Search

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]

In this paper, we study the secured access to the satellite–terrestrial networks of 6G with two directional traffics. Both access point (AP) and the sensor node in the terrestrial networks simultaneously transmit messages to each other. The passive reflection surface technique is employed to assist both directional traffics. Meanwhile, the satellite as the eavesdropper is keeping on receiving both messages by equipping multiple antennas on this illegal receiver. Specifically, we formulate the adaptive power design problem for both AP and the sensor node, respectively, as the transmitter of the downlink and the uplink traffic in terrestrial networks. The objective function is the energy efficiency maximization and the constraint is the maximum overall data rate of the illegal eavesdropper over the satellite networks. The closed form expression is mathematically derived for the optimum solution. The obtained approach achieves the best performance for the terrestrial networks from the energy efficiency point of view. Moreover, it assures any quality of service requirement concerning the security of the whole satellite–terrestrial networks of 6G. Our approach is numerically verified by the Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate green power control for user access in cloud-based cellular networks. The optimization problem is formulated as a non-convex and a non-linear mixed-integer fractional form considering the circuit power consumption. In order to solve the considered problem, we transform the considered optimization problem into a tractable subtractive form problem, where the solution can be obtained through three-stage optimization. The outer layer only refers one-dimension search for a parameter which can be obtained through bisection search. In the second stage, a non-fractional power control problem remains to solve, and we exploit a power update function to update the transmit power. The third stage performs user access. Simulation results indicate that our proposed algorithm archives better performance than the traditional algorithm. [ABSTRACT FROM AUTHOR]