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Open AccessCommunication

Distributed Joint Optimization of Beamforming and Power Allocation for Maximizing the Energy Efficiency of Cognitive Heterogeneous Networks

Department of Information and Communication Engineering, Dongguk University, Seoul 04620, Korea
Academic Editor: Jun-Pyo Hong
Sensors 2021, 21(9), 3186; https://doi.org/10.3390/s21093186
Received: 7 April 2021 / Revised: 26 April 2021 / Accepted: 1 May 2021 / Published: 4 May 2021
(This article belongs to the Special Issue Multi-Antenna Techniques for 5G and beyond 5G Communications)
This paper investigated an energy-efficient beamforming and power allocation strategy for cognitive heterogeneous networks with multiple-input-single-output interference channels. To maximize the sum energy efficiency of secondary users (SUs) while keeping the interference to primary networks under a predetermined threshold, I propose a distributed resource allocation algorithm using dual methods, in which each SU updates its beamforming vector and transmit power iteratively without any information sharing until convergence. The simulation results verify that the performance of the proposed scheme is comparable to that of the optimal scheme but with a much shorter computation time. View Full-Text
Keywords: cognitive heterogeneous networks; MISO interference channel; energy efficiency; joint optimization; distributed algorithm cognitive heterogeneous networks; MISO interference channel; energy efficiency; joint optimization; distributed algorithm
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MDPI and ACS Style

Lee, K. Distributed Joint Optimization of Beamforming and Power Allocation for Maximizing the Energy Efficiency of Cognitive Heterogeneous Networks. Sensors 2021, 21, 3186. https://doi.org/10.3390/s21093186

AMA Style

Lee K. Distributed Joint Optimization of Beamforming and Power Allocation for Maximizing the Energy Efficiency of Cognitive Heterogeneous Networks. Sensors. 2021; 21(9):3186. https://doi.org/10.3390/s21093186

Chicago/Turabian Style

Lee, Kisong. 2021. "Distributed Joint Optimization of Beamforming and Power Allocation for Maximizing the Energy Efficiency of Cognitive Heterogeneous Networks" Sensors 21, no. 9: 3186. https://doi.org/10.3390/s21093186

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