理想室内的可见光通信（VLC）信道传输模型并不适用于实际的不规则室内环境，针对这一问题，本文研究了可见光在不规则场景中无规律反射的情况，建立了更符合实际情况的VLC信道模型，并在此信道模型基础上提出了遗传算法优化反向传播（BP）神经网络（GA-BP）的定位算法，克服了BP神经网络存在处理非线性系统能力差的问题。通过仿真不规则场景下信道模型的反射元法向量信息，确定光线反射方向，使接收器可以接收到更加准确的光功率值。仿真结果表明，在5 m × 5 m × 5 m的不规则室内环境中，系统总接收光功率在0.0141~0.0639 W范围内波动，GA-BP算法较BP神经网络定位误差大幅减小，达到2.32 cm，平均定位时间为0.0625 s。

The ideal indoor visible light communication (VLC) channel transmission model is typically unsuitable for an actual irregular indoor environment. To address this issue, this study investigates the irregular reflection of visible light from irregular scenes and establishes a more realistic VLC channel model; further, based on this model, it develops a genetic algorithm-optimized back propagation (BP) neural network positioning (GA-BP) algorithm to overcome the poor performance of the BP neural network in handling nonlinear systems. Herein, by simulating the reflection element normal vector information of the actual channel model in irregular scenes, the direction of light reflection can be determined, enabling the receiver to collect more accurate optical power values. In an irregular indoor environment with dimensions of 5 m × 5 m × 5 m, the simulations reveal that the total optical power received by the system fluctuates within a range of 0.0141-0.0639 W; additionally, compared with the BP neural network, the GA-BP algorithm achieves a significantly reduced positioning error of 2.32 cm, along with an average positioning time of 0.0625 s.