Cognitive Radio Transmission Strategies for Primary Markovian Channels

TL;DR

This paper develops optimal transmission strategies for cognitive radios that leverage primary ARQ feedback to infer channel states, enabling better coexistence with primary users in Markovian channel environments.

Contribution

It introduces a novel approach using primary feedback to inform cognitive radio transmission strategies, including models with multiple states and channels, and considers learning unknown parameters with HMMs.

Findings

Optimal strategies improve primary-secondary throughput trade-offs.

Numerical results show advantages over greedy algorithms.

Strategies adapt to unknown parameters using HMMs.

Abstract

A fundamental problem in cognitive radio systems is that the cognitive radio is ignorant of the primary channel state and, hence, of the amount of actual harm it inflicts on the primary license holder. Sensing the primary transmitter does not help in this regard. To tackle this issue, we assume in this paper that the cognitive user can eavesdrop on the ACK/NACK Automatic Repeat reQuest (ARQ) fed back from the primary receiver to the primary transmitter. Assuming a primary channel state that follows 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 begin by formulating the problem for a channel with a general number of states. We then 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 extend our two-state model to a two-channel case, where the secondary user can decide to transmit on a particular channel or not to transmit at all. We provide numerical results for our optimal strategies and compare them with simple greedy algorithms for a range of primary channel parameters. Finally, we investigate the case where some of the parameters are unknown and are learned using hidden Markov models (HMM).