Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks

Zhang, L., Zhou, W., Zhao, G. , Wu, G. and Chen, Z. (2017) Primary Channel Gain Estimation for Spectrum Sharing in Cognitive Radio Networks. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, 19-22 Mar 2017, ISBN 9781509041831 (doi: 10.1109/WCNC.2017.7925693)

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 realize spectrum sharing. To obtain the primary channel gain, a backhaul between the primary system and the CT is needed. However, the backhaul is usually unavailable in practice. To deal with this issue, the CT is enabled to sense primary signals and estimate the primary channel gain in this paper. In particular, two estimators, namely, a high-complexity maximum likelihood (ML) estimator and a low-complexity median based (MB) estimator are proposed. Numerical results show that the ML estimator outperforms the MB estimator in terms of the accuracy if the signal to noise ratio (SNR) of the received primary signals at the CT is no smaller than 4 dB. Otherwise, the MB estimator is superior to the ML estimator from the aspects of both the computational complexity and accuracy.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Guodong
• Authors: Zhang, L., Zhou, W., Zhao, G., Wu, G., and Chen, Z.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISSN: 1558-2612
• ISBN: 9781509041831