Cross-Layer Design of Wireless Multihop Networks over Stochastic Channels with Time-Varying Statistics

TL;DR

This paper develops a stochastic channel-aware NUM framework for wireless multihop networks, enabling adaptive scheduling, routing, and power control over time-varying fading channels with non-convex utilities.

Contribution

It introduces a novel NUM formulation that models wireless channels with stochastic differential equations, addressing non-stationarity and non-convex utilities in cross-layer network design.

Findings

The proposed framework effectively manages stochastic fading channels.

Numerical results demonstrate convergence and utility improvements.

Energy-efficient power control enhances user utility in orthogonal access scenarios.

Abstract

Network Utility Maximization (NUM) is often applied for the cross-layer design of wireless networks considering known wireless channels. However, realistic wireless channel capacities are stochastic bearing time-varying statistics, necessitating the redesign and solution of NUM problems to capture such effects. Based on NUM theory we develop a framework for scheduling, routing, congestion and power control in wireless multihop networks that considers stochastic Long or Short Term Fading wireless channels. Specifically, the wireless channel is modeled via stochastic differential equations alleviating several assumptions that exist in state-of-the-art channel modeling within the NUM framework such as the finite number of states or the stationarity. Our consideration of wireless channel modeling leads to a NUM problem formulation that accommodates non-convex and time-varying utilities. We consider both cases of non orthogonal and orthogonal access of users to the medium. In the first case, scheduling is performed via power control, while the latter separates scheduling and power control and the role of power control is to further increase users' optimal utility by exploiting random reductions of the stochastic channel power loss while also considering energy efficiency. Finally, numerical results evaluate the performance and operation of the proposed approach and study the impact of several involved parameters on convergence.