The approach maximizes information transfer and energy transfer efficiency in MIMO systems, indicating trade-offs in resource allocation.
The transfer-efficient power allocation problem for downlink simultaneous wireless information and power transfer (SWIPT) is investigated in massive multiple-input multiple-output (MIMO) systems in this paper. In the considered system, the base station (BS) equipped with a large number of antennas simultaneously transmits information and sends energy signals to multiple information and energy terminals equipped with a single antenna. The aim is to maximize transfer efficiency while meeting quality-of-service (QoS) requirements for all terminals. First, the closed-form expressions of achievable rates for each information terminal and the harvested energy for each energy terminal are obtained. Then, two optimization problems are formulated according to the obtained expressions, with the purpose of maximizing information transfer efficiency (ITE) and energy transfer efficiency (ETE). The maximizations of ITE and ETE are fractional programming problems and are difficult to solve directly. For this reason, the iterative optimization algorithm is proposed to solve the ITE maximization problem by transforming it into a subtractive form and then utilizing a successive convex approximation (SCA) method. Following a similar approach, another iterative optimization algorithm is proposed to solve the ETE maximization problem by transforming it into a subtractive form and then utilizing a linear programming method. Finally, numerical results demonstrate that the two iterative optimization algorithms can achieve good ITE and ETE, and we also reveal the trade-off between them in this work.
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Sun et al. (2025) studied this question.
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