Robust And Optimal Opportunistic Scheduling For Downlink 2-Flow Network Coding With Varying Channel Quality and Rate Adaptation (New Simulation Figures)

TL;DR

This paper presents a novel opportunistic network coding and scheduling scheme for downlink 2-flow networks that achieves optimal throughput and stability under time-varying channels and rate adaptation, improving upon existing methods.

Contribution

It introduces a new binary INC operation and a queue-length-based scheduling scheme that are provably optimal for time-varying channels with rate adaptation.

Findings

Achieves the optimal stability region matching Shannon capacity.

Demonstrates optimal throughput with rate adaptation in simulations.

Provides a scheduling scheme for stochastic processing networks with random departure.

Abstract

This paper considers the downlink traffic from a base station to two different clients. When assuming infinite backlog, it is known that inter-session network coding (INC) can significantly increase the throughput. However, the corresponding scheduling solution (when assuming dynamic arrivals instead and requiring bounded delay) is still nascent. For the 2-flow downlink scenario, we propose the first opportunistic INC + scheduling solution that is provably optimal for time-varying channels, i.e., the corresponding stability region matches the optimal Shannon capacity. Specifically, we first introduce a new binary INC operation, which is distinctly different from the traditional wisdom of XORing two overheard packets. We then develop a queue-length-based scheduling scheme and prove that it, with the help of the new INC operation, achieves the optimal stability region with time-varying channel quality. The proposed algorithm is later generalized to include the capability of rate adaptation. Simulation results show that it again achieves the optimal throughput with rate adaptation. A byproduct of our results is a scheduling scheme for stochastic processing networks (SPNs) with random departure, which relaxes the of deterministic departure in the existing results.