on-parametric likelihood based channel estimator for Gaussian mixture noise. Signal Process. 87(11), 2569-2586 (2007) 39. A. Goldsmith, Wireless Communications (Cambridge University Press, Cambridge, 2005) Chapter 7 Vehicular Communications in the B5G Check for updates Leyre Azpilicueta, Cesar Vargas-Rosales, Ana Vazquez Alejos, and Francisco Falcone 7.1 Introduction The transition from 4G to 5G did not bring disruptive changes to the physical layer or radio interface. Up to 2020, the novelties consisted of higher throughput, latency, and reliability, due to the use of OFDM and the introduction of network slicing, among other functionalities. Furthermore, base stations have incorporated antenna arrays for massive MIMO and larger bandwidths that increased notably the spectral efficiency. The successful development of the fifth generation (5G) of wireless mobile communications requires disruptive technologies to fulfill unforeseen data rates, latency, and capacity that will be necessary, among others, including network densification and virtualization, massive multiple input multiple output (MIMO), antenna beamforming, device-to-device communications, smart cells, multi-cell cooperation, and interference mitigation techniques. In this scenario, the radio interface acquires a new dimension, and its proper modeling will contribute ensuring the expected 5G quality of service and quality of experience requirements. 5G pushes first-line questions like radio spectrum, radio access, and core network that incorporate slicing and virtualization concepts. Thus, 5G represents the most striving of the technology challenges for a generation of wireless mobile communi- L. Azpilicueta (X) . C. Vargas-Rosales School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL., Mexico e-mail: leyre.azpilicueta@tec.mx cvargas@tec.mx A. V. Alejos Campus Universitario, Escola de Ingeniería de Telecomunicación, Universidad de Vigo, Vigo, Spain e-mail: analejos@uvigo.es F. Falcone EEC Department, Universidad Publica de Navarra,