Visible light communication (VLC) channel is usually frequency selective and time variant channel, thus, degrade the data rate. High data rates can be achieved only by designing more efficient signaling techniques. MIMO technique is used to improve the system performance. Unfortunately, Inter Symbol Interference (ISI) is the main problem in MIMO system. There are different detection techniques to combat ISI. This paper discuss different algorithms to improve the performance of 2×2 MIMO model for vehicle to vehicle communication in visible light environment V2V-VLC. Those algorithms include, Minimum Mean Square Error (MMSE), Maximum likelihood (ML), Minimum Mean Square Error Successive Interference Cancellation (MMSE-SIC), Zero Forcing Successive Interference Cancellation (ZF-SIC), Zero Forcing (ZF), Expected Propagation (EP-10), Gaussian Tree Approximation (GTA) and Gaussian Tree Approximation Successive Interference Cancellation (GTA-SIC) detector. These detectors are compared and analyzed for different Signal Error Rate (SER) vs. Signal to Noise Ratio (SNR) in spatial multiplexing domain. Simulation results showed that ZF, MMSE and MMSE-SIC detectors have better performance in terms of SER and SNR and have less computing time.
Visible light communication (VLC) channel is usually frequency selective and time variant channel, thus, degrade the data rate. High data rates can be achieved only by designing more efficient signaling techniques. MIMO technique is used to improve the system performance. Unfortunately, Inter Symbol Interference (ISI) is the main problem in MIMO system. There are different detection techniques to combat ISI. This paper discuss different algorithms to improve the performance of 2×2 MIMO model for vehicle to vehicle communication in visible light environment V2V-VLC. Those algorithms include, Minimum Mean Square Error (MMSE), Maximum likelihood (ML), Minimum Mean Square Error Successive Interference Cancellation (MMSE-SIC), Zero Forcing Successive Interference Cancellation (ZF-SIC), Zero Forcing (ZF), Expected Propagation (EP-10), Gaussian Tree Approximation (GTA) and Gaussian Tree Approximation Successive Interference Cancellation (GTA-SIC) detector. These detectors are compared and analyzed for different Signal Error Rate (SER) vs. Signal to Noise Ratio (SNR) in spatial multiplexing domain. Simulation results showed that ZF, MMSE and MMSE-SIC detectors have better performance in terms of SER and SNR and have less computing time.