可调制光学IRS辅助无蜂窝VLC网络的接入资源管理算法

贾林琼; 冯事成; 乐淑娟; 施唯; 束锋

doi:10.11999/JEIT240710

可调制光学IRS辅助无蜂窝VLC网络的接入资源管理算法

doi: 10.11999/JEIT240710

1.
南京理工大学电子工程与光电技术学院南京 210094
2.
海南大学信息与通信工程学院术学院海口 570228

基金项目: 国家自然科学基金(62301257)，江苏省基础研究计划(自然科学基金)(BK20200488)

详细信息

作者简介:
贾林琼：女，博士，副教授，研究方向为可见光通信、智能反射面和人工智能

冯事成：男，研究方向为可见光通信和人工智能

乐淑娟：女，研究方向为可见光通信

施唯：女，研究方向为可见光通信

束锋：男，博士，教授，研究方向为无线通信、物理层安全和新一代智慧网络等

通讯作者:
贾林琼　jialinqiong@njust.edu.cn

中图分类号: TN926
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- 被引次数: 21
出版历程
- 收稿日期: 2024-08-15
- 修回日期: 2024-12-27
- 网络出版日期: 2025-01-09
- 刊出日期: 2025-02-28

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

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)

摘要

摘要: 该文研究了一种基于新型光学可调制智能超表面(IRS)辅助的无蜂窝可见光通信(VLC)网络接入方案，其中IRS可以为收发端提供额外的反射信道，也可以利用反射系数可调制的特性，直接为网络用户提供无线接入。该文建立了可调制IRS辅助的无蜂窝VLC接入网络的系统模型，推导了网络吞吐量与发光二极管(LED)照明通信设备的工作模式、IRS的工作模式和用户接入关联之间的关系，并提出以最大化网络吞吐量为目标的接入优化问题。该优化问题分两步求解：(1) 当调制模式的LED数和调制模式的IRS数给定时，基于深度确定性策略梯度(DDPG)的深度强化学习(DRL)算法可以得到最优的接入点工作模式和用户接入关联策略；(2) 遍历可能的调制LED数和调制IRS元件数即可得到优化问题的解。仿真结果表明，联合优化接入点的工作模式和用户接入关联矩阵可以提高IRS辅助无蜂窝VLC网络的吞吐量。
- 可见光通信 /
- 光学智能反射面 /
- 接入资源管理 /
- 最大化吞吐量 /
- DDPG算法
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.
- Visible Light Communication (VLC) /
- Optical intelligent reconfigurable surface /
- Resource management /
- Throughput maximization /
- Deep Deterministic Policy Gradient (DDPG) algorithm.

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图 1 可调制IRS辅助无蜂窝VLC 网络示意图

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图 2 可调制IRS辅助无蜂窝VLC 网络系统模型图

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图 3 基于DDPG的接入资源分配算法示意图

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图 4 DDPG-O和DDPG-N算法的收敛图

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图 5 不同的调制LED数对应的最大吞吐量

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图 6 不同IRS元件数对应的网络最大吞吐量

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图 7 接入方案分析

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1 DDPG-O：基于DRL的可调制IRS辅助无蜂窝VLC网络接入参数优化

1. 初始化：Actor网络、Critic网络、target-Actor网络、target- 　Critic网络的参数和梯度
初始输入：状态 ${s_0}$ 、用于调制的LED数目 $M'$ 、用于反射的　IRS数目 $K'$
输出：系统用户的和速率及对应的最优策略 $\{ {\boldsymbol{L}},{\boldsymbol{I}},{\boldsymbol{G}},{\boldsymbol{F}}\}$
2. For episode $\in$ episodes do：
3. 　从Replay Buffer中随机抽取初始状态 ${\boldsymbol{{s}}_t}$ ，若Replay 　　　Buffer未准备好则采用 ${{\boldsymbol{s}}_0}$ ；初始化set=0；
4. 　For t $\in$ Max steps do：
5. 　　根据当前的状态 ${{\boldsymbol{s}}_t}$ ，Actor网络基于当前的策略　　　　 $\pi ({{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t})$ 输出动作 ${{\boldsymbol{a}}_t}$
6. 　　if set < 0：
选择高斯噪声 ${{\boldsymbol{N}}_1}(0,\sigma _1^2)$ ，与动作 ${{\boldsymbol{a}}_t}$ 叠加　　　　　 ${{\boldsymbol{a}}_t}^\prime = {{\boldsymbol{a}}_t} + {{\boldsymbol{N}}_1}$
else：
选择高斯噪声 ${{\boldsymbol{N}}_2}(0,\sigma _2^2)$ ，与动作 ${{\boldsymbol{a}}_t}$ 叠加　　　　　 ${{\boldsymbol{a}}_t}^\prime = {{\boldsymbol{a}}_t} + {{\boldsymbol{N}}_2}$
7.　　根据动作 ${{\boldsymbol{a}}_t}^\prime$ ，与环境交互，获得奖励 ${r_t}$ 、下一时刻状态　　　　 ${{\boldsymbol{s}}_{t + 1}}$
8.　　if ${r_t}$ < 0，以概率 $\varsigma$ 将其储存到Replay Buffer；else 直接存　　　　入Replay Buffer
9.　　若Replay Buffer准备好，抽取Batch size个元组　　　　 $({{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t},{{\boldsymbol{s}}_{t + 1}},{r_t})$ 使智能体进行学习，通过梯度反向传播　　　　更新Actor网络和Critic网络的参数；若未准备好则只存储　　　　本次获得的元组 $({{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t},{{\boldsymbol{s}}_{t + 1}},{r_t})$ 。
10.　软更新target-Actor网络参数、target-Critic网络的参数
11.　计算近 $\eta$ 次与环境交互获得的奖励 $\bar r$ ， ${\mathrm{set}} = \bar r$
12.　 ${{\boldsymbol{s}}_t} = {{\boldsymbol{s}}_{t + 1}}$
13. end for
14. end for

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表 1 系统模型仿真参数列表

参数	值	参数	值
LED个数	$M = 4$	朗伯系数	$m = 1$
IRS个数	$K = 16$	PD视场角	${\text{FoV}} = {70^ \circ }$
PD个数	$N = 5$	增益函数	$g = 1$
调制LED个数	$0 \le M' \le M$	内部反射常数	${n_{\mathrm{r}}} = 1.5$
调制IRS个数	$0 \le K' \le K$	频带宽度	$W = 2 \times {10^8}\;{\text{Hz}}$
PD面积	$1\;{\text{c}}{{\text{m}}^2}$	调光功率	$A = 5$
最大反射系数	$\alpha = 0.9$	调光系数	$\xi = 0.5$
噪声功率	${\sigma ^2} = 1 \times {10^{ - 21}}$	PD响应率	$\rho = 0.5$

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表 2 DDPG-O算法参数设置

参数	值	参数	值
BufferSize	100 000	噪声系数1	${\sigma _1} = 0.15$
Batchsize	$B = 32$	噪声系数2	${\sigma _2} = 0.08$
隐藏层神经元数目1	${H_1} = 880$	价值衰减常数	$\gamma = 0.98$
隐藏层神经元数目2	${H_2} = 600$	策略网络学习率	${l_{{\mathrm{Policy}}}} = 1 \times {10^{ - 3}}$
策略网络深度	${D_P} = 1$	价值网络学习率	${l_{{\mathrm{Critic}}}} = 1 \times {10^{ - 2}}$
值网络深度	${D_C} = 2$	软更新常数	$\tau = 0.000\;01$
丢弃率	$\zeta = 0.85$	仿真周期	$E = 1\;000$
噪声切换长度	$\eta = 6$	最大步数	$s = 100$

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