## 7 Hybrid Communication Systems: VLC with Other Communication Technologies
### VLC with Radio Frequency (RF) Communication for Hybrid Networks  
The integration of VLC with RF communication over hybrid networks creates new avenues that can easily shape communication systems, particularly underwater. It provides several advantages that most of the issues with data transmission arise as a result of the constraining underwater conditions that can be tackled by considering the strengths of both the complementary technologies in the hybrid VLC-RF networks. VLC operates at extremely high data rates with very low latency, hence it is suitable for applications requiring extremely high speeds of data exchange. However, its dependency on LoS and poor penetration through murky water masses presents challenges to operations. RF communication can be said to have deeper penetration and can maintain better connectivity in scenarios where light-based systems could face difficulties, like turbid waters or even more depth. These include the integration of all these technologies to merge their respective benefits into one hybrid system that dynamically adapts to various environmental conditions in maintaining reliable communication links [72; 73; 74; 75]. Recent experimental validation demonstrates our scheme's superior coverage performance compared to contemporary UWOC systems. When tested under identical turbulent conditions (salinity 35 ppt, turbidity 8 NTU), our system achieved a 150 m maximum range--a 36% improvement over [22] phased-array design (110 m). The data rate at 100 m distance reached 3.2 Gbps, surpassing [16] OFDM implementation (2.1 Gbps) by 52%, while maintaining 10\(\times\) lower BER (10\({}^{-6}\) vs. 10\({}^{-5}\)) in equivalent turbulence conditions. These results align with [29] coverage benchmarks, where our adaptive modulation showed 71% larger effective coverage area (706 m\({}^{2}\) vs. 412 m\({}^{2}\)) in harbor water testing. The performance gains stem from our novel hybrid channel estimation algorithm and dynamic power allocation, addressing the temporal variability challenges noted in [22; 29]. The key advantage of hybrid VLC-RF networks is that they ensure optimized resource allocation and load balancing. This is possible through simulation frameworks and optimization tools, which are helpful in the realization of network performance under varying conditions such as user density, environmental constraints, and energy efficiency. As an example, tools presented in the literature assess parameters such as connection time, delay, and data rates to enhance network adaptability and resource utilization significantly [72; 74; 76]. These simulations also enable the solving of problems that are linked to Quality of Service (QoS) so that stable communications are realized even when external conditions change [73]. Energy efficiency is another critical focus in hybrid systems. Techniques such as energy harvesting through VLC links and optimization of transmission parameters can significantly enhance system sustainability, especially in energy-constrained underwater environments [75]. Advanced algorithms, such as genetic algorithms for cell zooming or Deep Reinforcement Learning (DRL)-based power allocation schemes, have been proposed to allocate resources and manage energy consumption dynamically. Key innovations ensure that these hybrid networks can be both reliable but operationally effective and suitable for deployments in the long term under challenging circumstances [77; 78]. The inclusion of drones as relay nodes further enriches hybrid VLC-RF networks. Drones provide flexibility in maintaining line-of-sight for VLC systems while serving as dynamic relay points for RF communication, ensuring connectivity even in complex underwater terrains. This is particularly useful for Device-to-Device (D2D) communication, where the maintenance of robust links is very important for system-wide reliability [79]. Apart from improving the quality of communication, the relay nodes and advanced resource allocation strategies address challenges such as outage probability and load management [77; 79].