Phase Transmittance RBF Neural Network Beamforming for Static and Dynamic Channels

Enriconi, Mateus Prauchner; Castro, Fernando César Comparsi de; Müller, Candice; Castro, Maria Cristina Felippetto de

doi:10.1109/lawp.2019.2958682

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…Substituting ( 8)-( 12) into (13), the final formula of the increase rate of system capacity for the serving BS is In other words, the system capacity for the serving BS can be increased by 2.3%. As indicated by (14), the increase rate of system capacity is related to the number of beam adjustments and total time-frequency resources occupied in the beam management procedure. And the number of beam adjustments is related to factors such as the antenna configurations, the moving speed of the UE, the bandwidth, the SINR threshold of the UE, the applicable channel model, etc.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In general, beamforming can be seen as an adaptation problem, which can be implemented by an artificial neural network. In [14], a phase transmittance radial basis function neural network (RBF-NN) beamforming for static and dynamic channels was proposed. A beamforming neural network was designed to deal with the power minimization problem in [15].…”

Section: Introductionmentioning

confidence: 99%

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Reducing the System Overhead of Millimeter-Wave Beamforming With Neural Networks for 5G and Beyond

et al. 2021

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To accommodate the rapid change of radio propagation environment for mobile communication scenarios, millimeter-wave beamforming requires instantaneous channel state information (CSI) to update its operational parameters in real time, resulting in heavy system overhead. As the number of antennas increases, the system overhead associated with beam management will increase dramatically. To address this overarching problem, a neural network-aided millimeter-wave beamforming algorithm is proposed in this paper. A new parameter, referred to as "beam adjustment interval", is proposed to evaluate the beamforming performance. It is defined as the maximum time duration in which the signalto-interference-plus-noise ratio (SINR) of the user equipment can be maintained above the predefined threshold. Besides, a predictive method of beam adjustment to maximize the beam adjustment interval is developed, which considers the SINR not only at the current location but also future possible locations. Simulation results show that the proposed algorithm can significantly increase beam adjustment interval and reduce the total number of beam adjustments for the moving user equipment, thus reducing the system overhead 41.4% on average over 10 randomly generated test traces.INDEX TERMS Beamforming, beam adjustment interval, millimeter-wave, neural network.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reducing the System Overhead of Millimeter-Wave Beamforming With Neural Networks for 5G and Beyond

et al. 2021

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“…Many DOA estimation methods based on neural networks have been developed in recent years to reduce the computational burden. References [12][13][14][15] using a convolutional neural network (CNN), references [16,17] using a support vector regression (SVR), reference [18] using a residual network, reference [19] using a fully connected neural network (FNN), reference [20] using a long short-term memory network, and references [21,22] using a radial basis function (RBF) achieve high accuracy DOA estimation. However, they can only be used in single-source scenarios, which may be extremely limited in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

Chen

Joo

2022

International Journal of Antennas and Propagation

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This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation.

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“…Under channel linearity and time-invariance constraints, the conventional minimum mean squared error (MMSE) is the optimal linear operator for channel estimation under jointly Gaussian distributed random variables [4]. However, these constraints are unrealistic since even pedestrian channels are dynamic [5], and nonlinearities are common in power amplifiers [6]- [9]. In addition, as the MMSE channel estimation relies on covariance matrix inversions, the computational complexity is extremely expensive [10].…”

Section: Introductionmentioning

confidence: 99%

PCA-based Channel Estimation for MIMO Communications

Soares¹,

Mayer²,

Valadares³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In multiple-input multiple-output communications, channel estimation is paramount to keep base stations and users on track. This paper proposes a novel PCA-based -principal component analysis -channel estimation approach for MIMO orthogonal frequency division multiplexing systems. The channel frequency response is firstly estimated with the least squares method, and then PCA is used to filter only the higher singular components of the channel impulse response, which is then converted back to frequency domain. The proposed approach is compared with the MMSE, the minimum mean square error estimation, in terms of bit error rate versus E b /N0.

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Phase Transmittance RBF Neural Network Beamforming for Static and Dynamic Channels

Cited by 13 publications

References 8 publications

Reducing the System Overhead of Millimeter-Wave Beamforming With Neural Networks for 5G and Beyond

Reducing the System Overhead of Millimeter-Wave Beamforming With Neural Networks for 5G and Beyond

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

PCA-based Channel Estimation for MIMO Communications

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