Pilot-Aided Channel Estimation for Massive MIMO Systems in TDD-mode Using Walsh-Hadamard Transformed Subsampled Data at the Base Station

In this paper, we have optimize specificities with the use of massive MIMO in 5 G systems. Massive MIMO uses a large number, low cost and low power antennas at the base stations. These antennas provide benefit such as improved spectrum performance, which allows the base station to serve more users, reduced latency due to reduced fading power consumption and much more. By employing the lens antenna array, beam space MIMO can utilize beam selection to reduce the number of required RF chains in mm Wave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beam space channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. In this work we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beam space channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beam space channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beam space channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beam space channel can be exploited.

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Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Wireless Communications and Mobile Computing ◽

10.1155/2019/6374764 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Athar Waseem ◽

Aqdas Naveed ◽

Sardar Ali ◽

Muhammad Arshad ◽

Haris Anis ◽

...

Keyword(s):

Channel Estimation ◽

Compressive Sensing ◽

Massive Mimo ◽

Communication Systems ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Sampling Theorem ◽

Base Station ◽

Mimo Channels ◽

Pilot Design

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

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A Robust Channel Estimation Scheme for 5G Massive MIMO Systems

Wireless Communications and Mobile Computing ◽

10.1155/2019/3469413 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Imran Khan ◽

Joel J. P. C. Rodrigues ◽

Jalal Al-Muhtadi ◽

Muhammad Irfan Khattak ◽

Yousaf Khan ◽

...

Keyword(s):

Channel Estimation ◽

Massive Mimo ◽

Mimo Systems ◽

Base Station ◽

Separation Mechanism ◽

Channel Matrix ◽

Large Channel ◽

Estimation Scheme ◽

Improved Performance ◽

Csi Feedback

Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for efficient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. The spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. Then, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. This scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme effectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-the-art schemes.

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Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

Electronics ◽

10.3390/electronics9020301 ◽

2020 ◽

Vol 9 (2) ◽

pp. 301

Author(s):

Jianhe Du ◽

Jiaqi Li ◽

Jing He ◽

Yalin Guan ◽

Heyun Lin

Keyword(s):

Channel Estimation ◽

Massive Mimo ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Estimation Algorithm ◽

Step Length ◽

Low Complexity ◽

Base Station ◽

Estimation Accuracy ◽

Input Multiple Output

For multi-user millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the precise acquisition of channel state information (CSI) is a huge challenge. With the increase of the number of antennas at the base station (BS), the traditional channel estimation techniques encounter the problems of pilot training overhead and computational complexity increasing dramatically. In this paper, we develop a step-length optimization-based joint iterative scheme for multi-user mmWave massive MIMO systems to improve channel estimation performance. The proposed estimation algorithm provides the BS with full knowledge of all channel parameters involved in up- and down-links. Compared with existing algorithms, the proposed algorithm has higher channel estimation accuracy with low complexity. Moreover, the proposed scheme performs well even with a small number of training sequences and a large number of users. Simulation results are shown to demonstrate the performance of the proposed channel estimation algorithm.

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Grouping-Based Channel Estimation and Tracking for Millimeter Wave Massive MIMO Systems

Wireless Communications and Mobile Computing ◽

10.1155/2021/2922359 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Rui Yin ◽

Xin Zhou ◽

Wei Qi ◽

Celimuge Wu ◽

Yunlong Cai

Keyword(s):

Computational Complexity ◽

Channel Estimation ◽

Millimeter Wave ◽

Massive Mimo ◽

Mimo Systems ◽

Mimo System ◽

Closed Form Solution ◽

Network Capacity ◽

Base Station ◽

Estimation Accuracy

Although the millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping-based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low-complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple-signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed-form solution for updating the step-size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

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Scalable user selection in FDD massive MIMO

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-02073-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Xing Zhang ◽

Ashutosh Sabharwal

Keyword(s):

Channel Estimation ◽

Massive Mimo ◽

Mimo Systems ◽

Base Station ◽

User Selection ◽

Selection Scheme ◽

Base Station Antennas ◽

Measurement Selection ◽

Weighted Sum Rate ◽

Experimental Findings

AbstractUser subset selection requires full downlink channel state information to realize effective multi-user beamforming in frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. However, the channel estimation overhead scales with the number of users in FDD systems. In this paper, we propose a novel propagation domain-based user selection scheme, labeled as zero-measurement selection, for FDD massive MIMO systems with the aim of reducing the channel estimation overhead that scales with the number of users. The key idea is to infer downlink user channel norm and inter-user channel correlation from uplink channel in the propagation domain. In zero-measurement selection, the base-station performs downlink user selection before any downlink channel estimation. As a result, the downlink channel estimation overhead for both user selection and beamforming is independent of the total number of users. Then, we evaluate zero-measurement selection with both measured and simulated channels. The results show that zero-measurement selection achieves up to 92.5% weighted sum rate of genie-aided user selection on the average and scales well with both the number of base-station antennas and the number of users. We also employ simulated channels for further performance validation, and the numerical results yield similar observations as the experimental findings.

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