Frameworks on Few-Shot Learning with Applications in Wireless Communication

Few-shot learning (FSL) enables adaptation to new tasks with only limited training data, which can be used to quickly adjust the wireless transceiver according to changing channel environments. In this paper, we develop two FSL frame-works that fit in wireless transceiver design. Both frameworks are based on optimization programs that can be solved by well-known algorithms, like the inexact alternating direction method of multipliers (iADMM) and the inexact alternating direction method (iADM). As examples, we demonstrate how the proposed FSL frameworks are used for the OFDM receiver and beamforming (BF) for the millimeter wave (mmWave) system. The numerical experiments confirm their desirable performance in both applications compared to other popular approaches, such as transfer learning (TL) and model-agnostic meta-learning.