Downlink Channel State Information Limited Feedback Using Fully Convolutional Network

IEEE Wireless Communications and Networking Conference, vol. 521, pp. 1–5

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.

Authors 6

1. Guanghui Fan

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

2. Zhengran He

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

3. Jinlong Sun

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

4. Guan Gui

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

5. Haris Gacanin Aachen Faculty of Electrical Engineering and Information Technology

   RWTH Aachen University

   Affiliation as printed

   Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany

6. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Faculty of Science and Engineering, Manchester Metropolitan University, UK