Distributed Learning and Multiaccess of On-Off Channels

TL;DR

This paper introduces a distributed learning policy called ASA for multiple users accessing on-off channels, achieving near-optimal performance with finite regret compared to centralized schemes.

Contribution

The paper proposes the ASA policy for distributed channel access, demonstrating finite expected regret and improved efficiency over existing methods.

Findings

ASA achieves finite expected regret compared to optimal centralized schemes.

Channels modeled as independent alternating renewal processes.

Distributed learning policy effectively manages multiaccess on on-off channels.

Abstract

The problem of distributed access of a set of N on-off channels by K<N users is considered. The channels are slotted and modeled as independent but not necessarily identical alternating renewal processes. Each user decides to either observe or transmit at the beginning of every slot. A transmission is successful only if the channel is at the on state and there is only one user transmitting. When a user observes, it identifies whether a transmission would have been successful had it decided to transmit. A distributed learning and access policy referred to as alternating sensing and access (ASA) is proposed. It is shown that ASA has finite expected regret when compared with the optimal centralized scheme with fixed channel allocation.