Optimal Routing with Mutual Information Accumulation in Wireless Networks

TL;DR

This paper explores optimal routing strategies in wireless networks utilizing mutual information accumulation with rateless codes, proposing simple greedy algorithms and heuristics that outperform traditional methods and are practical for implementation.

Contribution

It introduces the first optimal greedy algorithms for complex routing problems with mutual information accumulation, simplifying decision-making in wireless networks.

Findings

Greedy algorithms achieve optimal solutions without linear programming.

Heuristics perform close to optimal in simulations.

Mutual information accumulation significantly improves network performance.

Abstract

We investigate optimal routing and scheduling strategies for multi-hop wireless networks with rateless codes. Rateless codes allow each node of the network to accumulate mutual information from every packet transmission. This enables a significant performance gain over conventional shortest path routing. Further, it outperforms cooperative communication techniques that are based on energy accumulation. However, it requires complex and combinatorial networking decisions concerning which nodes participate in transmission, and which decode ordering to use. We formulate three problems of interest in this setting: (i) minimum delay routing, (ii) minimum energy routing subject to delay constraint, and (iii) minimum delay broadcast. All of these are hard combinatorial optimization problems and we make use of several structural properties of their optimal solutions to simplify the problems and derive optimal greedy algorithms. Although the reduced problems still have exponential complexity, unlike prior works on such problems, our greedy algorithms are simple to use and do not require solving any linear programs. Further, using the insight obtained from the optimal solution to a line network, we propose two simple heuristics that can be implemented in polynomial time and in a distributed fashion and compare them with the optimal solution. Simulations suggest that both heuristics perform very close to the optimal solution over random network topologies.