Accurate Channel Estimation for Millimeter-Wave MIMO Systems

Cheng, Xiantao; Tang, Chao; Zhang, Zhongpei

doi:10.1109/tvt.2019.2905640

Cited by 20 publications

(9 citation statements)

References 21 publications

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“…Afterwards, SIC is performed per NG, starting with the decoding of the symbol of user u o (see Algorithm 2, where C denotes the transmitted symbol constellation). For example, if K = 4 active users, then U o = {1, 2, 3, 4} and (1,3), (1,4), (2,3), (2,4), (3,4), (2, 1), (3, 1), (4, 1), (3, 2), (4, 2), (4,3) . Considering now 2 NGs, then if for example in the first iteration the set (2,3) of MSs maximizes the performance metric of Equation ( 7 (1,5), (4, 5), (4, 1), (5, 1), (5,4) .…”

Section: Code Reuse Via Principal Component Analysismentioning

confidence: 99%

“…The deployment of fifth-generation (5G) mobile cellular networks is inextricably connected with the provision of high data rates to mobile stations (MSs) in order support bandwidth demand and zero latency applications [1][2][3]. To this end, various novel technologies have been introduced over the last few years: mmWave transmission [4,5], massive multiple-input multiple-output (MIMO) systems [6,7], as well as non-orthogonal multiple access (NOMA) transmission schemes [8,9]. In the latter case, available network resources (i.e., frequency blocks or codewords) can be reused by certain groups of users, thus improving overall network capacity.…”

Section: Introductionmentioning

confidence: 99%

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Non-Orthogonal Multiple Access in Multiuser MIMO Configurations via Code Reuse and Principal Component Analysis

2020

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The goal of the study presented in this paper is to evaluate the performance of a proposed transmission scheme in multiuser multiple-input multiple-output (MIMO) configurations, via code reuse. Hence, non-orthogonal multiple access (NOMA) is performed. To this end, a correlation matrix of the received data is constructed at the transmitter, with feedback as only the primary eigenvector of the equivalent channel matrix, which is derived after principal component analysis (PCA) at the receiver. Afterwards, users experiencing improved channel quality (i.e., diagonal terms of the correlation matrix) along with reduced multiple access interference (i.e., the inner product of transmission vectors) are the potential candidates for their assigned code to be reused. As the results indicate, considering various MIMO configurations, the proposed approach can achieve almost 33% code assignment gain (CAG), when successive interference cancellation (SIC) is employed in mobile receivers. However, even in the absence of SIC, CAG is still maintained with a tolerable average bit error rate (BER) degradation.

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Section: Code Reuse Via Principal Component Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Orthogonal Multiple Access in Multiuser MIMO Configurations via Code Reuse and Principal Component Analysis

2020

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“…Another generally existed problem is that, although the sparse algorithms can achieve super estimation performance for sparse signals, there also exists a grid mismatch issue. Some superresolution (off-grid) compressed sensing methods and two-stage channel estimation algorithms have been applied to improve channel estimation accuracy [14][15][16][17][18][19][20], which aim to overcome the grid mismatch caused by conventional compressed sensing techniques. However, the super-resolution dictionary learning algorithm has two main shortcomings: the convergence of the results cannot be guaranteed; a large amount of prior training is required for the sparse dictionary.…”

Section: Introductionmentioning

confidence: 99%

GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

Shao

Wang

Lan

et al. 2021

EURASIP J. Adv. Signal Process.

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Based on the finite scattering characters of the millimeter-wave multiple-input multiple-output (MIMO) channel, the mmWave channel estimation problem can be considered as a sparse signal recovery problem. However, most traditional channel estimation methods depend on grid search, which may lead to considerable precision loss. To improve the channel estimation accuracy, we propose a high-precision two-stage millimeter-wave MIMO system channel estimation algorithm. Since the traditional expectation–maximization-based sparse Bayesian learning algorithm can be applied to handle this problem, it spends lots of time to calculate the E-step which needs to compute the inversion of a high-dimensional matrix. To avoid the high computation of matrix inversion, we combine damp generalized approximate message passing with the E-step in SBL. We then improve a refined algorithm to handle the dictionary matrix mismatching problem in sparse representation. Numerical simulations show that the estimation time of the proposed algorithm is greatly reduced compared with the traditional SBL algorithm and better estimation performance is obtained at the same time.

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“…Observing the evolution of generations of mobile communication systems, one easily realizes that there is an endless quest for an equilibrium between serving the exponentially increasing user needs (global wireless traffic volume in 2013 increased 30 times compared to that in 2008 [10]), and developing innovative technologies to enhance operational capabilities and network capacity given the scarce spectrum (wireless communications capacity in 2008 has increased by one million times compared to 1957 [11]). In this context, various solutions have been proposed for the deployment of 5G networks, such as mmWave transmission [12][13][14], massive MIMO systems [15][16][17], non-orthogonal multiple access (NOMA) schemes [18][19][20][21] as well as flexible network deployment along with nomadic nodes [22,23] (e.g., drones, uav, etc.). In the first case, mmWave spectrum covers the range from 30 GHz to 300 GHz (with equivalent wavelengths from 10 to 1 mm).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Study on Simulation Techniques for 5G Networks: State of the Art Results, Analysis, and Future Challenges

2020

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Ιn this review article, a comprehensive study is provided regarding the latest achievements in simulation techniques and platforms for fifth generation (5G) wireless cellular networks. In this context, the calculation of a set of diverse performance metrics, such as achievable throughput in uplink and downlink, the mean Bit Error Rate, the number of active users, outage probability, the handover rate, delay, latency, etc., can be a computationally demanding task due to the various parameters that should be incorporated in system and link level simulations. For example, potential solutions for 5G interfaces include, among others, millimeter Wave (mmWave) transmission, massive multiple input multiple output (MIMO) architectures and non-orthogonal multiple access (NOMA). Therefore, a more accurate and realistic representation of channel coefficients and overall interference is required compared to other cellular interfaces. In addition, the increased number of highly directional beams will unavoidably lead to increased signaling burden and handovers. Moreover, until a full transition to 5G networks takes place, coexistence with currently deployed fourth generation (4G) networks will be a challenging issue for radio network planning. Finally, the potential exploitation of 5G infrastructures in future electrical smart grids in order to support high bandwidth and zero latency applications (e.g., semi or full autonomous driving) dictates the need for the development of simulation environments able to incorporate the various and diverse aspects of 5G wireless cellular networks.

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Accurate Channel Estimation for Millimeter-Wave MIMO Systems

Cited by 20 publications

References 21 publications

Non-Orthogonal Multiple Access in Multiuser MIMO Configurations via Code Reuse and Principal Component Analysis

Non-Orthogonal Multiple Access in Multiuser MIMO Configurations via Code Reuse and Principal Component Analysis

GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

A Comprehensive Study on Simulation Techniques for 5G Networks: State of the Art Results, Analysis, and Future Challenges

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