Abstract-In this letter, an efficient channel estimation approach based on the emerging block-structured compressive sensing is proposed for the downlink massive multiuser (MU) MIMO system. By exploiting the channel properties of block sparsity and channel reciprocity in TDD mode, the auxiliary information based block subspace pursuit (ABSP) algorithm is proposed to recover the downlink channels, where the path delays acquired from uplink training is utilized as the auxiliary information. Unlike traditional approaches where the channel estimation overhead is proportional to the number of BS antennas, the proposed approach could provide an accurate channel estimation approaching the performance bound while reduce the pilot overhead by nearly one-third.Index Terms-Massive MU-MIMO, channel estimaion, block compressive sensing.

In this paper, the channel estimation problem for the uplink massive multi-input multioutput (MIMO) system is considered. Motivated by the observations that the channels in massive MIMO systems may exhibit sparsity and the channel support changes slowly over time, we propose one efficient channel estimation method under the framework of compressive sensing. By exploiting the channel impulse response (CIR) estimated from the previous OFDM symbol, we firstly estimate the probabilities that the elements in the current CIR are nonzero. Then, we propose the probability-weighted subspace pursuit (PWSP) algorithm exploiting these probability information to efficiently reconstruct the uplink massive MIMO channel. Moreover, noting that the massive MIMO systems also share a common support within one channel matrix due to the shared local scatterers in the physical propagation environment, an antenna collaborating method is exploited for the proposed method to further enhance the channel estimation performance. Simulation results show that compared to the existing compressive sensing methods, the proposed methods could achieve higher spectral efficiency as well as more reliable performance over time-varying channel.

Abstract-In this paper, the channel estimation problem for the downlink massive multi-input multi-output (MIMO) system is considered. Motivated by the observation that the channels in massive MIMO systems always exhibit sparsity and the path delays vary slowly in one uplink-downlink process, we propose a novel channel estimation method under the framework of the weighted compressive sensing. Unlike the conventional methods which do not make use of any a priori information or assume the path delays are invariable, we estimate the probabilities that the paths are nonzero in the downlink channel by exploiting the channel impulse response (CIR) estimated from the uplink channel estimation. Based on these probabilities, we propose the Weighted Structured Subspace Pursuit (WSSP) algorithm to efficiently reconstruct the massive MIMO channel. Simulation results show that compared to the conventional methods, the WSSP could achieve a significant reduction in the number of pilots while maintain good channel estimation performance.

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Massive (or large-scale) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is widely acknowledged as a key technology for future communication. One main challenge to implement this system in practice is the high dimensional channel estimation, where the large number of channel matrix entries requires prohibitively high computational complexity. To solve this problem efficiently, a channel estimation approach using few number of pilots is necessary. In this paper, we propose a weighted Homotopy based channel estimation approach which utilizes the sparse nature in MIMO channels to achieve a decent channel estimation performance with much less pilot overhead. Moreover, inspired by the fact that MIMO channels are observed to have approximately common support in a neighborhood, an information exchange strategy based on the proposed approach is developed to further improve the estimation accuracy and reduce the required number of pilots through joint channel estimation. Compared with the traditional sparse channel estimation methods, the proposed approach can achieve more than 2dB gain in terms of mean square error (MSE) with the same number of pilots, or * Corresponding author