Learning to Detect
TLDR
Li et al. as mentioned in this paper proposed two different deep architectures: a standard fully connected multi-layer network, and a detection network (DetNet), which was specifically designed for the task of multiple-input-multiple-output detection.Abstract:
In this paper, we consider multiple-input-multiple-output detection using deep neural networks. We introduce two different deep architectures: a standard fully connected multi-layer network, and a detection network (DetNet), which is specifically designed for the task. The structure of DetNet is obtained by unfolding the iterations of a projected gradient descent algorithm into a network. We compare the accuracy and runtime complexity of the proposed approaches and achieve state-of-the-art performance while maintaining low computational requirements. Furthermore, we manage to train a single network to detect over an entire distribution of channels. Finally, we consider detection with soft outputs and show that the networks can easily be modified to produce soft decisions.read more
Citations
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