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With the great success of deep learning (DL) in image classification, speechrecognition, and other fields, more and more studies have applied variousneural networks (NNs) to wireless resource allocation. Generally speaking,these artificial intelligent (AI) models are trained under some speciallearning hypotheses, especially that the statistics of the training data arestatic during the training stage. However, the distribution of channel stateinformation (CSI) is constantly changing in the real-world wirelesscommunication environment. Therefore, it is essential to study effectivedynamic DL technologies to solve wireless resource allocation problems. In thispaper, we propose a novel framework, named meta-gating, for solving resourceallocation problems in an episodically dynamic wireless environment, where theCSI 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 importantgoals, i.e., seamlessness, quickness and continuity. Specifically, for theformer two goals, we propose a training method by combining a model-agnosticmeta-learning (MAML) algorithm with an unsupervised learning mechanism. Withthis training method, the inner network is able to fast adapt to differentchannel distributions because of the good initialization. As for the goal ofcontinuity, the outer network can learn to evaluate the importance of innernetwork's parameters under different CSI distributions, and then decide whichsubset of the inner network should be activated through the gating operation.Additionally, we theoretically analyze the performance of the proposedmeta-gating framework.

Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments