Gossip-GAN trains a distributed GAN across user devices and uses its generated channel samples to train a CSI feedback autoencoder, matching centralized training accuracy while reducing uplink overhead.
Comparison of Methods Generalizing Max- and Average-Pooling
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abstract
Max- and average-pooling are the most popular pooling methods for downsampling in convolutional neural networks. In this paper, we compare different pooling methods that generalize both max- and average-pooling. Furthermore, we propose another method based on a smooth approximation of the maximum function and put it into context with related methods. For the comparison, we use a VGG16 image classification network and train it on a large dataset of natural high-resolution images (Google Open Images v5). The results show that none of the more sophisticated methods perform significantly better in this classification task than standard max- or average-pooling.
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Distributed Gossip-GAN for Low-overhead CSI Feedback Training in FDD mMIMO-OFDM Systems
Gossip-GAN trains a distributed GAN across user devices and uses its generated channel samples to train a CSI feedback autoencoder, matching centralized training accuracy while reducing uplink overhead.