Lei Qian

Visible Light Communication (VLC), with its high reliability, low latency, and high data rate, emerges as a promising technology for future Industrial Internet of Things (IIoT) applications. Considering the burstiness of IIoT traffic and the dynamics of VLC transmission, this paper focuses on resource allocation for the VLC-based IIoT network with statistical delay guarantees. Firstly, a Four-state Markov Modulated Multinomial Process (MMMP-4) arrival model is constructed to characterize the hybrid traffic of operational instruction data streams and process parameter data streams. Secondly, utilizing experimental data collected from an industrial-mimetic platform, a 2L-state Markov Modulated Multinomial Process (MMMP-2L) service model is proposed to characterize the transmission provided by the VLC system. Based on supermartingale theory, we derive the upper bound of the statistical delay violation probability for the VLC-based IIoT network. Further, under the long-term statistical delay constraint, a resource allocation problem is formulated to jointly optimize access point (AP)-IIoT device association and power control. By integrating Gumbel-Softmax approximation and introducing a branching structure for discrete and continuous variables, a Hybrid Resource-Optimized Deep Deterministic Policy Gradient (HyRO-DDPG) algorithm is proposed to solve the problem. Simulation results demonstrate that the proposed solution effectively enhances delay control performance and energy efficiency for the VLC-based IIoT systems.

Mirror array-based intelligent reflecting surfaces (mIRS) offer a promising solution to line-of-sight (LoS) blockage in optical camera communication (OCC). However, since the physical size of individual mIRS mirrors is smaller than the imaging field of view, reflected optical signals appear as discrete, fragmented stripe segments on the image sensor. These fragmented signals cannot be directly demodulated, constituting a primary challenge for mIRS-aided OCC. This Letter proposes a Geometric-Signal Reconstruction (GSR) scheme, which integrates an Overlapping Beam Alignment (OBA) algorithm for mIRS orientation configuration and a Correlation Sequence Stitching (CSS) algorithm for fragmented signal integration. We report the first, to the best of our knowledge, experimental implementation of an mIRS-aided OCC system. Experimental results verify that the proposed GSR scheme successfully restores blocked links with a bit error rate (BER) of 10 ⁻⁴ and significantly enhances physical layer security through spatial selectivity.

In this paper, we investigate task offloading and computing in an urban mobile edge computing (MEC) system assisted by an aerial intelligent reflecting surface (AIRS). To enhance information freshness while saving energy, we formulate a joint optimization problem to minimize the weighted sum of average age of information (AoI) and total energy consumption by jointly optimizing task offloading decisions, resource allocation, and AIRS configuration including its deployment position, phase shifts, and panel size, subject to offloading quality and computing deadline constraints. To tackle the resulting mixed-integer and nonconvex problem, we develop a hierarchical optimization framework based on the objective priority and variable coupling relations. Under this framework, the problem is solved in two stages using quadratic penalty, numerical analysis, and convex optimization techniques. Specifically, an AoI-aware task offloading policy is first designed to maximize information freshness; subsequently, given the obtained offloading policy, an AIRS configuration and resource allocation scheme is proposed to minimize energy consumption. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes in reducing both AoI and energy consumption, while exhibiting superior convergence performance.

The stochastic terminal rotations and random link blockages in visible light communication (VLC) systems can significantly degrade statistical delay performance. To address this, this letter aims to mathematically characterize the statistical delay for VLC systems. First, we propose a Markov-modulated Laplace process (MMLP) service model capturing burstiness from orientation dynamics and blockages. Then, we analytically derive the probability density function of the achievable transmission rate under dynamic channel conditions. Finally, we establish closed-form delay bounds using a unified exponential supermartingale construction. Monte Carlo simulations validate our bounds’ superior accuracy over traditional effective bandwidth/effective capacity methods, particularly in high-burstiness scenarios. Specifically, the proposed bound achieves a delay violation probability from 10-2 to 10-3 at a delay threshold of 1 ms, which meets the stringent requirements of delay-sensitive applications.

To accurately detect signals in multiple-input multiple-output visible light communication (MIMO-VLC) systems, this paper proposes an expectation propagation (EP)-based decision feedback equalization (EP-DEF) algorithm. A free space optical channel model incorporating signal-dependent noise (SDN) for MIMO-VLC systems, and a constellation design method combining uniform modulation and square root modulation are applied. In this work, we first present the EP-DFE using both the a posteriori moments of estimated symbols and the a priori moments of unestimated symbols, and then two fast recursive schemes are developed to reduce the computational complexity. To further reduce the error propagation, the serially connected bidirectional EP-DFE (SC-Bi-EP-DFE) is introduced, which utilizes the output of the forward EP-DFE as the input of the backward EP-DFE. Finally, with considering the channel estimation error (CEE), the new autocorrelation and the new covariance matrix of received vectors are derived to obtain the equalizer coefficients. Simulation results show that the proposed SC-Bi-EP-DFE provides good performance at an acceptable computational cost, especially for the degraded channels. With the CEE, our proposed method can achieve significant performance improvement without increasing complexity. Therefore, it is a promising candidate for application in practical MIMO-VLC systems.

This letter addresses the physical-layer security (PLS) challenge in multi-input multi-output (MIMO) visible light communication (VLC) systems. Existing solutions often overlook VLC-specific constraints and fail to jointly optimize key parameters. To overcome this, we propose an optical intelligent reflecting surface (OIRS)-assisted tri-level optimization framework that simultaneously handles power allocation, OIRS assignment, and orientation control. A block coordinate descent approach incorporating convex approximation, greedy heuristics, and metaheuristic search is developed. Simulation results confirm significant secrecy rate improvement and robustness under various conditions and imperfect channel knowledge.

Traditional wireless positioning techniques often suffer from accuracy degradation in indoor environments due to multipath effects and occlusion. To address this issue, this paper proposes an indoor positioning method for visible light communication (VLC) combined with intelligent reflective surface (IRS) assistance to improve the positioning accuracy and stability in complex environments. This work proposes the concepts of a virtual source and virtual receiver based on IRS and conducts positioning optimization by combining the measurements of the time difference of arrival (TDOA) and angle of arrival (AOA). The research adopts a semi-positive definite relaxation (SDR) optimization method to efficiently solve the nonlinear optimization problem, ensuring the global convergence and accuracy of the algorithm. Meanwhile, the weights of the positioning results of the virtual light source and the real light source are dynamically adjusted by using the distance residual, thereby reducing the influence of measurement noise. Monte Carlo simulation experiments demonstrate the advantages of the proposed method in terms of the positioning error and robustness compared with traditional positioning algorithms in the environment of large noise interference. The experimental results demonstrate the efficacy of the method in addressing multipath and occlusion issues, while also exhibiting notable adaptability and stability across diverse hardware configurations.