Learning to Detect
This addresses MIMO detection for wireless communication systems, offering an incremental improvement with a novel method for a known bottleneck.
The paper tackles MIMO detection by introducing two deep neural network architectures, including DetNet based on unfolding a projected gradient descent algorithm, achieving state-of-the-art performance with low computational complexity and training a single network for an entire channel distribution.
In this paper we consider Multiple-Input-Multiple-Output (MIMO) 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 purposed 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.