Article thumbnail
Location of Repository

Block-Adaptive Kernel-Based CDMA Multiuser Detector

By S. Chen and L. Hanzo


The paper investigates the application of a recently introduced learning technique, called the relevance vector machine (RVM) to construct a block-adaptive kernel-based nonlinear multiuser detector (MUD) for direct-sequence code-division multiple-access (DS-CDMA) signals transmitted through multipath channels. It is demonstrated that the RVM MUD can closely match the performance of the optimal Bayesian one-shot detector, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique

Year: 2002
OAI identifier:
Provided by: e-Prints Soton
Sorry, our data provider has not provided any external links therefore we are unable to provide a link to the full text.

Suggested articles


  1. (1993). A clustering technique for digital communications channel equalisation using radial basis function networks,” doi
  2. (1990). A family of suboptimum detectors for coherent multiuser communications,” doi
  3. (1998). A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery,
  4. (1998). Adaptive detection for DS-CDMA,” doi
  5. (2001). Adaptive multiuser receiver using support vector machine technique,” doi
  6. Adaptive wireless transceivers: Turbo-Coded, Turbo-Equalised and Space-Time Coded TDMA, CDMA and OFDM systems, doi
  7. (1995). An adaptive direct-sequence code-division multipleaccess receiver for multiuser interference rejection,” doi
  8. (1992). Bayesian interpolation,” Neural Computation, doi
  9. (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers,” doi
  10. (1999). Efficient training of RBF networks for classification,” in doi
  11. (1994). MMSE interference suppression for direct-sequence spread-spectrum CDMA,” doi
  12. (1994). Neural network techniques for adaptive multiuser demodulation,” doi
  13. (1992). Neural networks for multiuser detection in code-division multiple-access communications,” doi
  14. (1993). NonlinearProgramming: Theory and Algorithms.
  15. (1996). Radial basis function receivers for DSCDMA,” Electronics Letters, doi
  16. (2001). Support vector machine multiuser receiver for DS-CDMA signals in multipath channels,” doi
  17. (1992). The evidence framework applied to classification networks,” doi
  18. (1995). The Nature of Statistical Learning Theory. doi
  19. (2000). The relevance vector machine,”
  20. (1997). Volterra based receivers for DSCDMA,” in doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.