A Statistical Characterization of Wireless Channels Conditioned on Side Information

Prior knowledge about statistical channel characteristics and additional side information, such as estimated parameters shared between radar and communication systems, can both enhance physical layer applications. Generally, the wide-sense-stationary-uncorrelated-scattering WSSUS property together with the far-field approximation and strongly fluctuating path phases statistically characterize the unconditional wireless channel by a zero mean and Toeplitz-structured covariance matrices. In this letter, we comprehensively categorize side information based on whether the conditioning on this information preserves or abandons these statistical channel features. The established framework combines insights from a generic channel model with representing the channel as a Bayesian network (BN). Using our framework, we additionally analyze and improve machine learning (ML) aided channel modeling, clustering and estimation demonstrating its practicality for the physical layer.