Harvested Energy and Spectral Efficiency Trade-offs in Multicell MIMO Wireless Networks

Abstract

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.

Energy‐Spectral Efficiency Trade‐Offs in Full‐Duplex MU‐MIMO Cloud‐RANs with SWIPT

“…More recently, the multiobjective optimization (MOO) framework has been introduced as an effective mathematical tool to overcome this issue as well as to provide a balance among the system performance metrics on designing the wireless communication systems [37,38]. As a result, the MOO framework was recently exploited in many studies to address various trade-offs between two or more system metrics [39][40][41][42][43][44][45][46][47][48]. Particularly, the harvested energy and information transfer trade-offs in SWIPT-enabled wireless system were studied either in cognitive FD MU-MIMO systems [39] or in MIMO broadcast channels [40,41].…”

“…As a result, the MOO framework was recently exploited in many studies to address various trade-offs between two or more system metrics [39][40][41][42][43][44][45][46][47][48]. Particularly, the harvested energy and information transfer trade-offs in SWIPT-enabled wireless system were studied either in cognitive FD MU-MIMO systems [39] or in MIMO broadcast channels [40,41]. In [42], the authors studied the optimal Pareto set for DL and UL transmit power fairness in the FD MISO networks.…”

Multi‐objective design of energy harvesting enabled wireless networks based on evolutionary genetic optimisation

“…Moreover, the implementation of the developed optimisation model [20 ] is compared with the standard multi‐objective genetic algorithm technique in accomplishing the substantial performance gain. An iterative multi‐objective algorithm is proposed in [21 ] to explore the trade‐off between spectrum efficiency and harvested energy for multi‐cell MIMO networks subject to the transmission power restraints.…”