Conventional visible light positioning (VLP) systems are limited by inadequate positioning accuracy and vulnerability to obstacle occlusion, thereby hindering their deployment in precision-critical applications. To address these challenges, this paper proposes a fusion algorithm that synergistically combines received signal strength (RSS) and angle of arrival (AoA) information. Furthermore, the proposed approach incorporates an intelligent reflecting surface (IRS) framework into the system model, thereby improving system robustness and simultaneously enhancing positioning accuracy under sparse light-emitting diode (LED) deployment, blockage, or non-line-of-sight (NLoS) conditions. Specifically, this paper employs a multi-photodetector (PD) array at the receiver to formulate a system of linear equations based on RSS measurements, which facilitates accurate angle estimation. This derived AoA information is subsequently fused with the RSS data to establish a joint positioning objective function, thereby mitigating the limitations associated with single-parameter approaches. Crucially, an optical IRS is integrated to produce robust NLoS propagation paths, significantly enhancing accuracy in scenarios characterized by a scarcity of LEDs or obstructed line-of-sight (LoS) links, which are common challenges in practical deployments. To address the resulting non-convex optimization problem, a dimension learning-based hunting enhanced grey wolf optimizer (GWO-DLH) is developed, ensuring efficient convergence to the global optimum. Comprehensive simulations conducted under realistic channel models demonstrate that the proposed algorithm achieves a lower root-mean-square error compared to conventional RSS-only or AoA-only methods, while maintaining a computational complexity that is comparable to state-of-the-art techniques. These findings substantiate the algorithm’s effectiveness in balancing accuracy and robustness, thereby providing a foundational framework for the advancement of high-precision indoor optical positioning systems.

This study investigates the synergy between ratesplitting multiple access (RSMA) and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a unified framework to realize ubiquitous, intelligent, and resilient connectivity in future sixth-generation networks, while enhancing both spectral and energy efficiency. Specifically, in the STAR-RIS-assisted multi-user RSMA network under consideration, we develop an intelligent optimization strategy that jointly designs the active beamforming at the transmitter, the allocated transmission rate for the common stream, and the passive beamforming vectors for both transmission and reflection regions of the STAR-RIS, while accounting for transceiver hardware impairments and imperfect channel state information (CSI). In addition, system robustness is ensured by incorporating a bounded channel estimation error model that rigorously reflects CSI imperfections and ensures resilience against worst-case estimation errors. To tackle the highly non-convex problem, we propose an intelligent optimization algorithm that decouples the original problem into two sub-problems, which are then solved iteratively. Firstly, the active beamforming vectors for both the common and private signals are obtained by reformulating the original non-convex problem into a tractable convex semi-definite programming (SDP) framework, leveraging successive convex approximation (SCA) and semi-definite relaxation (SDR) for enhanced computational efficiency. Secondly, the passive beamforming vectors for the transmission and reflection regions of the STAR-RIS are optimized through a convex SDP reformulation by exploiting SCA and SDR techniques. Additionally, when the resulting active or passive beamforming solutions are of higher rank, Gaussian randomization is employed to construct rank-one solutions. Finally, the effectiveness of the proposed optimization strategy is demonstrated through numerical simulations, which reveal significant performance gains over benchmark schemes and confirm rapid convergence.

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, the power-splitting ratios, and scattering matrix of the BD-RIS, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model and ensure that the resulting system satisfies all energy harvesting constraints. In the considered system, we design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. To tackle the inherent non-convexity of the problem, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized in an iterative manner. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming problem through successive-convex approximation (SCA), whereas the inherently convex power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the conjugate-gradient method. Finally, numerical simulations are conducted to evaluate the performance of the proposed scheme, revealing significant performance improvements over existing benchmarks and demonstrating rapid convergence within a reasonable number of iterations.

The next generation of wireless communication networks must offer robust connectivity to serve users in remote or disaster-stricken regions where terrestrial infrastructure is unavailable or compromised. High-altitude platforms (HAPs), functioning as non-terrestrial network (NTN) nodes, can rapidly restore coverage and extend service reach by transmitting directly to ground users. To further enhance communication performance, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can be deployed alongside HAPs, intelligently shaping the wireless channel to improve channel reliability. In this work, we investigate a HAP-assisted NTN in which a STAR-RIS aids downlink transmission to multiple ground users under a rate-splitting multiple access (RSMA) protocol with imperfect successive interference cancellation (SIC). The joint design of power allocation at the HAP and STAR-RIS beamforming presents a challenging non-convex problem because of the coupled rate expressions and rank-one constraints on the STAR-RIS matrices. To address this, we introduce auxiliary variables and apply successive convex approximation (SCA) to convexify rate functions, while employing a difference-of-convex (DC) programming approach to handle the rank-one requirement. An alternating optimization framework is then developed to iteratively solve a convex power allocation subproblem and a penalized semi-definite program for STAR-RIS beamforming. Simulation results show the efficacy of the proposed framework, showing excellent performance even under imperfect SIC and with discretized phase shifts at the STAR-RIS.

Unmanned aerial vehicles (UAVs) have become essential in advancing multi-access edge computing (MEC), providing flexible platforms that enhance network capacity, coverage, and efficiency while reducing latency and improving communication quality. Integrating reconfigurable intelligent surfaces (RIS) with UAV-based MEC systems further elevates these capabilities, delivering significant gains in computational power, energy efficiency (EE), and physical layer security (PLS). However, managing the complexity of RIS within UAV networks requires sophisticated optimization strategies. This survey offers a comprehensive analysis of the fundamentals of RIS, UAVs, and MEC, followed by an in-depth examination of RIS configurations in UAV-based MEC systems, including static, dynamic, and hybrid models. We evaluate the benefits and challenges of RIS integration, such as improved communication, enhanced computational efficiency, optimized energy use, better task management, and strengthened security. In addition, the survey explores the latest advancements in RIS-assisted UAVs for MEC, focusing on boosting computational capacity, minimizing delay, maximizing EE, and enhancing security. To provide a thorough exploration of these topics, detailed summary tables are included, offering a comparative analysis of methodologies, performance metrics, and scenarios from recent studies. Furthermore, the survey presents key lessons learned from current research and identifies future research directions crucial for fully realizing the potential of RIS-enhanced UAV-based MEC systems in next-generation networks.

This survey provides an in-depth examination of the role of reconfigurable intelligent surfaces (RIS) in enhancing physical layer security (PLS) within unmanned aerial vehicle (UAV)-assisted networks, which are essential for the secure and efficient operation of sixth-generation (6G) wireless communications. The study covers various types of RIS—passive, active, and hybrid—and their applications in both terrestrial and aerial environments to strengthen PLS. Key focus areas include advanced PLS techniques such as optimizing UAV trajectory, beamforming, and RIS phase-shift configurations, all aimed at improving secrecy rates (SRs) while mitigating the risks of eavesdropping and jamming. Moreover, the survey also addresses strategies for enhancing energy-efficient SRs and implementing anti-jamming mechanisms within UAV-assisted networks. Additionally, it explores the integration of RIS-UAV systems with emerging technologies such as non-orthogonal multiple access (NOMA), mobile edge computing (MEC), cognitive radio, and THz networks, demonstrating how security can be enhanced in such networks. Through detailed performance analysis, the paper highlights the transformative potential of RIS-equipped UAVs in overcoming the potential security challenges for future 6G networks. Finally, the survey presents lessons learned and identifies critical future research directions and open challenges, offering insights that will guide the development of robust and secure RIS-assisted UAV systems in next-generation wireless networks.

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into Passive RIS (PRIS) and Active RIS (ARIS) paradigms. PRIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.

Low Earth orbiting (LEO) satellite-assisted wireless communication is increasingly vital for future communication networks due to the significant spectrum scarcity in radio frequency channels, presenting a critical bottleneck. Thus, optimizing the utilization of available radio frequency spectrum has become imperative. Advanced techniques like underlay communication and Rate Split Multiple Access (RSMA) have proven effective in enhancing spectrum utilization. When LEO satellites are applied to tasks such as agricultural assistance, search and rescue operations, and military defense, LEO-to-ground communication can leverage underlay fashion using RSMA to transmit messages to multiple users simultaneously on the same channel. However, conventional underlay communication setups necessitate transmitter cooperation to manage system interference. Enabling non-cooperative systems to communicate in an underlay fashion unlocks the untapped potential of these advanced transmission techniques. This study addresses the challenge of maximizing the RSMA rate of the LEO-to-ground communication system (secondary system) operating in an underlay mode without cooperation with the ground-to-ground communication system (primary system), where the primary network operates in a time-division multiple-access fashion. We propose a dueling-based double deep Q-learning solution to optimize the allowed transmission power at the LEO satellite, ensuring no outage in the primary system. Additionally, we introduce an optimal solution framework to distribute the allowed transmission power among all signals of the secondary devices, maximizing the RSMA rate while meeting the rate requirements of all underlay secondary devices. Simulation results demonstrate that this hybrid solution framework provides excellent performance while ensuring no outage at the primary network.

This manuscript introduces a novel energy-efficient optimization strategy for a zero-energy reconfigurable intelligent reflecting surface (Ze-RIS) supported backscatter communication system employing non-orthogonal multiple access (NOMA). The central objective is to maximize the energy-efficiency of the system by optimizing the several key parameters, including the amplitude reflection coefficient of Ze-RIS, the reflection coefficients of the backscatter tags, transmit beamforming at the base station, and passive beamforming at the Ze-RIS node, while incorporating a practical non-linear energy harvesting model both for the Ze-RIS and backscatter nodes. The proposed algorithm addresses the complex non-convex problem through three stages. Firstly, the transmit beamforming vectors are determined by leveraging the semi-definite programming and successive-convex approximation, while handling the rank-1 constraint with the semi-definite relaxation. Secondly, we determine the amplitude reflection coefficient of Ze-RIS by leveraging the monotonicity property of the objective function. Simultaneously, we compute the reflection coefficients of backscatter tags using the Dinkelbach algorithm, Lagrange duality, and the sub-gradient method. Thirdly, we compute passive beamforming using successive-convex approximation and semi-definite programming techniques, achieving a rank-1 solution through the penalty-based method. Finally, the numerical simulations confirm the effectiveness of the proposed approach, demonstrating its superiority over the benchmark competitors with rapid convergence within a few iterations.

This paper researches the system evaluations of the reconfigurable intelligent surface (RIS)-assisted satellite aerial terrestrial integrated systems. To ensure the stability of the regarded network, a link selection scheme is presented to get the balance between the system performance and the system efficiency. Besides, in order to build a practical environment of the transmission networks, the imperfect hardware, channel estimation errors and co-channel interference are both considered in the networks. Relied on the above considerations, the detailed analysis for the outage behaviors is shown along with the asymptotic outage probability in high signal-to-noise ratio scenarios. Moreover, the diversity order and coding gain are also provided to give fast methods to confirm the system evaluation. Finally, some re-presentative simulations are provided to confirm the efficiency and advantage of analytical results and the proposed link selection scheme.

In this manuscript, we introduce an efficient resource allocation strategy to enhance the security of an intelligent omni-surface (IOS) assisted secure Internet-of-things (IoT) enabled non-orthogonal multiple access (NOMA) network under residual hardware impairments (RHIs) resulting from imperfect hardware design. In particular, the goal is to maximize the sum secrecy rate of the considered multi-cluster based secure NOMA system assisted by an IOS node. This is achieved by optimizing both the active beamforming vectors of NOMA users within the transmission and reflection regions of the system, and the transmission and reflection coefficients of the IOS node, while adhering to quality-of-service, successive interference cancellation, power budget, and energy conservation constraints. Moreover, the presented alternating optimization framework tackles the significantly non-convex optimization problem through a two-stage process. Firstly, the active beamforming vectors are obtained using successive convex approximation (SCA) and second-order conic programming (SOCP) techniques. Secondly, based on the determined active beamforming vectors, the transmission and reflection coefficients of the IOS node are computed utilizing SCA and semi-definite relaxation (SDR) techniques, where rank-1 solution is achieved through Gaussian randomization method. Ultimately, the numerical simulations validate the efficacy of the suggested method over competing benchmarks, in terms of sum secrecy rate, showcasing its superiority in achieving fast convergence within a limited number of iterations.

Emerging technologies such as artificial intelligence and big data have made numerous Internet of things (IoT) applications possible. In particular, the Artificial Intelligence of Things (AIoT) has the potential to promote the digitization and intelligent connection of all things. However, the openness and diversity of AIoT makes data information vulnerable to security attacks which can lead to a disruption of mobile communication networks. The complexity of real-time data security events requires accurate prediction of AIoT security performance. In this paper, a secure communication system model based on decode-and-forward (DF) relaying is proposed and its security performance is analyzed. Expressions for the secrecy outage probability (SOP) are derived, and these are used to evaluate the security performance. For this purpose, an intelligent SOP prediction algorithm based on MS-Net is proposed. MobileNet and SqueezeNet networks are used to design an improved lightweight MS-Net model, which is composed of a depth separable convolution block and a fire module in parallel. The fire module is used to reduce the number of parameters in the first branch, and the depth-separable convolution block is employed in the second branch instead of the standard convolution. This can adapt to nonlinear characteristic in the AIoT safety data and reduce energy consumption. Afterwards, the convolutional block attention module(CBAM) attention mechanism is used to improve the model’s ability to capture features. The proposed algorithm provides better AIoT security performance than other algorithms. In particular, the mean squared error (MSE) is 68.1% better than that of RegNet.

In this manuscript, we present an energy-efficient alternating optimization framework based on the multiantenna ambient backscatter communication (AmBSC)-assisted cooperative nonorthogonal multiple access (NOMA) for next-generation (NG) Internet of Things (IoT)-enabled communication networks. Specifically, the energy-efficiency maximization is achieved for the considered AmBSC-enabled multicluster cooperative IoT NOMA system by optimizing the active-beamforming vector and power-allocation coefficients (PACs) of IoT NOMA users at the transmitter, as well as passive-beamforming vector at the multiantenna-assisted backscatter node. Usually, increasing the number of IoT NOMA users in each cluster results in intercluster interference (ICI) (among different clusters) and intracluster interference (among IoT NOMA users). To combat the impact of ICI, we exploit a zero-forcing (ZF)-based active-beamforming, as well as an efficient clustering technique at the source node. Further, the effect of intracluster interference is mitigated by exploiting an efficient power-allocation policy that determines the PAC of IoT NOMA users under the Quality-of-Service (QoS), cooperation, SIC decoding, and power-budget constraints. Moreover, the considered nonconvex passive-beamforming problem is transformed into a standard semidefinite programming (SDP) problem by exploiting the successive-convex approximation (SCA), as well as the difference of convex (DC) programming, where Rank-1 solution of passive-beamforming is obtained based on the penalty-based method. Furthermore, the numerical analysis of simulation results demonstrates that the proposed energy-efficiency maximization algorithm exhibits an efficient performance by achieving convergence within only a few iterations.

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

This correspondence presents a construction of quasicyclic (QC) low-density parity-check (LDPC) codes based on a special type of combinatorial designs known as block disjoint difference families (BDDFs). The proposed construction of QC-LDPC codes gives parity-check matrices with column weight three and Tanner graphs having a girth lower-bounded by 6. The proposed QC-LDPC codes provide an excellent performance with iterative decoding over an additive white Gaussian-noise (AWGN) channel. Performance analysis shows that the proposed short and moderate length QC-LDPC codes perform as well as their competitors in the lower signal-to-noise ratio (SNR) region but outperform in the higher SNR region. Also, the codes constructed are quasicyclic in nature, so the encoding can be done with simple shift-register circuits with linear complexity.

This correspondence presents a jointly designed quasicyclic (QC) low-density parity-check (LDPC) coded-relay cooperation with joint-iterative decoding in the destination node. Firstly, a design-theoretic construction of QC-LDPC codes based on a combinatoric design approach known as optical orthogonal codes (OOC) is presented. Proposed OOC-based construction gives three classes of binary QC-LDPC codes with no length-4 cycles by utilizing some known ingredients including binary matrix dispersion of elements of finite field, incidence matrices, and circulant decomposition. Secondly, the proposed OOC-based construction gives an effective method to jointly design length-4 cycles free QC-LDPC codes for coded-relay cooperation, where sum-product algorithm- (SPA-) based joint-iterative decoding is used to decode the corrupted sequences coming from the source or relay nodes in different time frames over constituent Rayleigh fading channels. Based on the theoretical analysis and simulation results, proposed QC-LDPC coded-relay cooperations outperform their competitors under same conditions over the Rayleigh fading channel with additive white Gaussian noise.