Statistical Routing for Multihop Wireless Cognitive Networks

TL;DR

This paper introduces a novel cross-layer routing framework for cognitive multihop wireless networks that optimizes routes, transmission probabilities, and power levels based on channel and interference statistics, improving adaptability under uncertainty.

Contribution

It presents a new statistical routing approach using non-convex optimization and successive convex approximation, with a distributed algorithm suitable for online implementation.

Findings

Efficiently finds KKT solutions for complex routing optimization.

Distributed algorithm enables real-time adaptation in cognitive networks.

Applicable to conventional networks with channel uncertainty.

Abstract

To account for the randomness of propagation channels and interference levels in hierarchical spectrum sharing, a novel approach to multihop routing is introduced for cognitive random access networks, whereby packets are randomly routed according to outage probabilities. Leveraging channel and interference level statistics, the resultant cross-layer optimization framework provides optimal routes, transmission probabilities, and transmit-powers, thus enabling cognizant adaptation of routing, medium access, and physical layer parameters to the propagation environment. The associated optimization problem is non-convex, and hence hard to solve in general. Nevertheless, a successive convex approximation approach is adopted to efficiently find a Karush-Kuhn-Tucker solution. Augmented Lagrangian and primal decomposition methods are employed to develop a distributed algorithm, which also lends itself to online implementation. Enticingly, the fresh look advocated here permeates benefits also to conventional multihop wireless networks in the presence of channel uncertainty.