Channel State Information Restoration and Positioning of massive Multiple Input Multiple Output Integrated Visible Light Communication and Sensing System
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摘要: 得益于丰富的频谱和光源，可见光通信感知一体化(IVLCP)系统为解决高性能通信定位的室内无线网络需求提供强有力的技术支撑。同时，大规模多输入多输出(m-MIMO)技术能有效提高IVLCP网络的服务范围和质量。然而，m-MIMO赋能的IVLCP网络的信道环境更加复杂且先验信息更易变化，这使得传统方法难以快速准确地完成信道估计和定位。针对此，该文提出一种信道状态信息还原和定位(CSIRP)网络，该网络不仅能够有效地捕捉复杂分布的可见光通信信道特征，同时能够应对信道状态的时变性，从而提高信道和位置估计的鲁棒性和动态适应性。具体而言，CSIRP网络首先基于条件生成对抗思想自适应训练生成器和鉴别器，进而实现根据接收信号进行信道估计，接着结合长短期记忆网络(LSTM)从估计的信道中获取接收终端的位置估计。仿真结果表明，采用CSIRP网络所获得的信道状态准确度和定位精度均优于现有的深度学习参考方法，这为m-MIMO赋能的IVLCP系统提供了可靠和精准的信道状态信息和位置感知能力。Abstract: Benefiting from rich spectrum and lamps, Integrated Visible Light Communication and Positioning (IVLCP) systems provide powerful technological solution to meet the high performance communication and positioning in indoor wireless networks. Meanwhile, the massive Multiple Input Multiple Output (m-MIMO) effectively enhance both service coverage and quality of IVLCP systems. However, the channel environment is more complex and the priori information rapidly changed in the m-MIMO-enabled IVLCP systems, making traditional methods challenging for fast and accurate channel estimation and positioning. In order to tackle this challenge, a Channel State Information Restoration and Positioning (CSIRP) network is proposed in this paper. The network not only effectively captures complex distribution feature of channel but also addressing the temporal variations in location, thereby enhancing the robustness and dynamic adaptability of channel and location estimation. Specifically, the CSIRP network employs a conditional generative adversarial process to adaptively train the generator and discriminatorr and thus achieves the channel estimation from received signals. Then, the Long Short-Term Memory(LSTM) is introduced to estimate the location of the receiver from the estimated channel. Simulation results demonstrate that the accuracy of both channel and location estimation achieved by the proposed CSIRP network outperforms existing deep learning benchmark schemes. This provides m-MIMO-enabled IVLCP systems with more reliable and accurate channel state information and positioning.
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表 1 所考虑场景的参数设置
参数 名称 数值 参数 名称 数值 ${N_{\text{r}}}$ PD阵列 64 ${\phi _{{\text{oc}}}}$ PD的视场角 70° $\tau $ 导频序列长度 8 ${\beta _{{\text{semi}}}}$ 半功率角 60° ${A_{\text{r}}}$ PD的有效光电接收区域面积 1 cm2 $ n $ OC的折射率 1.5 
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表 2 网络模型性能比较
网络模型 模型深度
(层)平均定位
误差(cm)收敛时的
RMSE
(×10–2)收敛时的
损失(×10–2)CSIRP 28 5.29 1.59 0.02 FFDNet-LSTM 24 14.28 4.71 1.09 CGAN-DNN 25 23.72 8.13 3.78 CGAN-CNN 31 125.74 84.28 25.26
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