Almost Optimal Channel Access in Multi-Hop Networks With Unknown Channel Variables

TL;DR

This paper introduces a distributed algorithm for multi-hop cognitive radio networks that efficiently learns channel access strategies, achieving near-optimal throughput with manageable complexity in complex network settings.

Contribution

It formulates the channel access problem as a combinatorial multi-armed bandit with unknown weights and proposes a scalable distributed solution with theoretical performance guarantees.

Findings

Achieves 1/ρ of the optimal throughput.

Communication complexity is O(r^2+D).

Time complexity is O(d^{ρ^r}) for random networks.

Abstract

We consider distributed channel access in multi-hop cognitive radio networks. Previous works on opportunistic channel access using multi-armed bandits (MAB) mainly focus on single-hop networks that assume complete conflicts among all secondary users. In the multi-hop multi-channel network settings studied here, there is more general competition among different communication pairs. We formulate the problem as a linearly combinatorial MAB problem that involves a maximum weighted independent set (MWIS) problem with unknown weights which need to learn. Existing methods for MAB where each of $N$ nodes chooses from $M$ channels have exponential time and space complexity $O(M^N)$, and poor theoretical guarantee on throughput performance. We propose a distributed channel access algorithm that can achieve $1/\rho$ of the optimum averaged throughput where each node has communication complexity $O(r^2+D)$ and space complexity $O(m)$ in the learning process, and time complexity $O(D m^{\rho^r})$ in strategy decision process for an arbitrary wireless network. Here $\rho=1+\epsilon$ is the approximation ratio to MWIS for a local $r$-hop network with $m<N$ nodes,and $D$ is the number of mini-rounds inside each round of strategy decision. For randomly located networks with an average degree $d$, the time complexity is $O(d^{\rho^r})$.