 1997, IEEE Trans. Inf. Theory.

[3]  Prakash Narayan,et al.  Reliable Communication Under Channel Uncertainty , 1998, IEEE Trans. Inf. Theory.

[4]  Georgios B. Giannakis,et al.  Adaptive MIMO-OFDM based on partial channel state information , 2003, 2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2003 (IEEE Cat. No.03EX689).

[5]  Eytan Modiano,et al.  Dynamic power allocation and routing for time varying wireless networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[6]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1990, 29th IEEE Conference on Decision and Control.

[7]  John Odentrantz,et al.  Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues , 2000, Technometrics.

[8]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1992 .

[9]  Georgios B. Giannakis,et al.  Adaptive MIMO-OFDM based on partial channel state information , 2004, IEEE Transactions on Signal Processing.

[10]  P. Baran,et al.  On Distributed Communications Networks , 1964 .