Cross-Layer Designs in Coded Wireless Fading Networks with Multicast

TL;DR

This paper develops a cross-layer optimization framework for wireless fading networks with network coding, providing provably optimal solutions and practical algorithms for resource allocation, power control, and network design.

Contribution

It proves dual decomposition optimality for multicast with network coding under continuous fading and introduces convergent subgradient algorithms with layered architecture and asynchronous updates.

Findings

Dual decomposition is optimal for multicast with network coding in continuous fading.

Proposed algorithms converge to near-optimal network configurations.

Asynchronous subgradient method enables practical physical layer updates.

Abstract

A cross-layer design along with an optimal resource allocation framework is formulated for wireless fading networks, where the nodes are allowed to perform network coding. The aim is to jointly optimize end-to-end transport layer rates, network code design variables, broadcast link flows, link capacities, average power consumption, and short-term power allocation policies. As in the routing paradigm where nodes simply forward packets, the cross-layer optimization problem with network coding is non-convex in general. It is proved however, that with network coding, dual decomposition for multicast is optimal so long as the fading at each wireless link is a continuous random variable. This lends itself to provably convergent subgradient algorithms, which not only admit a layered-architecture interpretation but also optimally integrate network coding in the protocol stack. The dual algorithm is also paired with a scheme that yields near-optimal network design variables, namely multicast end-to-end rates, network code design quantities, flows over the broadcast links, link capacities, and average power consumption. Finally, an asynchronous subgradient method is developed, whereby the dual updates at the physical layer can be affordably performed with a certain delay with respect to the resource allocation tasks in upper layers. This attractive feature is motivated by the complexity of the physical layer subproblem, and is an adaptation of the subgradient method suitable for network control.