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Energy Efficiency Maximization for Full Duplex MIMO Cloud Radio Access Networks

Tien Ngoc Ha 1, 2
Xuan-Xinh Nguyen 1, 2
Hoang Kha Ha 1, 2, *
  1. 1. Faculty of Electrical & Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
  2. 2. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
Correspondence to: Hoang Kha Ha, 1. Faculty of Electrical & Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam; 2. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam. Email: [email protected].
Volume & Issue: Vol. 3 No. 3 (2020) | Page No.: 488-499 | DOI: 10.32508/stdjet.v3i3.685
Published: 2020-12-20

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This article is published with open access by Viet Nam National University, Ho Chi Minh City, Viet Nam. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. 

Abstract

This paper studies a joint precoder and fronthaul compression design for full-duplex (FD) miltiple-input-multiple-output (MIMO) cloud radio access networks (CRANs). A cloud control unit (CU) communicates with multiple downlink and uplink users through FD radio units (RUs) connected to the CU through fronthaul links which are limited capacity. We address the energy efficiency (EE) maximization problem subject to the transmit power constraints at each RU, each user and the limited capacity of fronthaul links. Since the formulated design problem is a highly non-convex problem in design variables, we exploit a successive convex approximation (SCA) method to obtain the concave lower bound of the achievable sum rate and a convex upper bound of limited capacity fronthaul link functions. Then, we apply the Dinkelbach method to develop an efficient iterative algorithm guaranteeing convergence in which the convex optimization problems are solved. Numerical results are provided to investigate the EE of the proposed algorithm.

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