  With the great success of deep learning (DL) in image classification, speech
recognition, and other fields, more and more studies have applied various
neural networks (NNs) to wireless resource allocation. Generally speaking,
these artificial intelligent (AI) models are trained under some special
learning hypotheses, especially that the statistics of the training data are
static during the training stage. However, the distribution of channel state
information (CSI) is constantly changing in the real-world wireless
communication environment. Therefore, it is essential to study effective
dynamic DL technologies to solve wireless resource allocation problems. In this
paper, we propose a novel framework, named meta-gating, for solving resource
allocation problems in an episodically dynamic wireless environment, where the
CSI distribution changes over periods and remains constant within each period.
The proposed framework, consisting of an inner network and an outer network,
aims to adapt to the dynamic wireless environment by achieving three important
goals, i.e., seamlessness, quickness and continuity. Specifically, for the
former two goals, we propose a training method by combining a model-agnostic
meta-learning (MAML) algorithm with an unsupervised learning mechanism. With
this training method, the inner network is able to fast adapt to different
channel distributions because of the good initialization. As for the goal of
continuity, the outer network can learn to evaluate the importance of inner
network's parameters under different CSI distributions, and then decide which
subset of the inner network should be activated through the gating operation.
Additionally, we theoretically analyze the performance of the proposed
meta-gating framework.
