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.
Highlights
The authors in 17 first developed wireless power transfer techniques for FD MISO Cloud radio access networks (CRANs) systems and, proposed an optimal approach for minimizing transmit power (TP) with the constraints of both UL, DL rate qualities and minimum amount of harvested energy
History Received: 17-3-2020 Accepted: 4-12-2020 Published: 20-12-2020 DOI :10.32508/stdjet.v3i3.685Copyright © VNU-HCM Press
We assume that the same transmit power (TP) constraints, i.e., PiDL = PDL, i ∈ SRU are imposed on radio units (RUs) and UL users, and PiUL = PUL = 0.7 × PDL with k ∈ SUL
Summary
The authors in 17 first developed wireless power transfer techniques for FD MISO CRAN systems and, proposed an optimal approach for minimizing TP with the constraints of both UL, DL rate qualities and minimum amount of harvested energy. They developed the optimal solution for both data sharing and data compressing MISO CRANs. the work in 27 investigated the EE problem in DL MIMO CRANs by jointly designing precoding matrices and turning on/off RUs. The results shown that the system EE is significantly enhanced as compared to some benchmark schemes.
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