A Novel Approach for Blind Channel Equalization
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
On the performance improvements of max-SINR equalizers in wireless communications
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Variable Step-Size Constant Modulus Algorithm Employing Fuzzy Logic Controller
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An improved combination of constant modulus algorithms used in underwater acoustic channels
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
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The constant-modulus algorithm (CMA), while the most commonly used blind equalization technique, converges very slowly. We propose a "normalized" constant-modulus algorithm (analogous to the widely used normalized LMS algorithm) with an adjustable step size that greatly increases the convergence rate for noise colorings with large eigenvalue spreads. The normalized step size is proportional to that required to achieve the desired modulus with the current data vector. Only a few extra operations per update are required. Many applications now using the constant modulus algorithm should achieve greatly improved convergence rates at almost negligible computational increase by adopting the new normalized CMA algorithm.