Downlink compressive channel estimation with support diagnosis in FDD massive MIMO

Wei Lu,Yongliang Wang,Qiqing Fang,Shixin Peng

doi:10.1186/s13638-018-1131-4

Abstract

Downlink channel state information (CSI) is critical in a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. We exploit the reciprocity between uplink and downlink channels in angular domain and diagnose the supports of downlink channel from the estimated uplink channel. While the basis mismatch effects will damage the sparsity level and the path angle deviations between uplink and downlink transmission paths will induce differences in channel supports, a downlink support diagnosis algorithm based on the DBSCAN (density-based spatial clustering of applications with noise) which is widely used in machine learning is presented. With the diagnosed supports of downlink channel in angular domain, a weighted subspace pursuit (SP) channel estimation algorithm for FDD massive MIMO is proposed. The restricted isometry property (RIP)-based performance analysis for the weighted SP algorithm is given out. Both the analysis and the simulation results show that the proposed downlink channel estimation with diagnosed supports is superior to the standard iteratively reweighted least squares (IRLS) and SP without channel priori or with the assumption of the common supports for uplink and downlink channels in angular domain.

Highlights

Spectrum and radio resources in the forthcoming new communication systems are valuable for efficient transmission
It can be seen that support priori can improve the recovery performance in massive multiple-input multiple-output (MIMO), and compressive channel estimation can benefit from support priori by channel reciprocity in time division duplexing (TDD) system
4 Proposed methods the support diagnosis algorithm is first proposed based on the reciprocity in angular domain for uplink and downlink channels; the downlink massive MIMO channel estimation algorithm based on the diagnosed support is proposed. 4.1 Channel support diagnosis algorithm In this subsection, we propose the channel support diagnosis algorithm based on the analysis of basis mismatch and support difference between the downlink channel and uplink channel

Summary

Introduction

Spectrum and radio resources in the forthcoming new communication systems are valuable for efficient transmission. In [9], the authors consider the incorrect indices in the previous support set and exclude them adaptively From these researches, it can be seen that support priori can improve the recovery performance in massive MIMO, and compressive channel estimation can benefit from support priori by channel reciprocity in TDD system. In [14], a two-stage weighted block l1 minimization algorithm is proposed for downlink CSI estimation in FDD massive MIMO system, and the priori knowledge that MU channels share common supports is used. It can be seen that FDD massive MIMO channel estimation can benefit from the CS framework, and the support information from the previous estimated channel or from the structured sparsity among multiusers can improve the estimation performance.

System model

Conclusions