Passive Channel Gain Estimation Between Primary Transceivers in Cognitive Radio Networks

In cognitive radio networks, the channel gain between primary transceivers is crucial for a cognitive transmitter (CT) to control the transmit power and achieve the coexistence. To obtain the 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 the primary signals and estimate the channel gain between primary transceivers in this paper. In particular, two passive estimators, namely, a high-complexity maximum likelihood (ML) estimator and a low-complexity median based (MB) estimator, are proposed. 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. Besides, both advantages and disadvantages of the two estimators are analyzed in different scenarios.