Adaptive fusion in air: A cooperative sensing scheme based on distributed beamforming

Abstract

Cooperative spectrum sensing has been exploited to improve the detection reliability in cognitive radio networks. Likelihood ratio test (LRT) is optimal for distributed detection in cooperative sensing. However, it is not applicable in practice due to the lack of the local signal-to-noise ratio (SNR) and the fading effect of the control channels between the secondary users and fusion center. In this paper, an adaptive fusion in air (FAIR) cooperative spectrum sensing scheme based on distributed beamforming is proposed to solve this problem. First, a sequential estimation method by exploiting the global decision feedback is adopted at cognitive users to estimate the local SNR. Then a distributed beamforming scheme is introduced among cognitive users to transmit the local LRTs to maximize the received signal at fusion center. The transmission of the weighted sensing data is conducted in an analog way at the same time slot so that data fusion can be automatically carried out in air. Simulation results show that the performance of our proposed practical method is close to the theoretically optimal cooperative spectrum sensing. Meanwhile, higher spectrum efficiency is achieved due to data fusion in air.