Downlink compressive channel estimation with support diagnosis in FDD massive MIMO

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.

Downlink Channel Estimation in Massive Multiple-Input Multiple-Output with Correlated Sparsity by Overcomplete Dictionary and Bayesian Inference

“…It was proven that the prior support information could improve the channel recovery performance [3][4][5][6]. Hence in this paper we made use of the prior support information from the previous timeslot to improve the Bayesian channel estimation.…”

“…On the other hand, we could have also diagnosed the probable channel supports further by taking the angle deviation and leakage effects into consideration. In this paper we adopted the support diagnosis algorithm, and the details can be found in [6].…”

“…In the simulation, the support diagnosis algorithm in [6] was adopted, and we assumed that the transmission angle change between timeslots was within 1 degree. The pilot length was 50, and antenna number at the BS was 100.…”

“…The channel was generated according to the spatial model as defined in 3GPP TR25.996. We compared our proposed algorithm with a unitary dictionary with a size of 100 and the overcomplete dictionary with a size of 150, 200, and 250, and compared this with a Bayesian sparse learning (SL) [16], weighted subspace pursuit (WSP) [6], weighted l 1 minimization (W-l 1 min) [5], weighted iteratively reweighted least square(W-IRLS), IRLS [17], compressive sampling matched pursuit (COSAMP) in [11], and l 1 minimization (l 1 min) [18].…”

“…In [5] a three-level weighting scheme based on the support information was used for the weighted l 1 minimization and the simulation results showed superiority. In [6] we exploited the reciprocity between uplink and downlink channels in the angular domain, and diagnosed the supports of the downlink channel from the estimated uplink channel, and proposed a weighted subspace pursuit (SP) channel estimation algorithm for FDD massive MIMO. It can be seen that CS was effective in the channel estimation for massive MIMO.…”