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Article

Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission

by 1,*, 1, 1, 1, 1,2,3 and 1,3
1
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2
Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3
Purple Mountain Laboratories, Nanjing 211100, China
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(10), 1145; https://doi.org/10.3390/e22101145
Received: 16 September 2020 / Revised: 9 October 2020 / Accepted: 10 October 2020 / Published: 12 October 2020
(This article belongs to the Special Issue Information Theoretic Security and Privacy of Information Systems)
Herein, we focus on energy efficiency optimization for massive multiple-input multiple-output (MIMO) downlink secure multicast transmission exploiting statistical channel state information (CSI). Privacy engineering in the field of communication is a hot issue under study. The common signal transmitted by the base station is multicast transmitted to multiple legitimate user terminals in our system, but an eavesdropper might eavesdrop this signal. To achieve the energy efficiency utility–privacy trade-off of multicast transmission, we set up the problem of maximizing the energy efficiency which is defined as the ratio of the secure transmit rate to the power consumption. To simplify the formulated nonconvex problem, we use a lower bound of the secure multicast rate as the molecule of the design objective. We then obtain the eigenvector of the optimal transmit covariance matrix into a closed-form, simplifying the matrix-valued multicast transmission strategy problem into a power allocation problem in the beam domain. By utilizing the Minorize-Maximize method, an iterative algorithm is proposed to decompose the secure energy efficiency optimization problem into a sequence of iterative fractional programming subproblems. By using Dinkelbach’s transform, each subproblem becomes an iterative problem with the concave objective function, and it can be solved by classical convex optimization. We guarantee the convergence of the two-level iterative algorithm that we propose. Besides, we reduce the computational complexity of the algorithm by substituting the design objective with its deterministic equivalent. The numerical results show that the approach we propose performs well compared with the conventional methods. View Full-Text
Keywords: energy efficiency optimization; massive MIMO; privacy engineering; utility–privacy trade-off; statistical CSI; beam domain power allocation energy efficiency optimization; massive MIMO; privacy engineering; utility–privacy trade-off; statistical CSI; beam domain power allocation
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MDPI and ACS Style

Jiang, B.; Qu, L.; Huang, Y.; Zheng, Y.; You, L.; Wang, W. Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission. Entropy 2020, 22, 1145. https://doi.org/10.3390/e22101145

AMA Style

Jiang B, Qu L, Huang Y, Zheng Y, You L, Wang W. Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission. Entropy. 2020; 22(10):1145. https://doi.org/10.3390/e22101145

Chicago/Turabian Style

Jiang, Bin, Linbo Qu, Yufei Huang, Yifei Zheng, Li You, and Wenjin Wang. 2020. "Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission" Entropy 22, no. 10: 1145. https://doi.org/10.3390/e22101145

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