Learnable Sparse Transformation-Based Massive MIMO CSI Recovery Network
| Autor: | Yiyun Wang, Huarui Yin, Weidong Wang, Xiaohui Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Channel (digital image) Computer science MIMO 020206 networking & telecommunications Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Autoencoder Discrete Fourier transform Computer Science Applications Matrix (mathematics) Compressed sensing Channel state information Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Algorithm Computer Science::Information Theory Sparse matrix |
| Zdroj: | IEEE Communications Letters. 24:1468-1471 |
| ISSN: | 2373-7891 1089-7798 |
| Popis: | In frequency division duplex massive Multiple-Input Multiple-Output (MIMO) systems, plenty of Channel State Information (CSI) needs to be fed back. By exploiting the correlation of channel coefficients, the channel matrix can be transformed into a sparse form for compression. In this letter, we propose a model-based sparse recovery network that combines the advantages of compressed sensing reconstruction algorithms and neural networks, to perform CSI compression and reconstruction fast and accurately. Moreover, considering that the CSI is not strictly sparse in the discrete Fourier transform basis, we introduce a sparse autoencoder in our network to learn sparse transformations. Extensive experiments show that our model outperforms traditional compressed sensing algorithms and network-based methods. |
| Databáze: | OpenAIRE |
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