User Effects on Mobile Phone Antennas: Review and Potential Future Solutions

This paper explores the significant impact of human proximity on antenna design evolutionin mobile communication from GSM to LTE and future 5G technologies. It offers a comprehensive viewof the challenges posed by human interactions in current antenna designs, alongside modern solutions tomitigate these issues. Central to our study is the crucial role of extensive data acquisition in enabling AI-driven methodologies. We emphasize the need for diverse and comprehensive datasets to refine AI models.Our research demonstrates notable achievements, with our deep neural network-based models reaching upto 90% accuracy in radiation pattern classification and 87.5% in angular delay profile categorization.These results underscore our proficiency in incorporating AI into antenna engineering. We trace thehistorical trajectory of user-induced antenna challenges and their current implications, illustrating a coherentprogression. This narrative underscores the parallel evolution of antenna designs and user interactions,enhanced by our advanced model classification techniques. Our work presents an efficient method toaddress user effects using cutting-edge machine learning algorithms. (PDF) User Effects on Mobile Phone Antennas: Review and Potential Future Solutions. Available from: https://www.researchgate.net/publication/375926643_User_Effects_on_Mobile_Phone_Antennas_Review_and_Potential_Future_Solutions [accessed Nov 28 2023].This paper explores the significant impact of human proximity on antenna design evolution in mobile communication from GSM to LTE and future 5G technologies. It offers a comprehensive view of the challenges posed by human interactions in current antenna designs, alongside modern solutions to mitigate these issues. Central to our study is the crucial role of extensive data acquisition in enabling AI-driven methodologies. We emphasize the need for diverse and comprehensive datasets to refine AI models. Our research demonstrates notable achievements, with our deep neural network-based models reaching up to 90% accuracy in radiation pattern classification and 87.5% in angular delay profile categorization. These results underscore our proficiency in incorporating AI into antenna engineering. We trace the historical trajectory of user-induced antenna challenges and their current implications, illustrating a coherent progression. This narrative underscores the parallel evolution of antenna designs and user interactions, enhanced by our advanced model classification techniques. Our work presents an efficient method to address user effects using cutting-edge machine learning algorithms.</p