Resource Management for Maximizing the Secure Sum Rate in Dense Millimeter-Wave Networks

• Main Authors: Yanping Liu, Xuming Fang, Ming Xiao
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Millimeter-wave (mmWave) communication; resource management; game theory; secure sum rate
• Online Access: https://ieeexplore.ieee.org/document/9178814/

Due to the densifying of users and the reflections caused by small-scale objects, secure communication is an important research problem in dense millimeter-wave (mmWave) networks. In this paper, we investigate the resource management problem of joint transmission reception point (TRP) selection, power control and beamwidth selection for maximizing the secure sum rate in the dense mmWave network, while considering beamforming training overheads, blockage effect in mmWave communications, and imperfect channel state information (CSI) from a legitimate user equipment (LUE) to the corresponding malicious user equipment (MUE). To handle the problem with low complexity, we first break it down into two subproblems (i.e., TRP selection and joint power control and beamwidth allocation), and propose a two-stage game based decentralized resource management approach to solve them iteratively, where in the first stage we propose a matching game based distributed TRP selection algorithm to solve the first subproblem in which novel utilities for both LUEs and TRPs are designed to handle the high directivity transmission problem, and in the second stage we propose a weekly acyclic game based two-dimensional-strategy concurrent better response algorithm for the second subproblem to deal with the huge strategy space, which has been proved to converge to an invariant two-dimensional-strategy Nash equilibrium (NE). Moreover, to adapt to the three-dimensional optimization variables and the huge strategy space of the considered problem, we also propose a new three-dimensional-strategy iterative weekly acyclic game to solve it, where the three-dimensional optimization variables are optimized alternately during the decision-making process. Finally, extensive simulations are conducted to verify the effectiveness of the proposed schemes.