Cognitive Radio Transmission Strategies for Primary Erasure Channels

TL;DR

This paper develops optimal cognitive radio transmission strategies that leverage primary ARQ feedback to improve secondary user throughput while protecting primary transmissions, using Markov models and dynamic programming.

Contribution

It introduces novel strategies for cognitive radios that utilize primary ARQ feedback and Markov channel models to optimize secondary transmission without harming primary performance.

Findings

Optimal strategies outperform greedy algorithms in simulations.

Strategies effectively balance primary protection and secondary throughput.

Markov channel models enable adaptive transmission decisions.

Abstract

A fundamental problem in cognitive radio systems is that the cognitive radio is ignorant of the primary channel state and the interference it inflicts on the primary license holder. In this paper we assume that the primary transmitter sends packets across an erasure channel and the primary receiver employs ACK/NAK feedback (ARQ) to retransmit erased packets. The cognitive radio can eavesdrop on the primary's ARQs. Assuming the primary channel states follow a Markov chain, this feedback gives the cognitive radio an indication of the primary link quality. Based on the ACK/NACK received, we devise optimal transmission strategies for the cognitive radio so as to maximize a weighted sum of primary and secondary throughput. The actual weight used during network operation is determined by the degree of protection afforded to the primary link. We study a two-state model where we characterize a scheme that spans the boundary of the primary-secondary rate region. Moreover, we study a three-state model where we derive the optimal strategy using dynamic programming. We also show via simulations that our optimal strategies achieve gains over the simple greedy algorithm for a range of primary channel parameters.