Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks

TL;DR

This paper introduces two estimators for primary channel gain in cognitive radio networks, enabling spectrum sharing by allowing a cognitive transmitter to sense primary signals, with analysis of their accuracy and complexity.

Contribution

It proposes a maximum likelihood and a median-based estimator for primary channel gain, addressing the challenge of estimating this gain at the cognitive transmitter.

Findings

ML estimator has high accuracy but high computational complexity.

MB estimator offers low complexity with slightly lower accuracy.

Estimation errors can be as small as 0.6 dB (ML) and 0.7 dB (MB).

Abstract

In cognitive radio networks, the channel gain between primary transceivers, namely, primary channel gain, is crucial for a cognitive transmitter (CT) to control the transmit power and achieve spectrum sharing. Conventionally, the primary channel gain is estimated in the primary system and thus unavailable at the CT. To deal with this issue, two estimators are proposed by enabling the CT to sense primary signals. In particular, by adopting the maximum likelihood (ML) criterion to analyze the received primary signals, a ML estimator is first developed. After demonstrating the high computational complexity of the ML estimator, a median based (MB) estimator with proved low complexity is then proposed. Furthermore, the estimation accuracy of the MB estimation is theoretically characterized. By comparing the ML estimator and the MB estimator from the aspects of the computational complexity as well as the estimation accuracy, both advantages and disadvantages of two estimators are revealed. Numerical results show that the estimation errors of the ML estimator and the MB estimator can be as small as $0.6$ dB and $0.7$ dB, respectively.