Randomized Low-Rank Approximation Based Massive MIMO CSI Compression
| Autor: | Liu Hongfu, Haozhan Li, Bin Li, Chenglin Zhao, Ziping Wei |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Transmitter MIMO 020206 networking & telecommunications Low-rank approximation Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Spectral efficiency Computer Science Applications Channel capacity Computer engineering Channel state information Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Overhead (computing) Electrical and Electronic Engineering Computer Science::Information Theory Communication channel |
| Zdroj: | IEEE Communications Letters. 25:2004-2008 |
| ISSN: | 2373-7891 1089-7798 |
| DOI: | 10.1109/lcomm.2021.3065751 |
| Popis: | Massive multiple-input multiple-output (MIMO) is regarded as one enabling technique to improve channel capacity and energy/spectrum efficiency of 5G communications. To attain such potential benefits, accurate channel information is critical to the transmitter, which yet remains a challenging task for frequency division duplexing (FDD) systems, i.e., the channel state information (CSI) feedback tends to be resource-demanding especially for massive MIMO communications. In this work, we propose a novel CSI feedback method with low complexity and high precision, which is inspired by randomized matrix approximation. Our approach exploits the inherent low-rank characteristic of a large channel matrix, and approximates it by small sub-matrices which are then reported to transmitter to recover a CSI matrix. Theoretical bounds of the recovered CSI in both error-free and error cases are derived. Simulation results demonstrate our method could recover CSI accurately via an extremely low complexity and yet achieve a largely reduced compression ratio (or feedback overhead), compared to other schemes. It thus has the great potential in the emerging massive MIMO FDD communications. |
| Databáze: | OpenAIRE |
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