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Few-shot learning (FSL) enables adaptation to new tasks with only limitedtraining data. In wireless communications, channel environments can varydrastically; therefore, FSL techniques can quickly adjust transceiveraccordingly. In this paper, we develop two FSL frameworks that fit in wirelesstransceiver design. Both frameworks are base on optimization programs that canbe solved by well-known algorithms like the inexact alternating directionmethod of multipliers (iADMM) and the inexact alternating direction method(iADM). As examples, we demonstrate how the proposed two FSL frameworks areused for the OFDM receiver and beamforming (BF) for the millimeter wave(mmWave) system. The numerical experiments confirm their desirable performancein both applications compared to other popular approaches, such as transferlearning (TL) and model-agnostic meta-learning.

New Environment Adaptation with Few Shots for OFDM Receiver and mmWave Beamforming