Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret

TL;DR

This paper introduces distributed algorithms for cognitive radio networks that efficiently learn channel availability and access channels with minimal regret, achieving near-optimal throughput without prior knowledge of user count.

Contribution

The paper proposes novel distributed learning and access policies that attain order-optimal logarithmic regret in cognitive networks, even when the number of secondary users is unknown.

Findings

Achieves order-optimal logarithmic regret when user count is known.

Proposes a policy with near-logarithmic regret when user count is unknown.

Demonstrates policies that maximize successful transmissions in distributed settings.

Abstract

The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement among the secondary users. We propose policies for distributed learning and access which achieve order-optimal cognitive system throughput (number of successful secondary transmissions) under self play, i.e., when implemented at all the secondary users. Equivalently, our policies minimize the regret in distributed learning and access. We first consider the scenario when the number of secondary users is known to the policy, and prove that the total regret is logarithmic in the number of transmission slots. Our distributed learning and access policy achieves order-optimal regret by comparing to an asymptotic lower bound for regret under any uniformly-good learning and access policy. We then consider the case when the number of secondary users is fixed but unknown, and is estimated through feedback. We propose a policy in this scenario whose asymptotic sum regret which grows slightly faster than logarithmic in the number of transmission slots.