A Matrix-Factorization-Error-Ratio Approach to Cooperative Sensing in Non-Ideal Communication Environment

A fundamental challenge in cognitive radio is the detection of primary users in a licensed spectrum. Cooperative sensing, which utilizes multiple receivers distributed across different locations, offers the advantage of utilizing multiple antennas and achieving spatial diversity gain. However, successful implementation of cooperative sensing relies on the ideal exchange of information among cooperating receivers, which may not always be feasible in real-world scenarios. In this paper, we consider the cooperative sensing problem in a non-ideal communication scenario, where the raw data broadcasted from a receiving node can be received by only a subset of the nearby nodes. Existing multiantenna detectors can not deal with such a scenario. To tackle this issue, we propose a novel cooperative sensing scheme, where each node sends only its local correlation coefficients to the fusion center. A detection mechanism based on factorizing the partially received sample covariance matrix is developed. To achieve fast convergence and avoid exhaustive step size tuning, a Bregman proximal method, based on an alternating minimization algorithm (with convergence guarantees), is also developed. The advantages of our proposed cooperative scheme is demonstrated through numerical simulations.