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Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems

Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems
Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems
Adaptive training of neural networks is typically done using some stochastic gradient algorithm that tries to minimize the mean square error (MSE). For many applications, such as channel equalization and code-division multiple-access (CDMA) multiuser detection, the goal is to minimize the error probability. For these applications, adopting the MSE criterion may lead to a poor performance. A novel adaptive near minimum error rate algorithm called the least bit error rate (LBER) is developed for training neural networks for these kinds of applications. The proposed method is applied to multiuser detection in CDMA communication systems. Simulation results show that the LBER algorithm has a good convergence speed and a small radial basis function (RBF) network trained by this adaptive algorithm can closely match the performance of the optimal Bayesian multiuser detector. The results also confirm that training the neural network multiuser detector using the least mean square (LMS) algorithm, although converging well in the MSE, can produce a poor error rate performance.
0165-1684
1435-1448
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Samingan, A.K.
16b3f372-cc0c-473e-b345-6807442f9fb0
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Samingan, A.K.
16b3f372-cc0c-473e-b345-6807442f9fb0
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, S., Samingan, A.K. and Hanzo, L. (2005) Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems. Signal Processing, 85 (7), 1435-1448.

Record type: Article

Abstract

Adaptive training of neural networks is typically done using some stochastic gradient algorithm that tries to minimize the mean square error (MSE). For many applications, such as channel equalization and code-division multiple-access (CDMA) multiuser detection, the goal is to minimize the error probability. For these applications, adopting the MSE criterion may lead to a poor performance. A novel adaptive near minimum error rate algorithm called the least bit error rate (LBER) is developed for training neural networks for these kinds of applications. The proposed method is applied to multiuser detection in CDMA communication systems. Simulation results show that the LBER algorithm has a good convergence speed and a small radial basis function (RBF) network trained by this adaptive algorithm can closely match the performance of the optimal Bayesian multiuser detector. The results also confirm that training the neural network multiuser detector using the least mean square (LMS) algorithm, although converging well in the MSE, can produce a poor error rate performance.

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Published date: July 2005
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 260941
URI: https://eprints.soton.ac.uk/id/eprint/260941
ISSN: 0165-1684
PURE UUID: 68f0368d-93b0-42c4-af33-9f14b755380f
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 06 Jun 2005
Last modified: 06 Jun 2018 13:14

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