Efficient Real-Time Whitening for Blind Eigenvalue-Based Detection in mmWave Full Duplex Cognitive Radio

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS(2023)

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摘要
Although combining Full Duplex (FD) and Cognitive Radio (CR) technologies can provide several benefits, the presence of the residual self-interference (RSI) at the FD transceiver may influence the spectrum sensing capabilities. In particular, focusing on the blind eigenvalue-based detectors (EVDs), this paper shows that operating at mmWave widebands, the RSI is a colored noise strongly affecting the detection performance even when it is characterized by low power. Consequently, we propose here a whitened detector whose novelty consists in the real-time computation of the whitening matrix, differently from previous works which employ an offline computation. The proposed whitening matrix computation uses a sample covariance matrix (SCM) regularization tailored for the FD scenario, instead of the conventional SCM estimator. Particularly, we prove that the proposed whitened detector is robust against RSI power uncertainty, attaining a constant false alarm rate under typical FD operating conditions. Finally, we show that our method provides computational load reduction and faster convergence to the theoretical false alarm probability characteristic of the white noise case. Numerical simulations validate the theoretical analysis confirming the better performance of the proposed whitening approach, particularly when applied to the sphericity test, in comparison to the offline-based approach.
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关键词
Cognitive radio,spectrum sensing,full duplex,noise correlation,sphericity test
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