Optimal Backpressure Scheduling in Wireless Networks using Mutual Information Accumulation

TL;DR

This paper develops new scheduling algorithms for wireless networks that leverage mutual information accumulation to expand the stability region, addressing the challenges of non-i.i.d link decoding processes.

Contribution

It introduces two dynamic scheduling algorithms that handle non-i.i.d information accumulation, achieving optimal stability regions in wireless networks.

Findings

The first algorithm approaches the stability boundary as T increases.

The second algorithm uses virtual queues to match the stability region of the original system.

Simulation results demonstrate the effectiveness of the proposed algorithms.

Abstract

In this paper we develop scheduling policies that maximize the stability region of a wireless network under the assumption that mutual information accumulation is implemented at the physical layer. When the link quality between nodes is not sufficiently high that a packet can be decoded within a single slot, the system can accumulate information across multiple slots, eventually decoding the packet. The result is an expanded stability region. The accumulation process over weak links is temporally coupled and therefore does not satisfy the independent and identically distributed (i.i.d) assumption that underlies many previous analysis in this area. Therefore the problem setting also poses new analytic challenges. We propose two dynamic scheduling algorithms to cope with the non-i.i.d nature of the decoding. The first performs scheduling every $T$ slots, and approaches the boundary of the stability region as $T$ gets large, but at the cost of increased average delay. The second introduces virtual queues for each link and constructs a virtual system wherein two virtual nodes are introduced for each link. The constructed virtual system is shown to have the same stability region as the original system. Through controlling the virtual queues in the constructed system, we avoid the non-i.i.d analysis difficulty and attain the full stability region. We derive performance bounds for both algorithms and compare them through simulation results.