Joint Resource Management for Tunable Optical IRS-aided Cell-Free VLC Networks

JIA Linqiong; FENG Shicheng; LE Shujuan; SHI Wei; SHU Feng

doi:10.11999/JEIT240710

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JIA Linqiong, FENG Shicheng, LE Shujuan, SHI Wei, SHU Feng. Joint Resource Management for Tunable Optical IRS-aided Cell-Free VLC Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240710

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JIA Linqiong, FENG Shicheng, LE Shujuan, SHI Wei, SHU Feng. Joint Resource Management for Tunable Optical IRS-aided Cell-Free VLC Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240710

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Joint Resource Management for Tunable Optical IRS-aided Cell-Free VLC Networks

doi: 10.11999/JEIT240710

1.
School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China
2.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China

Funds: The National Natural Science Foundation of China (62301257), The Natural Science Foundation of Jiangsu Province (BK20200488)

Received Date: 2024-08-15
Rev Recd Date: 2024-12-27

Available Online: 2025-01-09

Abstract

Abstract

Objective Visible Light Communication (VLC) is emerging as a key technology for future communication systems, offering advantages such as abundant and license-free spectrum, immunity to electromagnetic interference, and low-cost front-end devices. Light Emitting Diodes (LEDs) serve a dual purpose, providing both communication and illumination in indoor environments. However, VLC links are vulnerable, as the interruption of the Line of Sight (LoS) can disrupt communication. The Optical Intelligent Reconfigurable Surface (IRS) has been proposed to enhance communication performance and robustness by reconfiguring optical channels. Two main types of optical IRS materials, mirror-based and meta-surface-based, are commonly used. Mirror-based IRS units introduce additional Non-LoS (NLoS) links with constant reflectance.A cell-free VLC network with the assistance of a newly proposed tunable IRS is proposed and fully investigated. The reflectance of the optical IRS can be dynamically adjusted, allowing it to function as a transmitter by modulating signals on the reflectance with stable incident light. In this system, at least one LED must operate in illumination mode to emit light with constant intensity when any IRS unit is in modulation mode. The IRS can also function in reflection mode to provide additional reflective links, enhancing signal strength. The tunable IRS increases the number of Access Points (APs), enabling ultra-dense VLC networks that significantly improve throughput and spectral efficiency. The system model for a tunable IRS-assisted cell-free VLC network is derived, and the channel gain is calculated using the Lambertian model. The transmission rate for each user is determined by the work mode of the APs and the IRS’s association with the LEDs and users, represented by binary variables. The primary objective of this study is to maximize the total throughput of the IRS-aided VLC network. Methods An optimization problem is formulated to maximize network throughput by jointly optimizing the work mode of the LEDs and IRS units, along with user-IRS associations. Given the non-convex nature of this integer optimization problem, it is decomposed into two sub-problems. (1) Problem P2: With fixed numbers of LEDs and IRS units in modulation mode, a Deep Deterministic Policy Gradient (DDPG)-based Deep Reinforcement Learning (DRL) algorithm is applied to optimize the work mode of each AP and the user-AP associations. The binary variables are relaxed to continuous values in the range [0,1]. The optimization problem is modeled as a Markov Decision Process (MDP), where the state corresponds to the channel gains, the action represents the optimization variables, and the reward is the network throughput. To ensure convergence, the reward is adjusted to reflect the negative of any unsatisfied constraints, and the noise in the DDPG model is dynamically modeled using two random variables. (2) Problem P1: The optimization problem is then solved by considering all possible combinations of the number of LEDs and IRS units in modulation mode. Results and Discussions Simulations for the indoor tunable IRS-aided system are performed using Python with PyTorch. The simulation parameters for the indoor scenario and the neural network configurations in the DDPG algorithm are shown (Table 1, Table 2), respectively. The results demonstrate the following: (1) The convergence and final reward of the modified DDPG algorithm (denoted as DDPG-O) are compared with the unmodified version (denoted as DDPG-N) in solving Problem P2 (Fig. 4). The results show that the modified DDPG algorithm converges efficiently and achieves an access and association policy that maximizes network throughput. (2) The maximized throughput for various numbers of LEDs in modulation mode, along with varying optical power, is presented when solving Problem P1 (Fig. 5). It is observed that the policy with one lighting LED achieves the maximum throughput with appropriate IRS units in modulation mode. (3) The relationship between maximized throughput and the number of IRS units is analyzed in (Fig. 6). The total throughput increases as the number of IRS units grows, although the increase is not linear. (4) Simulations with the same number of users and LEDs are also considered (Fig. 7). It is observed that the total network throughput with and without IRS APs is nearly identical when the number of users does not exceed the number of LEDs. Thus, the VLC network benefits more when the number of users exceeds the number of LEDs. Conclusions A tunable IRS-assisted cell-free VLC network has been proposed, where IRS units either operate in reflection mode to provide additional NLoS channels or in modulation mode to enable wireless access for users. The channel and transmission models are developed, and an optimization problem is formulated to jointly select the working mode of APs and user associations with the objective of maximizing network throughput. A modified DDPG algorithm is applied to solve for the optimal policy. The optimization problem is further tackled by exploring all possible combinations of modulating LEDs and IRS units. Simulation results verify the effectiveness of the proposed algorithm, showing that the network throughput can be significantly improved by incorporating IRS APs, particularly when the number of users is large.

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