# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 5 Advancements in Optical Wireless Communications
### Vehicular Visible Light Communication (V\({}^{2}\)Lc)
Bastien Bechadergue proposed a V\({}^{2}\)LC-based system that uses the headlamps and taillights of a vehicle to simultaneously perform range-finding and V\({}^{2}\)C. In dense traffic scenarios, as shown in Figure 35b, such as platooning, the proposed approach works by interchanging a clock signal accommodated in Manchester-encoded signals. By using phase-shift calculations, the leading and trailing vehicles may exchange information and compute the distance between them. The authors made a Simulink model and tested it with a 500 kbps range-finding system with a BER of \(\approx\)10\({}^{-6}\) at 30 m [315]. M Karbalayghareh suggested a channel model based on V\({}^{2}\)LC's inherent characteristics, such as influence of road reflection, weather conditions, and asymmetric headlamp radiation. The authors proposed a closed-form path-loss expression for V\({}^{2}\)LC as a function of system and channel characteristics, i.e., lateral shift between two vehicles, linking distance, different weather conditions (clear, foggy, and rainy), angle of divergence of the Tx beam, and diameter of the aperture of the receiver. The channel-impulse responses (CIRs) were obtained using a non-sequential ray-tracing technique for a given vehicular scenario. The findings show the overall detection range, between the vehicles, of single photon avalanche diode (SPAD) array in different weather conditions, background light noise ratio, and fill factor (FF) [316]. M Yoo et al. proposed a method for estimating distance from vehicle-to-vehicle using a V\({}^{2}\)LC based on a low-resolution camera. The proposed approach uses one LED and two cameras to estimate the distance between two vehicles running on the lane. The algorithm helps the vehicle to estimate the distance in a difficult situation. The authors are also working on the estimation error caused by the low-resolution cameras. They also suggested a compensation system to reduce measurement error. The authors say that the distance between vehicles can be measured at greater precision with the proposed compensation method [317]. In Ref. [318], the authors implemented a V\({}^{2}\)LC-based vehicle tracking system using a Kalman filter positioning algorithm. The suggested approach uses a headlight LED and tail lamps to relay to the other vehicles for positioning. The target vehicle's instant location can be calculated with the aid of two CMOS dashboard cameras positioned on each vehicle. The authors could filter out random errors using a modified Kalman filter to achieve precise tracking, resulting in the accurate prediction of a vehicle's location. Christopher J. Rapson et al. have analyzed RF and V\({}^{2}\)LC based on vehicular networks. The authors presented a comparison of the performance of V\({}^{2}\)C networks using DSRC, LTE, 5G, Li-Fi, and OCC. The authors considered key parameters for the network such as low latency, high security, and high reliability [304]. If network lifetime, load balancing and bandwidth are not considered when selecting the next-hop link for high-mobility systems such as VANETs, then packet transmission may fail frequently. With the optimum use of resources, one can fix the problems in ITS. DL MsonGaleli has suggested a multi-objective linear integer programming (LIP) model which optimizes the use of resources in VANETs based on V\({}^{2}\)LC. In addition, the authors suggested a heuristic algorithm for a network of high-density vehicles. To improve network performance, the experimental findings from the LIP model and heuristic methods were analyzed [305]. In Ref. [319], the authors examined the critical properties of enabling V\({}^{2}\)LC networks. The authors developed and experimentally evaluated the feasibility of a V\({}^{2}\)LC system under visible light noise and interference.