Radial basis function neural network algorithm for adaptive beamforming in cellular communication systems

Zooghby, A.H. El; Christodoulou, C.G.; Georgiopoulos, Michael

doi:10.1109/apwc.1998.730645

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…El Zooghby et al published detailed descriptions and results for adaptive beamforming algorithm to approximate Wiener beamforming weight evaluation algorithm using RBF neural networks for cellular [10,11,12,13] and satellite communications systems [14]. These are based on approximating the subspace multiple signal classification (MUSIC) DoA estimation algorithm and Wiener filter weight evaluation with respect to DoA estimates with the spatial correlation matrix estimated from input signals.…”

Section: Neuroadaptive Beamforming Using Rbfnnsmentioning

confidence: 98%

“…The simulation model integrates parameter input, data generation, network training, and simulation with beamforming weight graphical output to compare exact Wiener output and neural network estimate, integrated on the graphical user interface. It aims to verify the algorithm against claims in [12,13,17]; and provides a tool to investigate the algorithm strengths, weaknesses, and limitations. It also benchmarks the future software and hardwaresoftware codesign implementations in performance and accuracy.…”

Section: Functional-level Model With Matlabmentioning

confidence: 99%

“…The detailed mathematical derivation of the training algorithm and (3) is shown in [10,11,12,13,15]. The neural network is expected to estimateŴ opt from the trained neural network by presenting unseen spatial correlation vectors to the network input during the performance stage.…”

Section: Neuroadaptive Beamforming Using Rbfnnsmentioning

confidence: 99%

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Prototyping Neuroadaptive Smart Antenna for 3G Wireless Communications

Salčić

Nguang

2005

EURASIP J. Adv. Signal Process.

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This paper describes prototyping of a neuroadaptive smart antenna beamforming algorithm using hardware-software implemented RBF neural network and FPGA system-on-programmable-chip (SoPC) approach. The aim is to implement the adaptive beamforming unit in a combination of hardware and software by estimating its performance against the fixed real-time constraint based on IMT-2000 family of 3G cellular communication standards

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Section: Neuroadaptive Beamforming Using Rbfnnsmentioning

confidence: 98%

Section: Functional-level Model With Matlabmentioning

confidence: 99%

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Prototyping Neuroadaptive Smart Antenna for 3G Wireless Communications

Salčić

Nguang

2005

EURASIP J. Adv. Signal Process.

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“…W. Li [2], used a special king of Recurrent Neural Networks (RNN) for performance evaluation of digital beam forming strategies for satellite communications. T. Lo [3], T. Xinhai [4] [19] have hired the Radial Basis Function (RBF) neural network for direction of arrival estimation. The RBF is the most used neural network in this field.…”

Section: Introductionmentioning

confidence: 99%

Blind source separation and bearing estimation using Fourier- and wavelet-based spectrally condensed data and artificial neural networks for indoor environments

Gharavol

Leong

Mouthaan

2008

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

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A new method for blind separation and bearing estimation of wavefronts in a smart antenna scheme, which is based on the usage of artificial neural networks (ANN) is presented here. Because of "the curse of dimensionality," especially in the cases having many antenna elements, in uniform linear, circular or planar arrays, it is important to find a method which makes it feasible to use the ANNs. The proposed method, do not walk along the road of well-known method of correlation-coefficient training. In contrast this method uses the truncated version of their spectral representations. The Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) are employed to provide the spectral representations. The simulation scenario is set up to demonstrate that the results is applicable to realistic cases such as urban, non-line of sight, and indoor environments. For the sake of this purpose, coherent signals are employed in simulations. In this case, most conventional methods are not applicable, because they are built on some statistical assumptions which implies that the received signals by array must be independent.

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“…Hence the family of receivers based on linear combiners are subject to a severe performance degradation. As a consequence, numerous non-linear beamforming schemes [2][3][4] mostly based on Radial Basis Function Networks (RBFN) [5] have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Radial basis function network assisted wide-band beamforming

Wolfgang

Chen

Hanzo

IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004

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In this paper we present a novel Radial Basis Function Network (RBFN) assisted wide-band beamformer which is based on the derivation of the Bayesian detector. The proposed receiver structure significantly outperforms conventional linear wide-band beamformers in terms of the achievable angular resolution and thus the receiver is capable of supporting a higher number of users. For the further enhancement of the BER performance and for the sake of complexity reduction a decision feedback aided RBF scheme is proposed.

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Radial basis function neural network algorithm for adaptive beamforming in cellular communication systems

Cited by 4 publications

References 3 publications

Prototyping Neuroadaptive Smart Antenna for 3G Wireless Communications

Prototyping Neuroadaptive Smart Antenna for 3G Wireless Communications

Blind source separation and bearing estimation using Fourier- and wavelet-based spectrally condensed data and artificial neural networks for indoor environments

Radial basis function network assisted wide-band beamforming

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