This study addresses the challenge of achieving high-precision indoor positioning in non-line-of-sight (NLoS) environments through the development of an innovative visible light positioning (VLP) system that utilizes optical intelligent reflecting surfaces (OIRS). Unlike current hybrid methodologies that combine both line-of-sight (LoS) and NLoS techniques tailored for Internet of Things (IoT) environments, our novel single-LED architecture relies solely on signals reflected by an OIRS to facilitate accurate positioning in intricate indoor settings where direct light paths are often obstructed. This system employs a two-stage maximum likelihood estimation framework that effectively integrates received signal strength (RSS) and time-of-arrival (ToA) characteristics, thereby addressing the shortcomings of traditional single-feature methods and ensuring reliable performance in densely populated IoT scenarios. To tackle the non-convex optimization problem, we propose an improved grey wolf optimization (IGWO) algorithm, which exhibits superior positioning accuracy and convergence properties when compared to particle swarm optimization and genetic algorithms. Simulation results substantiate the framework’s efficacy, demonstrating improved positioning accuracy. The proposed system presents a cost-effective solution for complex indoor environments where direct light paths are frequently obstructed, thereby advancing the practical application of VLP technologies.

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 paper proposes a robust received signal strength (RSS)-based localization framework for non-cooperative unmanned aerial vehicles. Conventional RSS methods face three fundamental obstacles: susceptibility to heavy-tailed measurement noise, intractable non-convexity, and severe accuracy degradation when target transmission power is unknown. These vulnerabilities present critical security risks to emerging low-altitude economy networks. To overcome these limitations, we propose an integrated joint-estimation architecture. First, a cascaded preprocessing pipeline, combining Gaussian outlier suppression and statistical median weighting, is developed to mitigate multipath-induced biases and minimize variance. Second, an information-theoretic base station (BS) selection mechanism is designed to identify geometrically optimal BSs, thereby exponentially reducing computational overhead in both uniform and random deployment scenarios. Third, the power-unknown problem is reformulated via semidefinite programming, absorbing the unknown parameter into a higher-dimensional convex cone to guarantee global convergence without relying on initial guesses. Extensive Monte Carlo simulations demonstrate that under uniform BS deployment, our strategy achieves sub-10-meter accuracy (approximately 5 m root mean square error) using only 5 selected BSs in typical urban conditions with a path loss exponent of 3. Consequently, this approach delivers a highly accurate and computationally efficient solution for real-time target tracking in complex environments.

The demand for high-precision localization services has surged significantly due to the rise of intelligent transportation and autonomous driving in large-scale indoor factories. To mitigate the challenge of reduced positioning accuracy resulting from line-of-sight (LOS) occlusion in indoor visible light positioning (VLP) technology, this paper proposes a positioning strategy that leverages optical intelligent reflecting surfaces (IRSs) within indoor Internet of Vehicles (IoVs) environments. This scheme utilizes the principle of range positioning based on the time difference of arrival (TDOA). A weighted least squares (WLS) method is initially derived as a benchmark positioning approach based on TDOA. Additionally, two high-accuracy positioning methods, namely, the Chan method and Taylor series expansion method, are proposed for different scenarios. The Chan method provides a closed-form solution suitable for low-computation cases, while the Taylor series expansion method can be iteratively exploited in complex situations. The detailed procedures of these three positioning approaches are also presented. Furthermore, theoretical analyses of the computational complexity of the WLS method, Chan method, and Taylor series expansion method are provided. Additionally, the positioning performance of the Cramér-Rao lower bound (CRLB) is analyzed and derived for the considered system model. The simulation results illustrate that the system model and positioning methods outlined can asymptotically achieve the derived CRLB, thereby validating the efficacy of the proposed positioning scheme and methods. These advancements hold significant potential for industrial automation, logistics operations, and safety-critical autonomous guided vehicles (AGVs) in smart factories, where robust centimeter-level positioning is essential for collision avoidance and task coordination under dynamic occlusion conditions.

The proliferation of time-sensitive Internet of Things (IoT) applications has significantly increased the demand for real-time communication in uplink orthogonal frequency division multiple access (UL-OFDMA) Wi-Fi networks. Despite extensive studies, how to meet the heterogeneous age of information (AoI) requirements across different stations (STAs) in Wi-Fi networks remains an open question. To tackle this issue, we propose a multi-agent reinforcement learning (MARL) resource allocation algorithm based on independent hybrid proximal policy optimization (IHPPO), aiming to minimize the AoI and power consumption for each STA while guaranteeing the heterogeneous AoI requirements among STAs within a resource-constrained environment. Specifically, the proposed strategy utilizes HPPO to directly optimize the original hybrid action space by combining discrete resource unit (RU) selection and continuous transmit power adjustment. Extensive simulations demonstrate the superior performance of the IHPPO mechanism in terms of convergence performance and the trade-off between AoI and power consumption, relative to the decomposed multi-agent deep deterministic policy gradient (DE-MADDPG) algorithm, the fully decentralized MADDPG (FD-MADDPG) algorithm, and the random method in different access scenarios.

In computing-oriented communication scenarios, over-the-air computation (AirComp) directly computes results by leveraging the waveform superposition of wireless multiple access channels (MACs), eliminating the need to recover individual edge device (ED) signals. This approach enhances efficiency but faces challenges in maintaining low function distortion under deep fading. In this article, we propose a new retransmission-based AirComp framework that incorporates a channel prediction model to mitigate function distortion. Our framework introduces a performance analysis and optimization strategy involving cross-timeslot receiver combining. The optimal transmission coefficients and denoising factors are derived to minimize computing mean squared error (MSE) and reduce sum-power consumption. We design and analyze AirComp retransmission schemes that account for channel state information (CSI) estimation errors in the initial timeslot. Simulations validate the effectiveness of our framework, which markedly decreases the MSE and power consumption, e.g., by about 61% and 55%, compared to existing benchmarks, demonstrating its superior noise resilience and significantly alleviated the impact of imperfect CSI. These results highlight the potential of our framework to enable efficient and reliable AirComp in time-varying fading channels, particularly for resource-constrained Internet of Things (IoT) environments.

This paper proposes a secrecy enhancement scheme for the space shift keying (SSK) assisted multiple-input single-output (MISO) visible light communications (VLC) system in a complex indoor environment, where the line-of-sight (LoS) link of the transmitter and legitimate user can be blocked or exist. By leveraging a properly arranged mirror array as an optical intelligent reflecting surface (ORIS), a legitimate user can access confidential information, while an eavesdropping user cannot intercept the confidential message. To achieve this goal, an optical artificial noise (OAN) assisted secrecy enhancement strategy is introduced. In this strategy, the transmitter transmits both the desired signal and the OAN signal simultaneously while adhering to power and amplitude constraints. The average mutual information (AMI) and achievable secrecy rate (ASR) are employed to analyze the secrecy performance of the ORIS aided SSK VLC system. Furthermore, to adapt to different environments, four system configuration scenarios are presented, and the corresponding secrecy performance is analyzed. To clarify the theoretical results of the OAN assisted indoor MISO SSK VLC system with an ORIS, extensive simulation results are performed.

The dual-function radar communication (DFRC) system utilizes a hardware platform to achieve both radar and communication functions. In comparison to independently exploiting radar and communication systems, the DFRC system can significantly reduce system redundancy, volume, weight, and energy consumption. This makes DFRC an important area of research with practical value in advanced internet of things (IoT) techniques. This paper explores an exemplary DFRC system based on the orthogonal chirp division multiplexing (OCDM) methodology. In this system, OCDM achieves chirp multiplexing through the Fresnel transform and is identified as a potential replacement for OFDM in high-speed communication systems. In the proposed system, we integrate index modulation (IM) into the communication subsystem and utilize the subchirp index of OCDM to convey additional communication information, thereby significantly enhancing the communication rate of the DFRC system. Furthermore, a radar processing algorithm utilizing the OCDM signal is developed. This algorithm integrates the sparsity-aided compressed sensing (CS) algorithm into the radar subsystem to enhance estimation precision and reduce the sampling rate and hardware complexity of the radar receiver. Based on the proposed OCDM-assisted DFRC scheme, the communication rate of the DFRC system and the ambiguity function of OCDM are evaluated. The feasibility and effectiveness of the proposed DFRC system are confirmed through numerical calculations and simulation results.

The secrecy capacity based on the assumption of having continuous distributions for the input signals constitutes one of the fundamental metrics for the existing physical layer security (PHYS) solutions. However, the input signals of real-world communication systems obey discrete distributions. Furthermore, apart from the capacity, another ultimate performance metric of a communication system is its symbol error ratio (SER). In this article, we pursue a radically new approach to PHYS by considering rigorous direct SER optimization exploiting the discrete nature of practical modulated signals. Specifically, we propose a secure precoding technique based on a multi-objective SER criterion, which aims for minimizing the confidential messages' SER at their legitimate user, while maximizing the SER of the confidential messages leaked to the illegitimate user. The key to this challenging multi-objective optimization problem is to introduce a priority factor that controls the priority of directly minimizing the SER of the legitimate user against directly maximizing the SER of the leaked confidential messages. Furthermore, we define a new metric termed as the security-level, which is related to the conditional symbol error probability of the confidential messages leaked to the illegitimate user. Additionally, we also introduce the secure discrete-input continuous-output memoryless channel (DCMC) capacity referred to as secure-DCMC-capacity, which serves as a classical security metric of the confidential messages, given a specific discrete modulation scheme. The impacts of both the channel's Rician factor and the correlation factor of antennas on the security-level and the secure-DCMC-capacity are investigated. Our simulation results demonstrate that the proposed priority-aware secure precoding based on the direct SER metric is capable of securing transmissions, even in the challenging scenario, where the eavesdropper has three receive antennas, while the legitimate user only has a single one.

In order to enhance the secrecy performance of the generalized space shift keying (GSSK) visible light communication (VLC) system, in this paper, an optical jamming-aided secrecy enhancement scheme is proposed, in which the source transmitter (S) simultaneously sends both the confidential desired signal and optical jamming signals under the amplitude and power constraints. The optical jamming signals obey the truncated Gaussian distribution for satisfying the constraints. Given the discrete set of channel inputs, the optical jamming-aided GSSK-VLC system's secrecy performance is analyzed. Explicitly, the average mutual information (AMI), the lower bound of AMI and its closed-form approximation as well as the achievable secrecy rate are formulated analytically. Furthermore, the optimal power sharing strategy of the proposed GSSK-VLC systems relying on optical jamming is derived. Closed-form expressions are provided for the optimal power sharing in both the low- and high-SNR regions. Finally, the extensive simulation results are presented to validate our analytical results.