# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 5 Challenges and Future Directions for Hybrid VLC/RF Networks
The design of hybrid communication channels for VLC/RF technologies is considered a major challenge due to the marked differences in the communication channel of each network. The channel model, modulation and propagation techniques, data rate, transmission capacity, and coverage are among of the characteristics that differentiate VLC and RF networks [45]. Despite this, because VLC and RF networks do not interfere with each other it is possible to design a common channel for both technologies. The correct selection of the modulation techniques of the networks and the synchronization between the channels available for the transmission of both technologies is fundamental to optimizing the design of a hybrid channel [46]. Although several channel designs have been proposed for hybrid VLC/RF networks, it remains necessary to optimally design a hybrid channel model that integrates VLC and RF technologies and takes advantage of the benefits provided by each channel independently. A technical concern of hybrid VLC/RF networks is to achieve efficient load balancing. The main question is how to allocate users between VLC access networks and RF femtocells. The first step to finding an answer to this question involves solving the problem of joint association and resource allocation [50]. Although several authors have investigated these problems [53; 55; 56; 57; 59], several parameters remain to be solved before efficient load balancing can be achieved. It is necessary to delve deeper into issues such as the fair distribution of user load; dynamic allocation algorithms could be developed to ensure this balance [51]. In addition, it is necessary to develop load-balancing algorithms that analyze the state of each base station and dynamically assign users to base stations with less load. This can help to avoid congesting the stations and make the best use of the network resources [53]. During a call session a load balancing mechanism must be performed periodically, and users may need to switch to an AP with better service if they are on the move [70]. Due to the particular characteristics of each technology, it is essential to develop new modulation and coding schemes to improve the performance of hybrid VLC/RF networks [181]. While traditional RF networks have limitations such as electromagnetic interference and signal attenuation, VLC networks are not subject to these limitations and can offer higher transmission rates. However, their sensitivity to physical obstacles and variations in light intensity are relevant challenges. It is necessary to develop modulation and coding schemes to overcome these limitations of hybrid RF/VLC networks. These schemes have to mitigate the adverse effects of interference and attenuation on data transmission in order to improve the quality and reliability of optical communication [182]. These new schemes should be able to adapt to environmental conditions and optimize spectral efficiency to take full advantage of the available bandwidth [183]. Resource allocation and handover are essential to transmit large volumes of data quickly and reliably in hybrid VLC/RF networks [53]. These algorithms are crucial to the effective and balanced allocation of transmission resources such as bandwidth, transmitting power, and processing capacity. Moreover, they ensure continuous and uninterrupted transmission by managing the handover of data between VLC and RF technologies. Resource allocation and handover have a direct impact on the quality of service offered to users by maximizing network capacity and avoiding congestion. In addition, proper resource allocation helps to reduce energy consumption and costs associated with data transmission. Although several algorithms have been proposed for hybrid VLC/RF networks [26; 60; 66], there is a need to design new algorithms that optimize resource usage and decrease handover latency between networks. The use of tools such as artificial intelligence can be of great help in the evolution towards more efficient algorithms.