Two-Step Codeword Design for Millimeter Wave Massive MIMO Systems With Quantized Phase Shifters

Chen, Kangjian; Qi, Chenhao; Li, Geoffrey Ye

doi:10.1109/tsp.2019.2959250

Cited by 72 publications

(48 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering a multi-user mmWave massive MIMO communication system, the BS equipped with N R = 4 RF chains and N A = 64 antennas serves U = 3 users with single antenna. We set G = 128 according to [6], and we set the number of multiple paths in mmWave channel as L u = 2, where g u,1 ∼ CN (0, 1) and g u,2 ∼ CN (0, 0.5) [6], [11]. For the uplink pilot transmission, we set F k B = I N R .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Several hybrid precoding methods have been proposed for single-user multi-stream mmWave communication systems. For examples, a hybrid precoding algorithm was proposed [8], where the analog precoding problem was formulated as a sparse reconstruction problem and the OMP method was adopted; to avoid the greed of the OMP method, the alternating minimization method was used [9], where the hybrid precoding problem was designed as a matrix decomposition problem and the analog precoder and digital precoder were optimized alternately; to reduce the computational complexity of the alternating minimization method, the hierarchical codebook was used to obtain multiple beams and then form the analog precoding [10], [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

Zhang

et al. 2020

IEEE Trans. Commun.

Self Cite

169

View full text Add to dashboard Cite

Channel estimation and hybrid precoding are considered for multi-user millimeter wave massive multi-input multioutput system. A deep learning compressed sensing (DLCS) channel estimation scheme is proposed. The channel estimation neural network for the DLCS scheme is trained offline using simulated environments to predict the beamspace channel amplitude. Then the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. A deep learning quantized phase (DLQP) hybrid precoder design method is developed after channel estimation. The training hybrid precoding neural network for the DLQP method is obtained offline considering the approximate phase quantization. Then the deployment hybrid precoding neural network (DHPNN) is obtained by replacing the approximate phase quantization with ideal phase quantization and the output of the DHPNN is the analog precoding vector. Finally, the analog precoding matrix is obtained by stacking the analog precoding vectors and the digital precoding matrix is calculated by zero-forcing. Simulation results demonstrate that the DLCS channel estimation scheme outperforms the existing schemes in terms of the normalized mean-squared error and the spectral efficiency, while the DLQP hybrid precoder design method has better spectral efficiency performance than other methods with low phase shifter resolution.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

Zhang

et al. 2020

IEEE Trans. Commun.

Self Cite

169

View full text Add to dashboard Cite

show abstract

“…According to (64), all the power of w n should be concentrated on θ ∈ Υ n , which is an ideal assumption commonly used in the existing literature, such as [28]. In practice, it is difficult to achieve (64) [21].…”

Section: Hybrid Precoding and Combining Matrices Designmentioning

confidence: 99%

High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

2020

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

In this paper, we develop two high-resolution channel estimation schemes based on the estimating signal parameters via the rotational invariance techniques (ESPRIT) method for frequency-selective millimeter wave (mmWave) massive MIMO systems. The first scheme is based on two-dimensional ESPRIT (TDE), which includes three stages of pilot transmission. This scheme first estimates the angles of arrival (AoA) and angles of departure (AoD) and then pairs the AoA and AoD. The other scheme reduces the pilot transmission from three stages to two stages and therefore reduces the pilot overhead. It is based on one-dimensional ESPRIT and minimum searching (EMS). It first estimates the AoD of each channel path and then searches the minimum from the identified mainlobe. To guarantee the robust channel estimation performance, we also develop a hybrid precoding and combining matrices design method so that the received signal power keeps almost the same for any AoA and AoD. Finally, we demonstrate that the proposed two schemes outperform the existing channel estimation schemes in terms of computational complexity and performance.Index Terms-Millimeter wave communications, channel estimation, hybrid precoding, massive MIMO.

show abstract

“…In order to meet larger spectrum channels, and hence, multi Gigabits per second data rates, millimeter wave (mmWave) uses the spectrum from 30 GHz to 300 GHz [2]. It requires highly directive beams that can maximize energy transfer to targeted user [3], [4]. The visible light communication (VLC) is a fast speed technology which provides unregulated bandwidth and high data rate communication for indoor environment [5].…”

Section: Introductionmentioning

confidence: 99%

Linear Massive MIMO Uplink Detector Based On Joint Jacobi and Gauss-Seidel Methods

Albreem

El‐Saleh

Juntti

2020

2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020

View full text Add to dashboard Cite

In fifth generation (5G) cellular system, massive multiple-input multiple-output (MIMO) is utilized to improve the diversity gain, reliability, link robustness, latency, and power and spectral efficiencies. However, a large number of antennas requires sophisticated signal processing to detect data. Although the detection based on maximum likelihood (ML) obtains the best performance, it is not hardware friendly because of the exponential complexity. Therefore, several iterative methods are proposed to estimate the signal without computing the inverse of equalization matrix, and hence, minimize the complexity. The Jacobi (JA) and the Gauss-Seidel (GS) methods achieve a satisfactory performance. However, large iterations' number is in demand which produces a high computational complexity. This paper proposes a detector for massive MIMO uplink (UL) system based on the JA and GS methods. Proposed detector obtains a balance between the performance and the complexity. In this research, initialization is performed based on the JA method. After-that, the estimation is performed based on the GS method. Numerical results show that the proposed JA-GS detector outperforms the GS and the JA based detector. Moreover, proposed JA-GS based detector requires few iterations to obtain the target performance and hence, a considerable reduction in computational complexity is achieved.

show abstract

Two-Step Codeword Design for Millimeter Wave Massive MIMO Systems With Quantized Phase Shifters

Cited by 72 publications

References 27 publications

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

Linear Massive MIMO Uplink Detector Based On Joint Jacobi and Gauss-Seidel Methods

Contact Info

Product

Resources

About