3-D Position and Velocity Estimation in 5G mmWave CRAN with Lens Antenna Arrays

5G millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate multilateration: large bandwidth, large antenna arrays, and increased densities of base stations allow for unparalleled delay and angular resolution. However, combining localization into communications and designing joint position and velocity estimation algorithms are challenging problems. This paper considers the joint estimation in three-dimensional (3-D) lens antenna array based mmWave CRAN architecture. We embed multilateration into communications and explain its benefits for the initial access and beam training stages. We propose a closed-form solution for the joint estimation problem by forming the pseudo-linear matrix representation and designing the weighted least squares estimator with hybrid measurements. The proposed method is proven asymptotically unbiased and confirmed by simulations to achieve the Cramer- Rao lower bound and attain the desired sub-decimeter level accuracy.