Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO
| Autor: | Matteo Nerini, Valentina Rizzello, Michael Joham, Wolfgang Utschick, Bruno Clerckx |
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| Rok vydání: | 2023 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Information Theory (cs.IT) Computer Science - Information Theory Applied Mathematics FOS: Electrical engineering electronic engineering information engineering Data_CODINGANDINFORMATIONTHEORY Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering Computer Science::Information Theory Computer Science Applications |
| Zdroj: | IEEE Transactions on Wireless Communications. 22:2886-2900 |
| ISSN: | 1558-2248 1536-1276 |
| Popis: | To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division duplex (FDD) systems, the CSI is acquired through a feedback signal from the user equipment (UE). However, this may lead to an important overhead in FDD massive MIMO systems. Focusing on these systems, in this study, we propose a novel strategy to design the CSI feedback. Our strategy allows to optimally design variable length feedback, that is promising compared to fixed feedback since users experience channel matrices differently sparse. Specifically, principal component analysis (PCA) is used to compress the channel into a latent space with adaptive dimensionality. To quantize this compressed channel, the feedback bits are smartly allocated to the latent space dimensions by minimizing the normalized mean squared error (NMSE) distortion. Finally, the quantization codebook is determined with k-means clustering. Numerical simulations show that our strategy improves the zero-forcing beamforming sum rate by 17%, compared to CsiNetPro. The number of model parameters is reduced by 23.4 times, thus causing a significantly smaller offloading overhead. At the same time, PCA is characterized by a lightweight unsupervised training, requiring eight times fewer training samples than CsiNetPro. Accepted by IEEE for publication |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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