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Link-Sharing Backpressure Routing In Wireless Multi-Hop Networks

Zhongyuan Zhao, Yujun Ming, Ananthram Swami, Kevin Chan, Fikadu Dagefu, Santiago Segarra

TL;DR

Backpressure routing achieves throughput stability but suffers from the last-packet problem due to exclusive per-link commodity selection in SP-BP. The authors revisit Lyapunov-drift theory and replace exclusive selection with MaxU link-sharing, integrating a four-step multi-commodity allocation and MWIS-based scheduling to preserve throughput while reducing LPP. The proposed MaxU SP-BP yields slight throughput gains and substantial latency reductions under mixed streaming and bursty traffic in large random networks, expanding the performance envelope of SP-BP. This approach provides a distributed, scalable improvement for BP-based routing in modern wireless networks with diverse traffic and high-bandwidth interfaces.

Abstract

Backpressure (BP) routing and scheduling is an established resource allocation method for wireless multi-hop networks, noted for its fully distributed operation and maximum queue stability. Recent advances in shortest path-biased BP routing (SP-BP) mitigate shortcomings such as slow startup and random walks, yet exclusive link-level commodity selection still causes last-packet problem and bandwidth underutilization. By revisiting the Lyapunov drift theory underlying BP, we show that the legacy exclusive commodity selection is unnecessary, and propose a Maximum Utility (MaxU) link-sharing method to expand its performance envelope without increasing control message overhead. Numerical results show that MaxU SP-BP substantially mitigates the last-packet problem and slightly expands the network capacity region.

Link-Sharing Backpressure Routing In Wireless Multi-Hop Networks

TL;DR

Backpressure routing achieves throughput stability but suffers from the last-packet problem due to exclusive per-link commodity selection in SP-BP. The authors revisit Lyapunov-drift theory and replace exclusive selection with MaxU link-sharing, integrating a four-step multi-commodity allocation and MWIS-based scheduling to preserve throughput while reducing LPP. The proposed MaxU SP-BP yields slight throughput gains and substantial latency reductions under mixed streaming and bursty traffic in large random networks, expanding the performance envelope of SP-BP. This approach provides a distributed, scalable improvement for BP-based routing in modern wireless networks with diverse traffic and high-bandwidth interfaces.

Abstract

Backpressure (BP) routing and scheduling is an established resource allocation method for wireless multi-hop networks, noted for its fully distributed operation and maximum queue stability. Recent advances in shortest path-biased BP routing (SP-BP) mitigate shortcomings such as slow startup and random walks, yet exclusive link-level commodity selection still causes last-packet problem and bandwidth underutilization. By revisiting the Lyapunov drift theory underlying BP, we show that the legacy exclusive commodity selection is unnecessary, and propose a Maximum Utility (MaxU) link-sharing method to expand its performance envelope without increasing control message overhead. Numerical results show that MaxU SP-BP substantially mitigates the last-packet problem and slightly expands the network capacity region.

Paper Structure

This paper contains 6 sections, 1 theorem, 16 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Maximum Utility (MaxU) SP-BP
  4. Numerical experiments
  5. Conclusions
  6. Acknowledgment

Key Result

Theorem 1

With everything else equal, MaxU SP-BP does not shrink the network capacity region of classic SP-BP.

Figures (3)

  • Figure 1: Graph modeling for wireless multi-hop networks
  • Figure 2: A mini example of commodity selection and link utility calculation with three devices and three commodities. $B_{i}^{(c)}=0$.
  • Figure 3: (a) Average throughput per flow versus flow rate $\lambda$, where all flows are streaming at identical flow rate; (b) Average composite latency (Latency $\times$ delivery ratio + $T$($1-$ delivery ratio)) by flow rate $\lambda$ under mixed traffic. Both (a) and (b) are on random networks of 100 nodes. (c) Average composite latency in networks of 20-100 nodes under mixed traffic. $T=1000$. The bands indicate $95\%$ confidence interval.

Theorems & Definitions (2)

  • Theorem 1
  • proof