Search Results (14)

In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios. Full article

Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). Full article

Wireless sensor networks (WSNs) are a cornerstone of modern Internet of Things (IoT) infrastructure, enabling seamless data collection and communication for many IoT applications. However, the deployment of WSNs in remote or inaccessible locations poses significant challenges in terms of energy efficiency and secure communication. Sensor nodes, with their limited battery capacities, require innovative strategies to minimize energy consumption while maintaining robust network performance. Additionally, ensuring secure data transmission is critical for safeguarding the integrity and confidentiality of IoT systems. Despite various advancements, existing methods often fail to strike an optimal balance between energy efficiency and quality of service (QoS), either depleting limited energy resources or compromising network performance. This paper introduces a novel framework that integrates double reconfigurable intelligent surfaces (RISs) into WSNs to enhance energy efficiency while ensuring secure communication. To jointly optimize both RIS phase shift matrices, we employ a fuzzy deep reinforcement learning (FDRL) framework that integrates reinforcement learning (RL) with fuzzy logic and long short-term memory (LSTM)-based architecture. The RL component learns optimal actions by iteratively interacting with the environment and updating Q-values based on a reward function that prioritizes both energy efficiency and secure communication. The LSTM captures temporal dependencies in the system state, allowing the model to make more informed predictions about future network conditions, while the fuzzy logic layer manages uncertainties by using optimized membership functions and rule-based inference. To explore the search space efficiently and identify optimal parameter configurations, we use the advantage of the multi-objective artificial bee colony (MOABC) algorithm as an optimization strategy to fine-tune the hyperparameters of the FDRL framework while simultaneously optimizing the membership functions of the fuzzy logic system to improve decision-making accuracy under uncertain conditions. The MOABC algorithm enhances convergence speed and ensures the adaptability of the proposed framework in dynamically changing environments. This framework dynamically adjusts the RIS phase shift matrices, ensuring robust adaptability under varying environmental conditions and maximizing energy efficiency and secure data throughput. Simulation results validate the effectiveness of the proposed FDRL-based double RIS framework under different system configurations, demonstrating significant improvements in energy efficiency and secrecy rate compared to existing methods. Specifically, quantitative analysis demonstrates that the FDRL framework improves energy efficiency by 35.4%, the secrecy rate by 29.7%, and RSMA by 27.5%, compared to the second-best approach. Additionally, the model achieves an R² score improvement of 12.3%, confirming its superior predictive accuracy. Full article

Next- generation wireless communications are projected to integrate reconfigurable intelligent surfaces (RISs) to perpetrate enhanced spectral and energy efficiencies. To quantify the performance of RIS-aided wireless networks, the statistics of a single random variable plus the sum of double random variables becomes a core approach to reflect how communication links from RISs improve wireless-based systems versus direct ones. With this in mind, the work applies the statistics of a single random variable plus the sum of double random variables in the secure performance of RIS-based non-orthogonal multi-access (NOMA) systems with the presence of untrusted users. We propose a new communication strategy by jointly considering NOMA encoding and RIS’s phase shift design to enhance the communication of legitimate nodes while degrading the channel capacity of untrusted elements but with sufficient power resources for signal recovery. Following that, we analyze and derive the closed-form expressions of the secrecy effective capacity (SEC) and secrecy outage probability (SOP). All analyses are supported by extensive Monte Carlo simulation outcomes, which facilitate an understanding of system communication behavior, such as the transmit signal-to-noise ratio, the number of RIS elements, the power allocation coefficients, the target data rate of the communication channels, and secure data rate. Finally, the results demonstrate that our proposed communication can be improved significantly with an increase in the number of RIS elements, irrespective of the presence of untrusted proximate or distant users. Full article

For the future of sixth-generation (6G) wireless communication, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) technology is emerging as a promising solution to achieve lower power transmission and flawless coverage. To facilitate the performance analysis of RIS-assisted networks, the statistics of the sum of double random variables, i.e., the sum of the products of two random variables of the same distribution type, become vitally necessary. This paper applies the statistics of the sum of double random variables in the performance analysis of an integrated power beacon (PB) energy-harvesting (EH)-based NOMA-assisted STAR-RIS network to improve its outage probability (OP), ergodic rate, and average symbol error rate. Furthermore, the impact of imperfect successive interference cancellation (ipSIC) on system performance is also analyzed. The analysis provides the closed-form expressions of the OP and ergodic rate derived for both imperfect and perfect SIC (pSIC) cases. All analyses are supported by extensive simulation results, which help recommend optimized system parameters, including the time-switching factor, the number of reflecting elements, and the power allocation coefficients, to minimize the OP. Finally, the results demonstrate the superiority of the proposed framework compared to conventional NOMA and OMA systems. Full article

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs and the intelligent reflection of signals by RISs. However, these technologies are subject to inherent limitations such as the restricted range of UAVs and limited RIS coverage, which hinder their broader application. The integration of UAVs and RISs into UAV–RIS schemes presents a promising approach to surmounting these limitations by leveraging the strengths of both technologies. Motivated by the above observations, we contemplate a novel UAV–RIS-aided MEC system, wherein UAV–RIS plays a pivotal role in facilitating communication between terrestrial vehicle users and MEC servers. To address this challenging non-convex problem, we propose an energy-constrained approach to maximize the system’s energy efficiency based on a double-deep Q-network (DDQN), which is employed to realize joint control of the UAVs, passive beamforming, and resource allocation for MEC. Numerical results demonstrate that the proposed optimization scheme significantly enhances the system efficiency of the UAV–RIS-aided time division multiple access (TDMA) network. Full article

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity. Full article

Active reconfigurable intelligent surface (ARIS) has sparked more attention due to its capability to overcome the impact of double fading. This paper introduces using an ARIS to aid non-orthogonal multiple access (NOMA) communications over cascade Rician fading channels, where the direct links between the base station and users are seriously blocked. By applying ARIS, it can amplify the superposed signals to overcome the double pass loss effect caused by passive RIS. Both ARIS and NOMA can have synergistic impacts on sixth-generation communication systems. New approximated and asymptotic expressions in terms of outage probability and ergodic data rate of the k-th user are deduced for ARIS-NOMA systems. Based on asymptotic analytical results, we further calculate the diversity order and high signal-to-noise ratio slope of the k-th user. Finally, the system throughput of ARIS-NOMA is discussed in the delay-constrained transmission mode. Monte Carlo numerical results are performed to verify that: (1) the outage behaviors of ARIS-NOMA are better than that of ARIS-assisted orthogonal multiple access (OMA); (2) as the impact of thermal noise caused by ARIS becomes larger, the communication performance from the base station to ARIS, then to users, becomes worse; (3) the ARIS-NOMA systems have the ability to provide the improved ergodic data rate relative to ARIS-OMA. Full article

The reconfigurable intelligent surface (RIS) has been widely recognized as a rising paradigm for physical layer security due to its potential to substantially adjust the electromagnetic propagation environment. In this regard, this paper adopted the RIS deployed on an unmanned aerial vehicle (UAV) to enhance information transmission while defending against both jamming and eavesdropping attacks. Furthermore, an innovative deep reinforcement learning (DRL) approach is proposed with the purpose of optimizing the power allocation of the base station (BS) and the discrete phase shifts of the RIS. Specifically, considering the imperfect illegitimate node’s channel state information (CSI), we first reformulated the non-convex and non-conventional original problem into a Markov decision process (MDP) framework. Subsequently, a noisy dueling double-deep Q-network with prioritized experience replay (Noisy-D3QN-PER) algorithm was developed with the objective of maximizing the achievable sum rate while ensuring the fulfillment of the security requirements. Finally, the numerical simulations showed that our proposed algorithm outperformed the baselines on the system rate and at transmission protection level. Full article

Traditional anti-jamming techniques such as frequency hopping (FH) and direction-sequence spread spectrum (DSSS) are easily targeted by jammers. Inspired by the significant advantages of reconfigurable intelligent surfaces (RIS), and in order to overcome “double fading”, controllable multiple active RISs are proposed to explore anti-jamming communication in this paper. To verify the feasibility of active RIS, the anti-jamming performance of active RIS is analyzed through theoretical derivation and simulation and compared with passive RIS. Furthermore, to solve the optimization problem of active RIS, a controllable multi-active RIS assisted anti-jamming algorithm based on BCD is proposed. Theoretical analysis and simulation results show that in small-scale deployment scenarios of RIS, the anti-jamming performance of active RIS is better than that of passive RIS, and the complexity and optimization performance of the proposed algorithm are better than those of semidefinite relaxation (SDR) algorithms. Full article

This paper presents two precoding techniques for a reconfigurable intelligent surface (RIS)-assisted multi-user (MU) multiple-input multiple-output (MIMO) double quadrature spatial modulation (DQSM) downlink transmission system. Instead of being applied at the remote RIS, the phase shift vector is applied at the base station (BS) by using a double precoding stage. Results show that the proposed RIS-MU-MIMO-DQSM system has gains of up to 17 dB in terms of bit error rate (BER) and a reduction in detection complexity of 51% when compared with the conventional MU-MIMO system based on quadrature amplitude modulation (QAM). Compared with a similar system based on amplify and forward (AF) relay-assisted technique, the proposed system has a gain of up to 18 dB in terms of BER under the same conditions and parameters. Full article

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance the spectral efficiency of wireless communication systems. However, if there are many obstacles between the RIS and users, a single RIS may not provide sufficient performance. For this reason, a double RIS-aided communication system is proposed in this paper. However, this system also has a problem: the signal is attenuated three times due to the three channels created by the double RIS. To overcome these attenuations, an active RIS is proposed in this paper. An active RIS is almost the same as a conventional RIS, except for the included amplifier. Comprehensively, the proposed system overcomes various obstacles and attenuations. In this paper, an active RIS is applied to the second RIS. To reduce the power consumption of active elements, a partially active RIS is applied. To optimize the RIS elements, the sum of the covariance matrix is found by using channels related to each RIS, and the right singular vector is exploited using singular value decomposition for the sum of the covariance matrix. Then, the singular value of the sum of the covariance value is checked to determine which element is the active element. Simulation results show that the proposed system has better sum rate performance compared to a single RIS system. Although it has a lower sum rate performance compared to a double RIS with fully active elements, the proposed system will be more attractive in the future because it has much better energy efficiency. Full article

A reconfigurable intelligent surface (RIS) is a type of metasurface that can dynamically control the reflection and transmission of electromagnetic waves, such as radio waves, by changing its physical properties. Recently, RISs have played an important role in intelligently reshaping wireless propagation environments to improve the received signal gain as well as spectral efficiency performance. In this paper, we consider a millimeter wave (mmWave) vehicle-to-vehicle (V2V) multiple-input multiple-output (MIMO) system in which, an RIS is deployed to aid downlink V2V data transmission. In particular, the line-of-sight path of the mmWave system is affected by blockages, resulting in higher signaling overhead. Thus, the system performance may suffer due to interruptions caused by static or mobile blockers, such as buildings, trees, vehicles, and pedestrians. In this paper, we propose an RIS-assisted hybrid beamforming scheme for blockage-aware mmWave V2V MIMO systems to increase communication service coverage. First, we propose a conjugate gradient and location-based hybrid beamforming (CG-LHB) algorithm to solve the user sub-rate maximization problem. We then propose a double-step iterative algorithm that utilizes an error covariance matrix splitting method to minimize the effect of location error on the passive beamforming. The proposed algorithms perform quite well when the channel uncertainty is smaller than $10\%$. Finally, the simulation results validate the proposed CG-LHB algorithm in terms of the RIS-assisted equivalent channel for mmWave V2V MIMO communications. Full article

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RIS) are the promising emerging technologies proposed for the 6th-Generation (6G) network to improve the capacity, reliability, and coverage of wireless communications. By integrating the UAV with RIS (RIS-UAV), the three-dimensional (3D) mobility of the UAV can be leveraged to establish strong line-of-sight links with the ground nodes, while the RIS intelligently reflect the signals toward the desired directions. However, the existing literature on RIS-UAV systems mainly assumes the use of passive elements, which suffers from the double path-loss problem. The use of active elements in RIS, which could improve the reflected link performance at the cost of increased energy consumption, has not been considered for the RIS-UAV system. Further, the energy efficiency of a RIS-UAV with active elements remains as an open direction because there is a need to investigate the feasibility of either an active or hybrid RIS-UAV implementation. This paper proposes active and hybrid RIS-UAVs and investigates the energy efficiencies of active and hybrid RIS-UAVs in comparison with existing passive RIS-UAVs and conventional UAV relays. The numerical results reveal that the proposed hybrid and active RIS-UAV relaying schemes can provide up to 14 times and 26 times improvement as compared to the passive RIS-UAV, respectively. As opposed to the active RIS-UAV that requires a larger power budget, half-duplex UAV relays that have a lower spectral efficiency, and full-duplex UAV relays that suffer from self-interference, the hybrid RIS-UAV emerges as a promising option to assist the ground communication system. Full article