# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 5 Advancements in Optical Wireless Communications
### Vehicular Visible Light Communication (V\({}^{2}\)Lc)
Regardless of the discrepancies between the two systems, the existing cell-based handover algorithms cannot be extended to V\({}^{2}\)LC. A distance-based probabilistic algorithm is proposed in [325], which considered both the signal quality and missing handover rate to select the switching time. The proposed algorithm provides a high-quality signal, with a guaranteed missing rate of transmission. MS Demir et al. introduced a hierarchical soft transfer technique for V\({}^{2}\)LC networks based on a coordinated multipoint (CoMP) transmission that dynamically revises the time-to-trigger (TTT) and handover margin (HOM) values based on the rate of change in the power obtained. The simulation results show that the proposed approach outperforms traditional hard handover and CoMP, while preserving signal quality regardless of vehicle speed [326]. Safety concerns and the reliability of the connection is very important in V\({}^{2}\)LC communications. Since V\({}^{2}\)LC has limited applications due to range limitation, there is a need for integration with an RF system. The most promising RF technology for vehicle communications is RF-based dedicated short-range communication (DSRC). DSRC uses the 5.9 GHz radio band to enable V\({}^{2}\)C with increased network efficiency. Therefore, combining V\({}^{2}\)LC with RF systems improves link range, QoS, and the efficiency of the traffic/vehicle communication system. These hybrid communication systems find advantages in high traffic density, updating the vehicles' positions and health, and multi-hop communication between vehicles [327; 328]. Across the development of mobile networks including 5G/6G and beyond, more interest is seen in networking for various environments including V\({}^{2}\)LC, RF, and millimeter wave (mmW) systems. A hybrid VLC/mmW antenna was designed and implemented to function at 20.8 GHz [329]. To achieve data rates of 5 to 100 Mbps using V\({}^{2}\)LC and mmW connections, the authors employed OOK and QAM schemes with different orders. The authors evaluated the efficiency of each modulation technique by calculating the BER and the magnitude of the error vector for both RF and V\({}^{2}\)LC connections. The findings show that the proposed hybrid planar antenna for V\({}^{2}\)C was well suited [329]. The advancement of transportation networks makes regular vehicle service safer, greener, and more efficient, and paves the way for fully automated vehicles with the advent of the 5G/6G cellular system and beyond. As an AI branch, machine learning (ML) or deep learning (DL) seeks a way to apply a data-driven approach to solving many conventional problems in vehicular networks. The emerging vehicular system with ML/DL algorithms will become an essential component to develop the ITS and smart cities. ML/DL provide powerful methods for analyzing large quantities of data by exploring patterns and processing them. To extract and utilize data generated from multiple sources in vehicular networks, ML/DL provides a multi-skilled set of tools. This keeps the system more informed and makes data-driven decisions, diminishes communications challenges, and offers distinctive services such as real-time traffic control, location-based services, and autonomous driving. The adaptation and use of these ML/DL methods to support vehicle networks is a challenging and promising direction for research [330]. DL for optical vehicular communication was introduced in [331]. The authors considered various technical issues involved in vehicular communication systems with V2 LC technology. The authors implemented an IEEE 802.15.7-2018 hybrid spatial phase-shift keying (HSPSK) for the V\({}^{2}\)LC system as a standard hybrid modulation technique. To improve real-time region-of-interest (RoI) tracking, the authors applied the \"you only look once version 2\" object detection technique. The proposed novel AI-based error correction and neural-network-based decoder enhanced the accuracy of data decoding and SNR required for achieving a 10\({}^{-4}\) BER in a V\({}^{2}\)LC system.