Conventional antenna arrays rely primarily on digital beamforming for spatial control. While adding more elements can narrow beamwidth and suppress interference, such scaling incurs prohibitive hardware and power costs. Rotatable antennas (RAs), which allow mechanical or electronic adjustment of element orientations, introduce a new degree of freedom to exploit spatial flexibility without enlarging the array. By dynamically optimizing orientations, RAs can substantially improve desired link alignment and interference suppression. This paper investigates RA-enabled multiple-input single-output (MISO) interference channels under co-channel spectrum sharing and formulates a weighted sum-rate maximization problem that jointly optimizes transmit beamforming and antenna orientations. To tackle this nonconvex problem, we develop an alternating optimization (AO) framework that integrates weighted minimum mean-square error (WMMSE)-based beamforming with Frank-Wolfe-based orientation updates. To reduce complexity, we further study orientation optimization under maximum-ratio transmission (MRT) and zero-forcing (ZF) beamforming schemes. For finite-resolution actuators, we construct spherical Fibonacci codebooks and design a cross-entropy method (CEM)-based algorithm for discrete orientation selection. Simulations show that integrating RAs with conventional beamforming markedly increases weighted sum-rate, with gains rising with element directivity. Under discrete orientation control, the proposed CEM algorithm consistently outperforms the nearest-projection baseline.

With the rapid proliferation of large-scale sensor nodes and smart devices, the energy consumption of wireless networks has increased dramatically, posing significant challenges to the design of energy-efficient communication systems. Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged as a promising technology for enhancing energy efficiency, owing to its capability of reconfiguring the wireless propagation environment and providing full-space user coverage. In this paper, we investigate the application of STAR-RIS in a simultaneous wireless information and power transfer (SWIPT) system, leveraging the spatial beamforming capabilities of multiple-input multiple-output (MIMO) and the frequency diversity gain of orthogonal frequency-division multiplexing (OFDM). In specific, we aim to maximize the system’s weighted energy efficiency, subject to individual users’ energy harvesting and achievable data rate requirements. To address the inherent non-convexity of the formulated problem, we adopt a weighted minimum mean square error (WMMSE)-based reformulation, and develop an efficient algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP). Simulation results validate the performance advantages of the proposed STAR-RIS-aided design over conventional RIS. Furthermore, user-specific weight adjustment enables flexible and fair resource allocation across multiple users.

This paper proposes a unified optimization framework for a power splitting (PS)-based multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system with Tomlinson-Harashima pre coding (THP)-based non-linear transceivers or linear transceivers. Our aim is to minimize the transmit power under the sum mean-square-error (MSE) and energy harvesting (EH) constraints. To solve this formulated non-convex problem, we propose a structural solution (SS) which applies the closed-form expressions of equalization matrices, feedback matrices and precoding matrices to establish an equivalent optimization problem in terms of the power allocation and PS ratio. Then the equivalent problem is solved by a two-layer optimization scheme. Simulation results show that the THP-based non-linear transceivers need less transmit power than linear transceivers to achieve the same performance of EH and sum MSE.

The propagation loss of RF signals is a significant issue in simultaneous wireless information and power transfer (SWIPT) systems. Additionally, ensuring information security is crucial due to the broadcasting nature of wireless channels. To address these challenges, we exploit the potential of active intelligent reflecting surface (IRS) in a multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system. The active IRS provides better beamforming gain than the passive IRS, reducing the “double-fading” effect. Moreover, the noise introduced at the active IRS can be used as artificial noise (AN) to jam eavesdroppers. This paper formulates a secrecy sum-rate maximization problem related to precoding matrices, power splitting (PS) ratios, and the IRS matrix. Since the problem is highly non-convex, we propose a block coordinate descent (BCD)-based algorithm to find a sub-optimal solution. Moreover, we develop a heuristic algorithm based on the zero-forcing precoding scheme to reduce computational complexity. Simulation results show that the active IRS achieves a higher secrecy sum rate than the passive and non-IRS systems, especially when the transmit power is low or the direct link is blocked. Moreover, increasing the power budget at the active IRS can significantly improve the secrecy sum rate.

Simultaneous wireless information and power transfer (SWIPT) has emerged as an appealing solution to prolonging the lifetime of low-power Internet of Things (IoT) networks. Meanwhile, an intelligent reflecting surface (IRS) can reconstruct a favorable wireless propagation environment for IoT terminals to achieve high spectrum and energy efficiencies. To take full advantage of these two technologies, this article studies the optimization of an IRS-assisted multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system with nonlinear energy harvesting (EH) model. In particular, we aim to maximize the achievable data rate by jointly designing the transmit precoding matrices, the IRS matrix, as well as the power splitting (PS) ratio subject to the transmit power and harvested power constraints. Since the formulated problem is highly nonconvex, we develop an alternating optimization (AO)-based algorithm to find a high-quality suboptimal solution. Moreover, we further design a heuristic algorithm based on a two-stage optimization strategy to reduce the computational complexity. Simulation results verify that the proposed AO-based algorithm can significantly improve the achievable data rate compared to conventional benchmarks, and the proposed heuristic low-complexity algorithm can achieve comparable performance to the AO-based algorithm.

This paper studies the joint transceiver design and receive power splitting (PS) optimization for a multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. We aim to maximize the harvested power at the receiver under a sum mean-square error (MSE) constraint and a total transmit power constraint. By first deriving the linear minimum MSE equalization matrices, we obtain a non-convex optimization problem involving the transmit precoding matrices and the receive PS ratio. For a given PS ratio, we show that the problem can be recast as a semidefinite programming (SDP) problem, which can be solved with the interior point algorithm. Then, by employing the unimodal property of the objective function with respect to the PS ratio, we propose a Golden-section search method to find the optimal PS ratio efficiently. Further, to reduce the complexity of solving the inner SDP problems, we exploit the optimal structure of the precoding matrices to transform the original matrix-based optimization problem into a scalar-based optimization problem, for which the closed-form solution is obtained through convex optimization techniques. Simulations are provided to validate the superior performance of our proposed solutions.

This paper studies the joint transceiver and power splitting (PS) ratio design for a multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. We present a unified optimization framework based on the minimization of a general mean square error (MSE) objective function, which includes the most commonly used criteria, such as arithmetic MSE, geometric MSE and maximum MSE minimizations. The optimal equalization matrices are first derived. Then we propose a two-layer scheme to jointly optimize the precoding matrices and the PS ratio. In the inner layer, a structural solution for the precoding matrices is derived based on the Schur-convexity/concavity of the objective function, with which the precoding optimization problems are solved by different closed-form power allocations. In the outer layer, we show that the optimized objective functions obtained by the inner-layer optimization are unimodal with respect to the PS ratio. This enables us to find the optimal PS ratio very efficiently by exploiting the Golden-section search. Simulations are provided to compare the achievable rate and error rate performances of the proposed transceiver schemes.