Institute for Communication Technologies and Embedded Systems

Downlink Channel State Information Limited Feedback Using Fully Convolutional Network

Authors:
Fan, G. ,  He, Z. ,  Sun, J. ,  Gui, G. ,  Gacanin, H. ,  Abedisi, B.
Book Title:
2021 IEEE Wireless Communications and Networking Conference (WCNC)
Date:
Apr. 2021
DOI:
10.1109/WCNC49053.2021.9417350
hsb:
RWTH-2021-11447
Language:
English
Abstract:
In massive multiple input multiple output (MIMO) systems, the base station (BS) requires channel state information (CSI) to better utilize the available spatial diversity and multiplexing gains. However, in frequency division duplex (FDD) systems, user equipment (UE) needs to keep on feeding downlink CSI back to the BS, thereby consuming precious bandwidth resources. In this paper, we propose a deep learning (DL) based downlink CSI limited feedback scheme, called FullyConv, which is composed of all convolutional layers to compress and decompress the downlink CSI. FullyConv will improve reconstruction accuracy and robustness as well as reduce the time and space complexity, thus enhancing the system feasibility. Experimental results demonstrate that the FullyConv has a gain of nearly 5 dB compared to baseline. The performance of the FullyConv degrades slightly in the noisy uplink channel, which shows the robustness of FullyConv. Meanwhile, the complexity of the model composed of time complexity and space complexity is significantly reduced.
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