Space-division multiple access-based beamforming for multi-user batteryless machine-type communications

• Ano de defesa: 2024
• Autor(a) principal: Silva, Lucas Jurgen Klein da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial; UTFPR
• Palavras-chave em Português: Sistemas de comunicação sem fio; Energia - Transferência; Processamento de sinais; Internet das coisas; Aprendizado do computador; Wireless communication systems; Energy transfer; Signal processing; Internet of things; Machine learning; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/35756

Machine-Type Communication (MTC) in 5G and future 6G networks requires efficient and sustainable energy solutions to support massive deployments of IoT devices. Batteryless MTC, powered by Wireless Energy Transfer (WET) from dedicated Power Beacons (Pbs), has emerged as a promising approach to eliminate the need for frequent battery replacements and maintenance. This study focuses on the comparative analysis of different WET strategies: pure Statistical Channel State Information (SCSI), SCSI combined with Space-Division Multiple Access (SDMA), and the omnidirectional Switching Antenna (SA) technique, each having distinct advantages and limitations in various deployment scenarios. The incident power statistics and outage probabilities under realistic Rician fading channel models were defined, and an optimization algorithm was proposed to minimize the energy consumption of the PB while meeting stringent reliability and latency constraints. Numerical results reveal optimal harvesting and transmission times for each strategy and demonstrate the trade-offs between energy efficiency and system complexity. While the SDMA approach leverages multiple antennas to enhance energy delivery efficiency in dense environments, the SA technique offers a simpler, yet efficient solution for large-scale deployments. The insights gained from this study provide a comprehensive understanding of how to dynamically adapt WET strategies to optimize the performance of batteryless MTC networks.