Bayesian CRLB for Joint AoA, AoD and Multipath Gain Estimation in Millimeter Wave Wireless Networks

In this paper, we present an analysis of the non- random and the Bayesian Cramer-Rao lower bound (CRLB) for the joint estimation of angle-of-arrival (AoA), angle-of-departure (AoD), and the multipath gain in the millimeter-wave (mmWave) wireless networks. Our analysis is applicable to multipath channels with Gaussian noise and independent path parameters. Numerical results based on uniform AoA and AoD in $[0,\pi)$, and Rician fading path gains, reveal that the Bayesian CRLB decreases monotonically with an increase in the Rice factor. Further, the CRLB obtained by using beam forming and combining code books generated by quantizing directly the domain of AoA and AoD was found to be lower than those obtained with other types of beam forming and combining code books.