# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 2 Related Work  
Channel estimation in wireless sensor networks (WSNs) has been extensively explored using various methods, including traditional, machine learning (ML), and deep learning (DL) approaches. Each offer distinct benefits but also face specific challenges, particularly in hybrid visible light communication-radio frequency (VLC-RF) environments. A distributed blind equalization method was proposed in [12], where local outputs are optimized based on channel quality, offering resilience to nonlinear distortions. Building on this, [PERSON] et al. [13] introduced a signal-power-based weight combination rule to further refine blind equalization performance. Despite these improvements, both approaches assume homogeneous signal types and centralized learning, limiting their applicability in heterogeneous VLC-RF WSNs. For low-power environments, [PERSON] et al. [14] developed a simplified log-likelihood ratio (LLR) estimation under Rayleigh fading. Their approach is computationally efficient but remains narrowly tailored to specific fading and modulation types, restricting its generalization to broader hybrid channel scenarios. To address mobile jamming, [PERSON] et al. [15] proposed a blind channel estimation technique for UAV-assisted WSNs. While effective in dynamic aerial scenarios, the method's reliance on UAV mobility and scenario-specific configurations reduces its suitability for static WSN deployments. A channel access mechanism was presented in [16] to enhance throughput and fairness. This was extended in a follow-up study [17], where [PERSON] et al. employed a game-theoretic fairness model to optimize resource allocation. Although these methods successfully improve access efficiency, they primarily address medium access control rather than the precision of channel estimation under nonlinear channel behaviors. [PERSON] et al. [18] introduced a fractional-order diffusion-based estimation approach, which theoretically accelerates convergence. However, its high computational demands make it less practical for embedded WSN nodes with limited resources. In [19], [PERSON] and [PERSON] proposed a variance-based cooperative estimation scheme that dynamically triggers updates based on channel variations, improving energy efficiency. Nevertheless, their method targets RF-only systems and lacks responsiveness in dual-modality hybrid networks. More recently, [PERSON] et al. [20] applied Kalman and extended Kalman filtering for RIS-assisted channel tracking under pilot contamination and hardware impairments. While effective under mobility, RIS-based solutions still face deployment challenges, particularly in cost-sensitive WSN scenarios. With the increasing complexity of channel behaviors, ML-based methods have emerged to address nonlinear estimation through data-driven approaches. Recent ML efforts also include shallow ANN architectures for signal-dependent noise mitigation in VLC, targeting low-complexity channel refinement [21]. In [22], two ANN models were proposed for VLC-based spatial modulation under mobility and random receiver orientation, outperforming traditional interpolation methods in CSI prediction. A decision tree-based interference mitigation scheme in [9] was proposed for lightweight WSN deployments. While demonstrating resource efficiency, this method remains confined to RF systems and does not incorporate VLC characteristics. An unsupervised learning model for VLC-based environmental monitoring was introduced by [PERSON] et al. [23], capturing spatial characteristics effectively, though limited to static object detection. In a related work, [PERSON] et al. [24] employed a random forest approach for VLC-based object classification. Although accurate, this approach focuses primarily on semantic interpretation rather than physical-layer channel estimation. ML techniques were also applied to ATSC 3.0 systems for dynamic vehicular environments by [PERSON] et al. [25], offering strong adaptive capabilities.