# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 2 Related Work
However, their method demands high-speed computational resources, which are unsuitable for constrained WSNplatforms. [PERSON] et al. [26] utilized RF fingerprinting for interference-resilient industrial IoT networks, enhancing security, yet leaving real-time hybrid VLC-RF estimation unaddressed. In [27], [PERSON] and [PERSON] implemented hybrid random forest and deep learning models for urban vehicular propagation modeling, achieving high accuracy but requiring complex model orchestration. Similarly, [PERSON] [28] applied random forests to underwater optical demodulation. Although effective for nonlinear optical environments, the method is contextually restricted to underwater scenarios. [PERSON] et al. [29] provided a comprehensive review of ML and DL methods aimed at enhancing WSN security. While highlighting the intersection between channel estimation and attack resilience, the review focused primarily on security aspects rather than on hybrid estimation adaptability. In [30], [PERSON] et al. extended support vector regression (SVR) to VLC systems by introducing a time-delay twin SVR model, improving the predistortion accuracy for LED nonlinearities, though remaining limited to optical domains. Similarly, [PERSON] et al. [31] employed SVMs for signal detection in generalized spatial modulation (GSM) VLC systems, achieving improved BER performance at lower complexity compared to maximum likelihood detection; however, their focus remained on detection rather than channel estimation. Finally, [PERSON] et al. [32] proposed a tapped delay line (TDL) model combined with ML-based estimation for environmental awareness. While promising for hybrid-aware sensing, the current application remains limited to static inference scenarios. Deep learning techniques have pushed further into complex nonlinear modeling. [PERSON] and [PERSON] [33] developed a DNN-based OTFS transceiver capable of addressing delay-Doppler and IQ imbalance issues, though its high processing demands limit practical deployment in WSNs. In [34], a multitask CNN model was proposed to estimate multiple channel parameters simultaneously in vehicular contexts. Although offering strong generalization capabilities, the model requires significant inference time and large data volumes. Similarly, [PERSON] et al. [35] advocated the use of RNNs for capturing time-variant wireless propagation, which is effective for sequence modeling but challenged by training overhead and memory constraints. [PERSON] et al. [36] introduced RadioNet, a Transformer-based model designed for radio map prediction in dense urban environments. While it demonstrates strong performance in capturing long-range dependencies and handling multipath effects, the model's high computational complexity and limited interpretability present challenges for deployment in resource-constrained edge wireless sensor network (WSN) applications. [PERSON] et al. [37] utilized deep learning for sparse channel estimation and hybrid precoding in mmWave MIMO systems, successfully reducing pilot overhead but still being dependent on pre-training and centralized learning schemes. Attention mechanisms were incorporated by [PERSON] et al. [38] to enhance feature focus under noisy conditions, yet such networks carry substantial computational burdens. He and [PERSON] [39] proposed a cascaded DL model for intelligent metasurface-assisted MIMO, effectively handling passive element nonlinearities, but it is tailored for specialized infrastructure beyond typical WSN nodes. [PERSON] et al. [40] employed autoencoders to mitigate nonlinear distortions in high-power amplifiers, though the approach requires precise parameter tuning and large-scale training datasets. [PERSON] et al. [41] focused on frequency-selective DL-based estimation for hybrid MIMO systems, achieving strong performance in dynamic channels but facing challenges for real-time low-power sensor nodes. [PERSON] et al.