The paper focuses on designing precoding matrices in multi-cell multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer networks (SWIPT) where the sets of users are selected for data transmission in each time slot and the unselected users are dedicated to energy harvesting. The precoding design for the SWIPT problem is formulated as a general multi-objective maximization problem, in which the sum-rate (SR) and sum harvested energy (SHE) are maximized simultaneously under the transmit power constraints. Since the objective function of the maximization problem is not concave in the design matrix variables, it is difficult to directly obtain the optimal solutions. To tackle this challenge, we recast the SR function into one more amenable by applying the connection between the minimum mean square error and achievable data rate. In addition, to deal with the non-concavity of the harvested energy function, we derive its concave minorant. Then, we develop an efficient iterative algorithm based on alternating optimization (AO) to obtain the optimal precoders. We also analyze the convergence and computational complexity of the proposed algorithm. Finally, by numerical simulation results we investigate the trade-offs between the SR and SHE.
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.
This paper studies the problems of precoding designs to achieve the energy efficiency (EE) in the uplink heterogeneous networks in which the multiple small cells are deployed in a macro-cell. We consider two design problems which maximize either the total system energy efficiency (SEE) or the minimum energy efficiency (MinEE) among users subject to the transmit power constraints at each user and interference constraints caused to the macro base station. Since the optimization problems are non-convex fractional programming in matrix variables, it cannot be straightforward to obtain the optimal solutions. To tackle with the non-convexity challenges of the design problems, we adopt the relationships between the minimum mean square error (MMSE) and achievable data rate to recast the EE problems into ones more amenable. Then, we employ the block coordinate ascent (BCA) and the Dinkelbach methods to develop efficient iterative algorithms in which the closed form solutions are obtained or the semi-definite programming (SDP) problems are solved at each iteration. Simulation results are provided to investigate the EE performance of the EE optimization as compared to those of the spectral efficiency (SE) optimization.Index Terms-Heterogeneous networks (HetNets), energy efficiency, multiple-input-multiple-output (MIMO), precoding design.
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