A convex optimization approach to blind channel shortening in multicarrier modulations

Authors:
Huy-Dung Han;Zhi Ding
Affiliations:
University of California, Davis, CA;University of California, Davis, CA
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

Citing 6
Cited 0

Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming

Journal of the ACM (JACM)
Semidefinite programming

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Convex Optimization

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Second-order statistical approaches to channel shortening in multicarrier systems

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Abstract

We study the problem of channel shortening in multicarrier modulation systems without training. We reformulate two existing methods, the sum-squared and the sum-absolute autocorrelation minimization algorithms (SAM and SAAM), into semidefinite programming to overcome their shortcoming of local convergence. We present the original SAM and SAAM cost functions into as a batch optimization problem before relaxing the original problem into globally convergent semidefinite programming algorithms. Our batch processor is superior to the original stochastic gradient algorithms in terms of achievable bit rate and signal to interference and noise ratio (SINR).