Multi-Rate Compression for Downlink CSI Based on Transfer Learning in FDD Massive MIMO Systems

IEEE Vehicular Technology Conference, pp. 1–5

Abstract

Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

Authors 7

1. Yuting Wang

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

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

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

2. Jinlong Sun

   Nanjing University of Posts and Telecommunications · Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space · Ministry of Industry and Information Technology · Nanjing University of Aeronautics and Astronautics

   Affiliation as printed

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

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

   The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

3. Jie Wang

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

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

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

4. Jie Yang

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

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

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

5. Tomoaki Otsuki Ohtsuki

   Keio University

   Affiliation as printed

   Keio University,Department of Information and Computer Science,Yokohama,Japan

6. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University,Faculty of Science and Engineering,Manchester,United Kingdom

   Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, United Kingdom

7. Haris Gacanin Aachen Institute for Communication Technologies and Embedded Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University,Aachen,Germany

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany