Potential Communication in B5G Networks Through Hybrid Millimeter‐Wave Beamforming and Machine Learning

B5G and 6G are still in their infancy, but the technological advancement and curiosity about the potential inclusion of ML in today's wireless communication have shown many dimensions to current researchers and academicians. Smartest as proposed and advanced as suggested, ML has proven that it will rule for at least another decade in the field of wireless communication. Basic challenges in the current communication network, like proper bandwidth utilization, security enhancement, interference limitations to a lower level, and data rate increments to some high expectation level, have been solved and maintained by the proper inclusion of millimeter-wave and ML algorithms. As stated, the pros of mmWave are quite impressive, as is the ML algorithm's performance in the field of wireless communication. Algorithms, like supervised, unsupervised, and reinforced learning, are the building blocks of today's huge data optimizations in wireless communication. Together, they have revolutionized the current scenario of smart networking. This chapter dives into the basics of mmWave beamforming and the subsequent contribution of ML techniques toward the deployment of mmWave in B5G communications. It also studies the challenges for implementation and the future path in this regard.