To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient (UFEE) baseband processors.
To derive guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work aims at the derivation of the tradeoff between the model convergence and the settings of power sources in two scenarios: 1) the transmission power and density of power-beacons (dedicated charging stations) if they are deployed, or otherwise 2) the transmission power of a server (access-point).
Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation.
Information Theory Signal Processing Information Theory
Specifically, the metrics that measure data importance in active learning (e. g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.
To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling.