This paper investigates an integrated sensing and communication (ISAC) system incorporating backscattering radio frequency identification (RFID) tag. In this configuration, a base station (BS) simultaneously serves multiple users through a communication beam while utilizing a sensing beam to detect the presence of an RFID tag. A joint beamforming design problem is formulated to maximize the sum-rate for the users while ensuring the minimum quality of service (QoS) for the tag detection and the minimum QoS of all communication users. To tackle the non-convex nature of objective function the Lagrangian dual transform technique is employed. Due to the coupling of variables, an alternating optimization (AO) based algorithm is proposed with guaranteed convergence. Through numerical simulations, we validate the effectiveness of our proposed algorithm. Additionally, we assess the impact of several key parameters on system performance, including the number of transmitting antennas at the BS, the available transmit power at the BS, the minimum QoS for the communication users, and the number of communication users.

As the proliferation of Internet of Things (IoT) devices accelerates, next-generation wireless networks face unprecedented demands for secure, efficient, and scalable communication frameworks. This paper investigates the integration of non-orthogonal multiple access (NOMA) with reconfigurable intelligent surfaces (RIS) and full-duplex (FD) operations to address these challenges while mitigating the adverse effects of residual hardware impairments (HWI) that cause signal distortion. The proposed RIS-aided FD-NOMA system is designed to enhance resilience and secrecy in IoT communication networks, optimizing the secrecy rate while adhering to power constraints for active beamforming at the base node (BN) and unit-modulus requirements for passive beamforming via RIS. Employing an alternate optimization (AO) framework, the complex joint optimization problem is divided into tractable subproblems solved through generalized convex approximations to achieve near-optimal solutions. Numerical results validate the superiority of the proposed model, demonstrating substantial performance improvements over conventional IoT systems without RIS support or with half-duplex NOMA protocols. The findings underscore the transformative potential of RIS-aided FD-NOMA systems in securing IoT networks against hardware imperfections while meeting their stringent connectivity and security demands.

In this work, we investigate the performance of a multi-active reconfigurable intelligent surface (ARIS)-aided full-duplex (FD) secure Internet of Things (IoT) network with imperfect Channel State Information (iCSI) in the presence of an eavesdropper (Eve). We formulate a power minimization problem while ensuring the minimum Quality of Service (QoS) of all the nodes within available resource constraints considering the norm-bounded iCSI. To tackle the nonconvex nature of the formulated problem, we adopt analytical methods, such as semidefinite programming, S-procedure, and general sign-definiteness, and propose an alternating optimization (AO)-based algorithm that jointly optimizes the receive and transmit beamforming at Alice, power allocation at each uplink user, and active beamforming at ARIS. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of ARISs, compared to its passive RIS (PRIS) counterpart, toward achieving a robust and secure FD system is demonstrated. Finally, we discuss the impact of key parameters, such as maximum amplification factor, RIS, and CSI error on the performance of the considered system.

In this work, we investigate the performance of an active reconfigurable intelligent surface (RIS)-aided multi-user uplink secure private industrial network. With an aim to provide a more sophisticated and consolidated framework towards the robust transmission design, we formulate a sum secrecy rate maximization while ensuring a minimum performance at each user within available resource constraints considering the norm-bounded imperfect channel state information (CSI) at Eavesdropper (Eve). To tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the equalizer, beamforming at the RIS, and power allocation at each user. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of active RIS, compared to passive RIS, towards a robust uplink secure private network is demonstrated. Finally, we discuss the impact of key parameters such as maximum power budget at each user and RIS, and CSI error on the secrecy performance of the considered network.

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided internet of things (IoT) communications under imperfect channel state information (CSI). We formulate a multi-objective optimization problem (MOOP) to design transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm bounded CSI error model. Due to the conflicting objective functions and non-convex nature of the above optimization problem, the MOOP is simplified using the.-constraint method and subsequently adopting advanced optimization tools, such as Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters such as the number of RIS elements, available transmit power at BS and the minimum HP on the performance of the considered system.

This work proposes a new model of reconfigurable intelligent surface (RIS) called cognizable RIS (CRIS) that is specifically designed to meet the unique demands of users who require extreme-ultra-reliable and low-latency Communication (xURLLC) in the sixth generation (6G) wireless networks. The programmable elements in the proposed CRIS unit can adapt to different modes of operation to provide significant performance gain. To improve reliability at the receiver, we integrate unmanned aerial vehicles with the CRIS module, which enhances network performance through beamforming and mobility. Our study focuses on maximizing the sum throughput in a multiple-input multiple-output scenario using the rate-splitting multiple access communication system. To achieve this, we introduce a novel hybridized multi-agent-based deep reinforcement learning (DRL) algorithm for optimal resource allocation that maximizes the sum throughput. We incorporate long-short-term memory (LSTM) networks into our proposed DRL to address the temporal dependencies due to stochastic channel conditions. By utilizing the proposed LSTM-based multi-agent DRL (MA-DRL) algorithm, we achieve notable gains of 11.7% and 26.9% in sum throughput over widely recognized DRL benchmark algorithms, all while adhering to xURLLC’s stringent maximum packet error probability constraint of 10−9.

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided green multiple input single output (MISO) communications under imperfect channel state information (CSI). With an aim to provide green and robust transmission, we formulate a multi-objective optimization problem (MOOP) to design transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that maximizes the energy efficiency at the information receivers and harvested power at the energy receivers under the norm bounded CSI error model. Due to the conflicting objective functions and non-convex nature, the MOOP is simplified using the$\epsilon$-constraint method. We then propose an alternating optimization-based iterative algorithm that utilizes semidefinite programming to determine the optimal TPV and PSM one by one until convergence is achieved. Via exhaustive simulations, we graphically demonstrate the effectiveness and robustness of the proposed algorithm. Furthermore, we assess the impact of several key parameters such as the number of RIS elements, available transmit power at BS and the minimum harvested power at each energy receiver on the performance of the considered system.

This paper investigates an unconventional framework of rate-splitting multiple access (RSMA)-integrated full-duplex (FD) system to attain spectral-efficient multi-user communication. The considered FD-RSMA system divides and encodes the original messages of each downlink (DL) and uplink (UL) into two different sub-messages, and later transmits them at the same resource block, resulting in strong inter-user interference and cross-link interference, i.e., self-interference (SI) and co-channel interference (CCI). Specifically, we focus on maximizing the sum rate of the considered FD-RSMA system via joint power allocation for simultaneous UL and DL communication, subject to transmit power constraints and given quality of service (QoS) requirements. To tackle the non-convexity of the formulated problem, we adopt an iterative algorithm that employs semidefnite programming (SDP), majorization minimization (MM), and inner approximation (IA) techniques to attain near-optimal resource allocation with effective interference management. Simulation results validate that the FD-RSMA scheme outperforms conventional half-duplex, multi-user linear precoding, and non-orthogonal multiple access schemes.

Reconfigurable intelligent surface (RIS) is an emerging next-generation technology that can improve the energy efficiency using multiple low-power passive metasurfaces which reflect the desired signal to the users. In this work, we consider a RIS-assisted underlay multiuser multiple-input multiple-output cognitive radio network and formulate a weighted energy efficiency maximization problem in order to jointly optimize the active precoding matrix (APM) at the secondary transmitter and passive precoding matrix (PPM) at the RIS subject to the constraints of available transmission power at the secondary transmitter and maximum allowable interference towards the primary user/receiver. However, due to the coupling of APM and PPM variables, the problem becomes non-convex, and conventional optimization methods cannot be used to solve it. Therefore, by adopting the weighted minimum mean-square error method we first transform the non-convex objective function into a convex one. Next, based on the block coordinate descent method, we propose an iterative algorithm that determines the optimal APM and PPM using Lagrange dual decomposition method and inner approximation method, respectively. Finally, the optimality and efficacy of the proposed algorithm are validated using numerical simulations. The impact of the channel state information (CSI) error has been studied via numerical simulations on the proposed network and it shows that the proposed design is relatively robust against the CSI imperfections, especially at low SNR conditions.