Linear Precoding for Fading Cognitive Multiple-Access Wiretap Channel With Finite-Alphabet Inputs

• Published in: IEEE transactions on vehicular technology Vol. 66; no. 4; pp. 3059 - 3070
• Main Authors: Jin, Juening, Xiao, Chengshan, Tao, Meixia, Chen, Wen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation; Approximation algorithms; Codes; Cognitive multiple-access wiretap channel (CMAC-WT); Fading; Fading channels; finite-alphabet inputs; Interference; linear precoding; Mathematical analysis; multiple-input multiple-output (MIMO); Optimization; physical-layer security; Polynomials; Precoding; Receivers; Secrecy aspects; Security; statistical channel state information (CSI); Transmitters; Wiretapping
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2016.2590539

We investigate the fading cognitive multiple-access wiretap channel (CMAC-WT), in which two secondary-user transmitters (STs) send secure messages to a secondary-user receiver (SR) in the presence of an eavesdropper and subject to interference threshold constraints at multiple primary-user receivers (PRs). We design linear precoders to maximize the average secrecy sum rate for a multiple-input-multiple-output (MIMO) fading CMAC-WT under finite-alphabet inputs and statistical channel state information at STs. For this nondeterministic polynomial-time NP-hard problem, we utilize an accurate approximation of the average secrecy sum rate to reduce the computational complexity and then present a two-layer algorithm by embedding the convex-concave procedure into an outer-approximation framework. The idea behind this algorithm is to reformulate the approximated average secrecy sum rate as a difference of convex functions and then generate a sequence of simpler relaxed sets to approach the nonconvex feasible set. Subsequently, we maximize the approximated average secrecy sum rate over the sequence of relaxed sets by using the convex-concave procedure. Numerical results indicate that our proposed precoding algorithm is superior to the conventional Gaussian precoding method in the medium and high signal-to-noise ratio (SNR) regimes.