Multidimensional Codebook Design Using Deep Learning Techniques for Rayleigh Fading Channels
| Autor: | Bruno Fontana da Silva, Xiaotian Fu, Didier Le Ruyet |
|---|---|
| Přispěvatelé: | Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Instituto Federal Sul-rio-grandense [Passo Fundo] (IFSUL) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning Codebook Data_CODINGANDINFORMATIONTHEORY Autoencoder [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Signal-to-noise ratio Control and Systems Engineering Fading Artificial intelligence Electrical and Electronic Engineering business Algorithm [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Decoding methods ComputingMilieux_MISCELLANEOUS Rayleigh fading |
| Zdroj: | IEEE Wireless Communications Letters IEEE Wireless Communications Letters, IEEE comsoc, In press, pp.1-1. ⟨10.1109/LWC.2021.3089024⟩ |
| ISSN: | 2162-2337 |
| DOI: | 10.1109/LWC.2021.3089024⟩ |
| Popis: | A new approach based on deep learning techniques for multidimensional codebook (MDC) design over Rayleigh fading channels is proposed in this letter. Different from autoencoder (AE) techniques, the proposed deep neural network (DNN) can generate codebooks directly without a decoder structure. Two loss functions, one exploiting essential figures of merit (FoMs) and the other based on theoretical symbol error probability over fading channels, are introduced for the proposed DNN structure. Simulation results reveal that the resulting codebooks of the proposed approach have similar symbol error rate (SER) performance when adopting different loss functions. They have substantial SER performance gain over the codebooks learned by AEs, and reach close SER performance with codebooks conventionally designed by state-of-the-art. Moreover, the proposed approach guarantees good FoMs for the learned MDCs. |
| Databáze: | OpenAIRE |
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