Low complexity neural network structure for implementing the optimum maximum-likelihood multi-user receiver in a DS-CDMA communication system

The capacity of direct-sequence code division multiple access systems is interference limited, particularly by multiple-access interference produced by other co-channel users. The optimum multi-user receiver calculates the maximum-likelihood ratio of the detected data for all users simultaneously, b...

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Bibliographic Details
Main Authors: Khoshbin-Ghomash, H., Ormondroyd, R.F., Dunn, R.W.
Format: Conference Proceeding
Language:English
Subjects:
Computational complexity
Detectors
Matched filters
Maximum likelihood detection
Maximum likelihood estimation
Multiaccess communication
Multiple access interference
Multiuser detection
Neural networks
Recurrent neural networks
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Summary:The capacity of direct-sequence code division multiple access systems is interference limited, particularly by multiple-access interference produced by other co-channel users. The optimum multi-user receiver calculates the maximum-likelihood ratio of the detected data for all users simultaneously, but it has a complexity that grows exponentially with the number of users. In this paper, a neural network approach to multi-user detection is considered. It is shown that the performance of this receiver is the same as the maximum-likelihood multi-user receiver but it has a much lower computational complexity.
ISSN:1090-3038
2577-2465
DOI:10.1109/VETECF.1999.798408