AI Based Zero Energy Communication in 6G

#AI #6G

Supporting low- or zero-energy machine type devices through the incorporation of energy harvesting technologies in 6G is
expected to reduce frequent battery replacements as well as sustaining the operation of battery-less devices. The nodes can
harvest energy from natural sources, such as sun, vibration and pressure; inductive and magnetic coupling; and radio
frequency (RF) based wireless energy transfer. The complexity of the integration of energy harvesting into communication
system design is increasing with the wide variety of the requirements of massive machine type communication scenarios in
terms of reliability, delay and throughput. This high complexity can only be handled by the usage of AI techniques to
continuously monitor the system knowledge and update the optimal communication and energy harvesting parameters
accordingly. In this talk, novel optimal resource allocation algorithms are presented for low- or zero-energy machine type
devices based on the usage of AI techniques. The wireless communication network and energy harvesting parameters are
mapped to the optimal resource allocation by using deep neural networks.