In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several low mobility targets. We formulate a sum-rate optimization problem that accounts for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory, and transmit beamformer for integrated sensing and communication (ISAC). Subsequently, we introduce a transfer learning based approach combining with deep deterministic policy gradient (DDPG) to enhance the overall data rate while minimizing the time it takes for users to transmit data. Additionally, we present an alternating optimization (AO) algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and offers a solution that is very close to optimal. Finally, we showcase the superiority of the proposed scheme through Monte Carlo simulations. Also, we have compared the performance with perfect channel state information (CSI) counterpart. The outcomes of simulations confirm the theoretical analysis and demonstrate the efficiency of the proposed framework. Additionally, the results reveal the advantages of incorporating HRIS aided UAV assisted ISAC in improving the quality of both communication and sensing performance.

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several moving targets. We formulate an optimization model that account for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory and transmit beamformer for integrated sensing and communication (ISAC), while aiming to maximize the overall achievable sum-rate. Subsequently, we introduce an alternating optimization algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and ultimately yielding a solution that is nearly optimal. Using Monte Carlo simulations, we showcase the superiority of the suggested approach in comparison to baseline schemes.

In this work, we explore the use of active reconfigurable intelligent surfaces (A-RIS) to improve vehicle-to-everything (V2X) communication systems to address limitations in traditional vehicular communication. In particular, we formulate an optimization problem to maximize the uplink sum rate for vehicle-to-infrastructure (V2I) links by optimizing transmit precoders, phase-shift matrices, transmit power, and spectrum sharing for vehicle-to-vehicle (V2V) links. To handle complex hybrid control scenarios, we propose a mixed-action deep reinforcement learning (DRL) algorithm and compare it with conventional benchmark methods like deep deterministic policy gradient (DDPG) with discrete actions (DA) and alternating optimization (AO). We evaluate the proposed algorithm’s effectiveness under imperfect channel state information as well. Simulation results highlight the efficacy of our approach, demonstrating significant enhancement in vehicular communication quality through A-RIS. Furthermore, we illustrate the impact of various factors such as number of A-RIS elements, vehicle speed, loss, execution time, amplification power, and CSI error on the performance of the V2X system.

In this article, we propose a novel framework that combines simultaneous localization and communication (SLAC) using a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) systems. Our primary focus is on enhancing resource efficiency in such systems. We introduce Cloud Radio Access Networks (C-RAN) that facilitate collaboration between multiple base stations (BSs), enhancing cooperation benefits for both communication and sensing capabilities. To evaluate localization performance, we formulate an optimization problem to minimize the squared position error bound (SPEB) that reflects the system functional performance by optimizing the transmit beamformer, phase shift and subcarrier assignment under certain constraints. Moreover, in order to adjust the phase shift of the RIS, we propose a RIS-aided cooperative ISAC SLAC protocol. This approach utilizes the measurements collected to refine the location and velocity estimates of the agent, as well as to reconstruct the environmental map with enhanced accuracy. However, the high dimensionality of the decision space makes the problem computationally intensive and challenging to navigate using gradient-based or exhaustive search methods. To efficiently tackle these issues, we construct a framework based on Markov decision processes (MDPs) and address it by introducing a novel algorithm called hybrid deep reinforcement learning (HDRL) algorithm. We validate our proposed algorithm through various simulations, demonstrating its effectiveness in improving system performance by comparing with the baseline schemes.

In this work, we consider a full-duplex (FD) communication system that uses a simultaneous transmission and reflection (STAR) enabled reconfigurable intelligent surfaces (RIS) to assist the communication and sensing between a base station (BS) to a single set of UL and DL user, and target over the same-time frequency dimension. In order to explore the performance of the proposed framework, we offer an analytical framework and accordingly, we propose an optimization problem to jointly optimize the phase-shift matrices at the STAR RIS (S-RIS) that maximizes the possible sum-rate. Due to the non-convexity of the optimization problem, we then propose a low-complexity meta-reinforcement learning (MRL) algorithm that reduces the overall training overhead. We also demonstrate the effectiveness of the proposed algorithm in providing near-optimal design in the case of imperfect channel state information (ICSI). Additionally, in order to verify how well the proposed framework work and to show the superiority of the proposed algorithm, we provide a fair comparison with two baseline schemes a) twin delayed deep deterministic policy gradient (TD3) and b) deep deterministic policy gradient (DDPG). Simulation results verify that the proposed approach results in superior performance.

In this paper, we consider Simultaneous Transmission and Reflection (STAR) Reconfigurable Intelligent Surface (S-RIS) and passive RIS (P-RIS) assisted integrated sensing and communication system (ISAC), where S-RIS is enabled to broadcast communication signal, and P-RIS assists sensing functionalities. In particular, we jointly optimize the beamforming vector at the multi-antenna ISAC transmitter, and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit at ISAC transmitter while ensuring the performance of sensing model to detect targets in its vicinity and limitations of phase and amplitude of S-RIS elements. To address the non-convexity of the above problem, we propose a low-complexity alternating optimization (AO) algorithm. Furthermore, we provide a comprehensive simulation-based graphical results to verify the viability of the proposed framework with its P-RIS assisted counterpart. Eventually, exhaustive simulation results are demonstrated to present the impact of RIS elements and the number of antennas at the ISAC transmitter. Accordingly, we illustrate the impact of S-RIS and the number of targets to highlight the trade-off between sensing and communication.

In this work, we consider a novel reconfigurable intelligent surface (RIS)-assisted full duplex (FD) sixth generation (6G)-vehicle-to-everything (V2X) communication network having a FD base station (BS) simultaneously communicating with an uplink (UL) and a downlink (DL) mobile vehicles with the aide of two RISs, one for each link. We provide an analytical framework to investigate the performance of this network and, consequently, formulate an optimization problem to jointly optimize the phase-shift matrices at both the RISs that maximizes the achievable sum-rate. Thereafter, we propose a successive refinement algorithm which uses an iterative approach to solve the problem and provide optimum values of phase-shift matrix at each RIS. We validate the accuracy of the proposed algorithm by exhaustive simulation based graphical results. Accordingly, we demonstrate the dominance of the considered FD system over its half-duplex (HD) counterpart. Moreover, we also highlight the impact of imperfect self interference cancellation and discuss the trade-off between the UL and DL performances due to this imperfection.

In this work, we investigate the performance of a federated edge learning (FEEL)-enhanced edge computing in unmanned aerial vehicles (UAV)-aided internet of things (IoT) system under the consideration of limited energy at each UAV. It consists of multiple UAVs which apply FEEL for local training and, then, transmit the required parameters to a centralized IoT-server. We use a new cost metric obtained by a linear combination of latency and energy consumption, and formulate an optimization problem to jointly optimize the central processing unit (CPU)-frequency during FEEL and allotted bandwidth under the consideration of the limited overall system bandwidth and energy available at each UAV. Due to the non-convex nature of the formulated problem, we propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm that solves the problem and provides the optimum CPU frequency and allotted bandwidth to each user. We validate the accuracy and convergence of the proposed algorithm via exhaustive simulations and highlight its effectiveness by comparing its performance with that of deep deterministic policy gradient (DDPG) and deep Qnetwork (DQN)-based solutions.