Energy efficiency is regards as an important problem in energy-limited wireless medical sensor networks owing to the energy shortage. On the other hand, full-duplex is considered as the promising technology for the future wireless consumer medical sensor networks that provides higher transmission rate. In this paper, we discussed a secure energy-efficient beamformer design that was impaired by channel state information (CSI) for two-way multi-input multi-output (MIMO) wireless medical sensor networks in smart healthcare, enabling simultaneous wireless data and power communication. We consider the norm-bounded channel error model and pose a new worse case maximization problem in terms of secure energy efficiency, aiming to promote the secure rate and reduce the networks power consumption, subject to meeting several reality constraints such as the full-duplex transmitter’s power constraint and the minimum harvested energy constrains. To handle the non-convex problem, we firstly examine the relationship between rate and weighted mean square error (WMSE), then utilize the sign-definiteness and S-procedure lemma to eliminate the impact of channel errors. Furthermore, an iterative green secure beamformer design algorithm is developed. The numerical results show that our proposed algorithm has the better performance compared with the conventional schemes in terms of secure-energy-efficiency. Specifically, our proposed algorithm improves secrecy energy efficiency by 57.14%, as compared to the half-duplex secure transmission scheme.

The accuracy of channel state information (CSI) acquisition is nontrivial for intelligent reflection surface (IRS)-assisted massive multiple-input–multiple-output (MIMO) systems due to the IRS comprises of a large number of low-cost electromagnetic reflection units that can smartly reflect the impinging signal. In this article, we investigate the downlink channel estimation problem for IRS-assisted millimeter-wave (mmWave) massive MIMO systems, where the IRS provides effective reflected paths to enhance the coverage and spectral efficiency. To reduce the hardware costs and power consumption, the massive MIMO system is adopted the mixed-analog-to-digital converter (ADC) architecture, in which a fraction of antennas is equipped with high-resolution ADCs and a large number of antennas are deployed with low-resolution ADCs. Considered the finite-dimensional millimeter-wave channel model, we utilize the special row–column sparse characteristics of the channel matrix and propose a row-structured sparsity based on the orthogonal matching pursuit (RS-OMP) algorithm. The RS-OMP algorithm sequentially calculates the row–column support sets of the angular cascaded channel matrix and then uses the least square (LS) algorithm to reconstruct the cascaded channel matrix, which aim to achieve a lower computational complexity. Simulation results demonstrate that under the same system configuration, the proposed RS-OMP algorithm not only improves the accuracy of channel estimation but also reduces more than 75% of the pilot overhead compared to the traditional orthogonal matching pursuit algorithm.

The profound fusion of reconfigurable intelligent surfaces (RIS) with non-orthogonal multiple access (NOMA) holds the potential to substantially boost the data transmission rates within the Internet-of-Medical-Things (IoMT). However, a notable concern arises from the utilization of successive interference cancellation (SIC) technology by NOMA users in the IoMT networks, which raises privacy challenges when dealing with a substantial number of NOMA users within the IoMT ecosystem. This paper introduces a groundbreaking approach leveraging RIS to significantly enhance the security of private users within RIS-aided NOMA networks, specifically tailored for IoMT applications. Our core objective is to maximize the system1secure rate for private users, achieved by a simultaneous optimization of both transmit beamforming and reflecting vectors. Acknowledging the inherent non-convex nature of the optimization problem at hand, we strategically decompose it into two distinct subproblems: the optimization of beamforming strategies and the intricate optimization of RIS reflection mechanisms. Leveraging the established successive convex approximation (SCA) methodology, we adeptly transform these challenging subproblems into tractable and convex forms. Addressing these complex challenges, we introduce a highly efficient algorithm grounded in an alternating optimization paradigm, enabling us to iteratively address the subproblems in a coordinated, synergistic manner. Our simulation results unequivocally showcase substantial gains in secrecy performance afforded by our proposed scheme.

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.

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.

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.