Spatio-Temporal Recommender for V2X Channels

Recommending channel characteristics for V2X communication has the distinct advantage of pre-conditioning the waveform at the transmitter to match the expected fading profile. The difficulty lies in extracting an accurate model for the channel, especially if the underlying variables are uncorrelated, unobserved and immeasurable. Our work implements this prescience by assimilating the Channel State Information (CSI), obtained as a feedback from vehicles, over time and space to adjust the modulation vectors such that the channel impairments are significantly diminished at the receiver, improving the Bit Error Rate (BER) by 96% for higher order modulations. To account for the multivariate, non-stationary V2X channel, a tensor decomposition and completion approach is used to mitigate the effects of sparsity and noise in the CSI measurements. Overall, the system is shown to operate with a prediction accuracy of 10 −3 MSE even in dense scattering environments over space and time.