For Example, vehicular communication operates on highly dynamic condition where communication nodes are not like common cellular networks because, here channel is time-varying and network topology also varies fast due to high mobility. If mobility is high, then channel impulse response varies fast, that dramatically reduces the coherence time in compare to common cellular networks. Coherence time is denoted by in wireless communication Channel Impulse Responses (CIRs) experiences almost same attenuation in a particular time interval during data transmission. As dynamic channel impulse response is changing very fast, so obtaining high beamforming gain also becomes challenging. Hence, we need to estimate the time-varying channel in extremely short time duration.
Future self-driving cars would need up to 1 TB of data per hour of
driving, with data speeds above 750 Mbit/s. This highlights the
limitations of modern wireless technologies for automotive data sharing
and justifies the use of mm wave spectrum for greater data availability
and reduced latency due to the much wider bandwidth allocations.
Moreover, as a supporter of the concept of fully connected vehicles, the
NLOS transmission is a major issue for mm-wave communication. 5G
communication is growing interest in the automotive industry due to its
low latency and larger bandwidth spectrum.