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The pervasive nature of wireless telecommunication has made it the foundationfor mainstream technologies like automation, smart vehicles, virtual reality,and unmanned aerial vehicles. As these technologies experience widespreadadoption in our daily lives, ensuring the reliable performance of cellularnetworks in mobile scenarios has become a paramount challenge. Beamforming, anintegral component of modern mobile networks, enables spatial selectivity andimproves network quality. However, many beamforming techniques are iterative,introducing unwanted latency to the system. In recent times, there has been agrowing interest in leveraging mobile users' location information to expeditebeamforming processes. This paper explores the concept of contextualbeamforming, discussing its advantages, disadvantages and implications.Notably, the study presents an impressive 53% improvement in signal-to-noiseratio (SNR) by implementing the adaptive beamforming (MRT) algorithm comparedto scenarios without beamforming. It further elucidates how MRT contributes tocontextual beamforming. The importance of localization in implementingcontextual beamforming is also examined. Additionally, the paper delves intothe use of artificial intelligence schemes, including machine learning and deeplearning, in implementing contextual beamforming techniques that leverage userlocation information. Based on the comprehensive review, the results suggestthat the combination of MRT and Zero forcing (ZF) techniques, alongside deepneural networks (DNN) employing Bayesian Optimization (BO), represents the mostpromising approach for contextual beamforming. Furthermore, the study discussesthe future potential of programmable switches, such as Tofino, in enablinglocation-aware beamforming.

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance