Neural networks for multiuser detection in code-division multiple-access communications

Aazhang, Behnaam; Paris, B.-P.; Orsak, G.C.

doi:10.1109/26.153366

Cited by 215 publications

(74 citation statements)

References 23 publications

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“…The most popular solution for the detector (13) is the MMSE solution given by (15) where is the th column of . The linear detector (13) is computationally very simple, and standard least mean square (LMS) or recursive least squares (RLS) algorithms can be used to implement the MMSE solution adaptively.…”

Section: The Linear and Optimal Detectorsmentioning

confidence: 99%

See 1 more Smart Citation

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels

Chen

Samingan

Hanzo

2001

IEEE Trans. Neural Netw.

111

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Section: The Linear and Optimal Detectorsmentioning

confidence: 99%

“…In the work [15], a multilayer perceptron (MLP) was applied to a CDMA system without intersymbol interference (ISI). The experience shows that the MLP MUD has better performance than the linear MUD but training times are long and unpredictable.…”

mentioning

confidence: 99%

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels

Chen

Samingan

Hanzo

2001

IEEE Trans. Neural Netw.

111

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“…However, to find the optimum is a NP -hard problem as the number of users grows. Many authors proposed suboptimal linear and nonlinear solutions such as Decorrelating Detector, MMSE (Minimum Mean Square Error) detector, Multistage Detector, Hoppfield neural network or Stochastic Hoppfield neural network [1,2,3,4], and the references therein. One can find a comparison of the performance of the above mentioned algorithms in [5].…”

Section: Introductionmentioning

confidence: 99%

Quantum Signal Processing

Benslama¹,

Benslama²,

Skander³

2017

Quantum Communications in New Telecommunications Systems

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Abstract3G and 4G mobile are based on CDMA technology. In order to increase the effectiveness of CDMA receivers large amount of effort is invested to develop suitable multi-user detector techniques. However, at this moment there are only suboptimal solutions available because of the rather high complexity of optimal detectors. One of the possible receiver technologies can be the quantum assisted computing devices which allows high level parallelism in computation. The first commercial devices are estimated by 2004, which meets the advert of 3G and 4G systems. In this paper we introduce a novel quantum computation based Quantum Multi-user detection (QMUD) algorithm, employing simple Positive Operation Valued Measurement (POVM), which provides optimal solution. The proposed algorithm is robust to any kind of noise.

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“…The first neural network work based suboptimal detectors were first proposed by Azhang [4]. This classical paper ignited the research on neural network receivers and was followed by several other works [5].…”

Section: Introductionmentioning

confidence: 99%

Effects of Training Parameters on Neural Network Parallel Interference Canceller for CDMA

Joseph¹,

Francis²

2013

IJFCC

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Abstract-Reduction of multiple access interference is occupying great importance in wireless communication using code division multiple access (CDMA). Different interference cancellation schemes were developed and neural network based canceller is one of them. This paper introduces the effect of training parameters on achieving optimum performance on a multi stage parallel interference canceller (PIC) based on neural network.Index Terms-BER, CDMA, LM algorithm, neural network.

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Neural networks for multiuser detection in code-division multiple-access communications

Cited by 215 publications

References 23 publications

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels

Quantum Signal Processing

Effects of Training Parameters on Neural Network Parallel Interference Canceller for CDMA

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