DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations
| Autor: | Vesa Starck, Janne M. J. Huttunen, Dani Korpi, Mikko Honkala |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
Networking and Internet Architecture (cs.NI) FOS: Computer and information sciences Computer Science - Machine Learning business.industry Computer science Deep learning MIMO Physical layer Context (language use) Convolutional neural network Spatial multiplexing Machine Learning (cs.LG) Computer Science - Networking and Internet Architecture Transformation (function) FOS: Electrical engineering electronic engineering information engineering Maximal-ratio combining Artificial intelligence Electrical Engineering and Systems Science - Signal Processing business Algorithm Computer Science::Information Theory |
| Zdroj: | ICC |
| DOI: | 10.48550/arxiv.2010.16283 |
| Popis: | Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of encouraging results, most works have not considered spatial multiplexing in the context of multiple-input and multiple-output (MIMO) receivers. In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together. We propose two novel alternatives for the transformation layer: a maximal ratio combining-based transformation, or a fully learned transformation. The former relies more on expert knowledge, while the latter utilizes learned multiplicative layers. Both proposed transformation layers are shown to clearly outperform the conventional baseline receiver, especially with sparse pilot configurations. To the best of our knowledge, these are some of the first results showing such high performance for a fully learned MIMO receiver. |
| Databáze: | OpenAIRE |
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