Hybrid beam-forming and beam-switching for OFDM based wireless personal area networks

Yoon, Seokho; Jeon, Taehyun; Lee, Wooyong

doi:10.1109/jsac.2009.091012

Cited by 33 publications

(4 citation statements)

References 13 publications

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“…It is known that 60 GHz wireless channel is characterized by intensive multipath propagation in a typical indoor environment. Currently, the popular channel model adopted in 60 GHz mmWave system is a modified Saleh-Valenzuela(S-V) indoor model [20], which is developed by the IEEE 802.15.3c task group. The channel impulse response (CIR) of this model can be defined as follows:…”

Section: B Channel Modelmentioning

confidence: 99%

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Unsupervised Clustering for Nonlinear Equalization in Indoor Millimeter-Wave Communications

Zhang

Liu

et al. 2019

IEEE Access

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Millimeter-wave (mmWave) systems have been considered as a promising candidate for 5G networks because of their potential advances in significant bandwidth enhancement. However, due to the extremely high operating frequency of mmWave systems, they generally suffer from severe frequencyselective propagation and nonlinear distortion in the power amplifier, which introduces unfavorable impact on the signal detection process. We discover that for indoor mmWave communications, the constellation of signals becomes much more ''clean and tidy'' at the receiver side compared with the current wireless systems (e.g., LTE and WiFi), thanks to the channel sparsity characteristic of mmWave communications. Motivated by this observation, we propose in this paper several detection algorithms. Specifically, K-means clustering (KMC) algorithm is first introduced into clustering signal detection due to its advantage in the circle or spherical cluster shape. Then, an improved KMC detector is proposed to avoid the deficiencies of KMC for the error floor and high complexity. Moreover, a density and distance-based clustering detector, a non-iterative algorithm, is proposed and it does not need to preset the number of clusters. The aboveproposed algorithms do not require any prior information about the power amplifier and the channel state information at the receiver end, which presents noticeable practical achievements on cost, complexity, and hardware constraints. The simulation results verify the effectiveness of the proposed schemes.INDEX TERMS Millimeter-wave (mmWave) communication, nonlinear signal detection, nonlinear power amplifier, unsupervised clustering.

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Section: B Channel Modelmentioning

confidence: 99%

“…random phase ψ k,l distributed over U [0, 2π), τ k,l is the time delay, θ k,l and φ k,l are the angle of arrival and of departure, respectively. All channel parameters are random, and their distributions for some typical environment settings can be found in [20].…”

Section: B Channel Modelmentioning

confidence: 99%

Unsupervised Clustering for Nonlinear Equalization in Indoor Millimeter-Wave Communications

Zhang

Liu

et al. 2019

IEEE Access

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“…where L denotes the number of clusters; K l denotes the number of sub-paths of the lth cluster; α k, l is the complex channel gain of the kth sub-path of the lth cluster; τ k, l is the time delay; and θ k, l and φ k, l are the angle of arrival (AoA) and the angle of departure (AoD), respectively [19]. Accordingly, the SNR in the receiver can be written as:…”

Section: System Modelmentioning

confidence: 99%

Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm

Sun

Jiang

2019

Future Internet

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The codebook-based beamforming for millimeter-wave (mm Wave) communication systems is usually used to compensate the severe attenuation of the mm Wave region. The beam training process based on pre-specified beam codebooks is considered a global optimization problem in 2-D planes formed by the potential beam index. The Rosenbrock algorithm (RA) is adopted to implement optimum beam searching whereas the simulated annealing (SA) algorithm is used to solve the problem of falling into the local optimum, due to the unavailable gradient information of the objective function. However, the RA implements rounding to the integer which leads to the problem of repeated search and beam space discontinuity caused by beam index will impair the powerful local search ability. Thus, in this paper, an enhanced RA based on tabu search and combined with SA algorithm is proposed as an alternative solution for beam search success rate. The proposed algorithm reduces the search times by forbidding the repeat search with tabu table and design of neighbor region. Moreover, to prevent the search failure, the search candidate index is defined to keep the local search ability of the original algorithm and wrap around of beam index is applied to maintain continuity of the search direction. Experimental simulations show that the proposed technique can improve the search efficiency in terms of reduced steps and increase search success rate during the beam training procedure compared to existing techniques.

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“…With the rapid evolution and upgrading of wireless networks, the amount of new mobile service data and the number of users has shown an exponential growth trend, and the demand for mobile data traffic has also shown explosive growth [1]- [2]. To meet the demand of large-capacity, high-speed and low-latency services, the communication frequency band is rapidly expanding to the millimeter wave [3]. The bandwidth of a millimeter wave communication system can reach to 800 MHz, and the pertinent communication rate is able to achieve 10Gbps, which can definitely meet the requirements of the International Telecommunication Union (ITU) for 5G communication systems [4]- [5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Beamforming With Deep Learning for Large-Scale Antenna Arrays

Jiang

2021

IEEE Access

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The emergence of highly directional beamforming technology makes millimeter wave frequency band communication possible in future wireless communication networks. Based on the multipath characteristics of millimeter wave frequency communication, a high-precision multipath channel estimation algorithm based on signal subspace is proposed. In the mobile terminal, an iterative heuristic radio frequency combination algorithm based on spatial points is proposed. The analog precoding at the base station uses deep learning to accelerate the calculation, and then the multi-user communication is modeled to design the digital precoding. The simulation results show that the multi-channel estimation algorithm can estimate 4 paths with an error of no more than 0.3 rad. The proposed DL algorithm takes only 20% of the time when it is close to the 87% spectral efficiency of the traditional algorithm.INDEX TERMS Millimeter wave, hybrid beamforming, large-scale antenna arrays, multiple-input multipleoutput (MIMO), channel estimation, space point iteration, deep learning (DL).

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Hybrid beam-forming and beam-switching for OFDM based wireless personal area networks

Cited by 33 publications

References 13 publications

Unsupervised Clustering for Nonlinear Equalization in Indoor Millimeter-Wave Communications

Unsupervised Clustering for Nonlinear Equalization in Indoor Millimeter-Wave Communications

Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm

Hybrid Beamforming With Deep Learning for Large-Scale Antenna Arrays

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