A New Adaptive Linear Multiuser Detector based on Approximate Negentropy Minimization

Sooyong Choi, Te Won Lee

Research output: Contribution to conferencePaperpeer-review


In this paper, we introduce an information theoretic learning method as a new approach to multiuser detection. We propose a new adaptive linear multiuser detector based on approximate negentropy minimization of the output error and investigate its characteristics and performance. Negentropy includes higher order statistical information and its minimization provides improved converge and performance compared to traditional methods such as minimum mean squared error. The proposed algorithm is derived under the assumption that a Gaussian variable has the largest entropy among all random variables of unit variance and hence a normalization process is required. Simulation experiments show that our multiuser detector has similar bit error rate (BER) characteristics to the least BER multiuser detector. Furthermore, the proposed detector has faster convergence speed than the LBER detector.

Original languageEnglish
Number of pages5
Publication statusPublished - 2003
EventIEEE Global Telecommunications Conference GLOBECOM'03 - San Francisco, CA, United States
Duration: 2003 Dec 12003 Dec 5


OtherIEEE Global Telecommunications Conference GLOBECOM'03
Country/TerritoryUnited States
CitySan Francisco, CA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Global and Planetary Change


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