ELM-Based Superimposed CSI Feedback for FDD Massive MIMO System

Chaojin Qing,Chuan Huang,Bin Cai,Qingyao Yang,Jiafan Wang

doi:10.1109/access.2020.2980969

Abstract

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO), deep learning (DL)-based superimposed channel state information (CSI) feedback has presented promising performance. However, it is still facing many challenges, such as the high complexity of parameter tuning, large number of training parameters, and long training time, etc. To overcome these challenges, an extreme learning machine (ELM)-based superimposed CSI feedback is proposed in this paper, in which the downlink CSI is spread and then superimposed on uplink user data sequence (UL-US) to feed back to base station (BS). At the BS, an ELM-based network is constructed to recover both downlink CSI and UL-US. In the constructed ELM-based network, we employ the simplified versions of ELM-based subnets to replace the subnets of DL-based superimposed feedback, yielding less training parameters. Besides, the input weights and hidden biases of each ELM-based subnet are loaded from the same matrix by using its full or partial entries, which significantly reduces the memory requirement. With similar or better recovery performances of downlink CSI and UL-US, the proposed ELM-based method has less training parameters, storage space, offline training and online running time than those of DL-based superimposed CSI feedback.

Highlights

The massive multiple-input multiple-output (MIMO) brings the fifth generation (5G) wireless communication system many advantages in system capacity and link robustness
The codebook-based channel state information (CSI) feedback method effectively reduces the feedback overhead at user side, the huge number of base station (BS) antennas in massive MIMO system substantially results in a tremendous dimension of codebook, which is too large to be applied in practice [3]
The proposed extreme learning machine (ELM)-based network consists of four subnets (i.e., CSI-ELM1, DET-ELM1, CSI-ELM2 and DET-ELM2), in which the downlink CSI recovery and uplink user data sequence (UL-US) detection are addressed by solving a multi-task problem

Summary

INTRODUCTION

The massive multiple-input multiple-output (MIMO) brings the fifth generation (5G) wireless communication system many advantages in system capacity and link robustness. The DL-based scheme is still hindered by many disadvantages, such as the high complexity of parameter tuning, large number of training parameters, long training time, etc This motivates us to develop ELM-based superimposed CSI feedback to improve the DL-based approach in [24]. To remedy the defect of [23], a DL-based superimposed CSI feedback was proposed in [24], which consistently improved the estimation of downlink CSI with similar or better UL-US detection performance. The ELM processed many advantages, e.g., fast learning speed (hundreds of times faster than that of backpropagation algorithm), good generalization performance [27]–[29], etc Inspired by these advantages, an ELM-based superimposed CSI feedback method is proposed in this paper to improve the DL-based superimposed CSI feedback in [24]. IP is the identity matrix of size P × P; BN (·) denotes the operation of batch normalization; · 2 is the Euclidean norm

SYSTEM MODEL

NETWORK ARCHITECTURE

ONLINE RUNNING

EXPERIMENTAL ANALYSIS

Findings

CONCLUSION