Global Optimization of Precoder for Multi-Antenna Secure Cognitive Radios with Finite-Alphabet Inputs and Statistical CSI
This paper considers the precoder optimization for secure cognitive radios. Different from existing works, we consider multiple antennas at each node, instead of assuming only particular node/nodes having multiple antennas; we use finite-alphabet inputs as the signaling, instead of ideal Gaussian-input assumption; we exploit statistical channel state information (CSI), instead of instantaneous CSI at the transmitter. We maximize the secrecy rate of the secondary user, while controlling the transmit power and the power leakage to primary receivers that share the same frequency spectrum. We reformulate the precoder design problem and propose a branch-and-bound based algorithm, which provides a solution asymptotically converging to the global maximum of the secrecy rate. We illustrate the tradeoff between the performance and complexity of the proposed algorithm and demonstrate the performance gains comparing with other approaches.
W. Zeng et al., "Global Optimization of Precoder for Multi-Antenna Secure Cognitive Radios with Finite-Alphabet Inputs and Statistical CSI," Proceedings of the 2016 IEEE International Conference on Communications (2016, Kuala Lumpur, Malaysia), Institute of Electrical and Electronics Engineers (IEEE), May 2016.
The definitive version is available at https://doi.org/10.1109/ICC.2016.7511633
2016 IEEE International Conference on Communications (ICC) (2016: May 22-27, Kuala Lumpur, Malaysia)
Electrical and Computer Engineering
Keywords and Phrases
Antennas; Cognitive radio; Communication channels (information theory); Global optimization; Optimization; Finite-alphabet inputs; Frequency spectra; Gaussian inputs; Multiple antenna; Performance Gain; Precoder optimizations; Secondary users; Statistical channel state informations; Channel state information
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Article - Conference proceedings
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