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Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems. [ABSTRACT FROM AUTHOR]

To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS technology is employed to architect a wireless and energy-efficient cognitive secure communication network assisted by IRS. To further optimize the overall energy harvesting of this network, we present a cognitive secure resource allocation scheme, aiming to maximize the system's total collected energy. This scheme carefully considers various constraints, including transmission power constraints for cognitive base stations, power constraints for jammer devices, interference limitations for all primary users, minimum security rate constraints for all cognitive Internet of Things (IoT) devices, and phase shift constraints for IRS. We establish a comprehensive hybrid cognitive secure resource allocation model, encompassing joint cognitive transmission beam design, jammer device transmission beam design, and phase shift design. Given the non-convex nature of the formulated problem and the intricate coupling relationships among variables, we devise an effective block coordinate descent (BCD) iterative algorithm. The realization of joint cognitive/jammer base station transmission beam design and phase shift design employs sophisticated techniques such as continuous convex approximation methods and semi-definite programming. Simulation results underscore the superior performance of the proposed scheme compared to existing resource allocation approaches, particularly in terms of total harvested energy and other critical metrics. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication networks and Internet of Things technology (IoT), higher requirements have been put forward for spectrum resource utilization and system performance. In order to further improve the utilization of spectrum resources and system performance, this paper proposes an intelligent reflecting surface (IRS)-assisted fair energy allocation algorithm for cognitive multiple-input single-output (MISO) wireless-powered networks. The goal of this paper is to maximize the minimum energy receiving power in the energy receiver, which is constrained by the signal-to-interference-plus-noise ratio (SINR) threshold of the information receiver in the secondary network, the maximum transmission power at the cognitive base station (CBS), and the interference power threshold of the secondary network on the main network. Due to the coupling between variables, this paper uses iterative optimization algorithms to optimize and solve different variables. That is, when solving the active beamforming variables, the passive beamforming variables are fixed; then, the obtained active beamforming variables are fixed, and the passive beamforming variables are solved. Through continuous iterative optimization, the system converges. The simulation results have verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Network energy resources are limited in communication systems, which may cause energy shortages in mobile devices at the user end. Active Reconfigurable Intelligent Surfaces (A-RIS) not only have phase modulation properties but also enhance the signal strength; thus, they are expected to solve the energy shortage problem experience at the user end in 6G communications. In this paper, a resource allocation algorithm for maximizing the sum of harvested energy is proposed for an active RIS-assisted Simultaneous Wireless Information and Power Transfer (SWIPT) system to solve the problem of low performance of harvested energy for users due to multiplicative fading. First, in the active RIS-assisted SWIPT system using a power splitting architecture to achieve information and energy co-transmission, the joint resource allocation problem is constructed with the objective function of maximizing the sum of the collected energy of all users, under the constraints of signal-to-noise ratio, active RIS and base station transmit power, and power splitting factors. Second, the considered non-convex problem can be turned into a standard convex problem by using alternating optimization, semi-definite relaxation, successive convex approximation, penalty function, etc., and then an alternating iterative algorithm for harvesting energy is proposed. The proposed algorithm splits the problem into two sub-problems and then performs iterative optimization separately, and then the whole is alternately optimized to obtain the optimal solution. Simulation results show that the proposed algorithm improves the performance by 45.2% and 103.7% compared to the passive RIS algorithm and the traditional without-RIS algorithm, respectively, at the maximum permissible transmitting power of 45 dBm at the base station. [ABSTRACT FROM AUTHOR]

Abstract In future wireless communication systems, combining unmanned aerial vehicles (UAVs) with massive MIMO technology is a promising solution to meet ever-increasing data demand due to it has advantages of high reliability, cost effective, and flexible deployment. In this paper, we study the achievable rate of UAV-based communication under Ricean fading, where the transceivers of UAV is equipped with circular antenna array (UCA) configuration. We exploit an effective statistical-eigenmode space division multiple-access transmission (SE-SDMA) scheme with only the knowledge of the CSI at the UAV's transceivers. An exact expression on the achievable rate of UAV-based communication systems with UCA configuration is derived, in which the transceivers utilizes the channel response vector as the beamforming precoder. Based on the derived theoretical results, we gain the relationships of the achievable SE and the system parameters, such that the achievable sum-rate tends to a saturation value in the high signal-to-noise ratio regime. In addition, we find that the high Ricean K -factor and the small values of inner product between different statistical channel vector are contribute to the achievable sum-rate of the UAV-based communication system. [ABSTRACT FROM AUTHOR]

Vehicle to everything (V2X) is emerging as a promising application scenario of next generation mobile communications. In V2X systems, a series of applications (information transmission, in-car entertainment, etc.) rely on the limited vehicle battery. However, most prior work limits to the automatic piloting, channel measurement/estimation and high speed transmissions, seldom study has been done on the battery-limited for V2X. In light of this, here we investigate the problem of the downlink beamforming design for signal-to-interference ratio balancing in a V2X-SWIPT environment with imperfectly estimated channels at the base station, where a practical non-linear energy harvesting model is considered. The problem is further transformed into a two-layer optimization problem, where we solve the inner-level problem by exploiting S-Procedure, and the outer-level problem through bisection approach. Finally, simulation results are carried out to confirm that the proposed robust beamforming design obtains a favorable result in applications of practical interest. [ABSTRACT FROM AUTHOR]

In this paper, we present two novel interference management stratagems for coexisting one primary user (PU) and multiple secondary users (SUs) by exploiting the unused spatial directions at PU. The cognitive stations sense their environment to determine the users they are interfering with, and adapt to it by designing the corresponding precoders using interference alignment (IA) in order to avoid causing performance degradation to nearby PU and SUs. The first proposed approach judiciously designs the set of precoders based on an improved version of minimum weighted leakage interference algorithm. However, there are still leftover interference signals in SUs’ desired signal space due to the limited iterative times in the first algorithm. To tackle this problem, another scheme combining the first one and power allocation method at the secondary stations is developed. Numerical results validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A hierarchy precoding approach is proposed in this study for multi‐cell multiuser systems with any number of base stations and that of users, which is suitable for any number of data streams. The key feature of this approach is aligning the inter‐user interferences within the same cell to the room spanned by the inter‐cell interferences, by which both the inter‐cell and inter‐user interferences are cancelled simultaneously. Then, the inter‐stream interference for each user can be easily tackled. It is found that the interference alignment‐based hierarchy precoder achieves to the full freedom of degree. With interference‐free transmissions achieved by the proposed precoder, the transmit power is optimised in an analytical expression by maximising the sum rate and minimising the sum weighted mean square error. Extensive simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this letter, we study energy-efficient precoder design in a three-node MIMO wiretap channel. Different from previous works in this area that mainly focus on secrecy rate maximization or power minimization problems, here, we aim to maximize the energy efficiency (EE) of the system subject to the secret rate and transmit power constraints, which is a nonconvex optimization and hard to tackle. To solve it, we first transform it into an equivalent parameterized polynomial subtractive form and then approximate the nonconvex equivalent problem into a convex optimization problem by means of Taylor series expansion. Based on that, an iterative algorithm with proved convergence is proposed, and this algorithm is also extended to an imperfect-channel-state-information case. Numerical results show that the proposed algorithm outperforms traditional design methods in terms of EE. [ABSTRACT FROM PUBLISHER]

Vehicle to everything (V2X) has been deemed a promising technology due to its potential to achieve traffic safety and efficiency. This paper considers a V2X downlink system with a simultaneous wireless information and power transfer (SWIPT) system where the base station not only conveys data and energy to two types of wireless vehicular receivers, such as one hybrid power-splitting vehicular receiver, and multiple energy vehicular receivers, but also prevents information from being intercepted by the potential eavesdroppers (idle energy vehicular receivers). Both the base station and the energy vehicular receivers are equipped with multiple antennas, whereas the information vehicular receiver is equipped with a single antenna. In particular, the imperfect channel state information (CSI) and the practical nonlinear energy harvesting (EH) model are taken into account. The non-convex optimization problem is formulated to maximize the minimum harvested energy power among the energy vehicular receivers satisfying the lowest harvested energy power threshold at the information vehicular receiver and secure vehicular communication requirements. In light of the intractability of the optimization problem, the semidefinite relaxation (SDR) technique and variable substitutions are applied, and the optimal solution is proven to be tight. A number of results demonstrate that the proposed robust secure beamforming scheme has better performance than other schemes. [ABSTRACT FROM AUTHOR]