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Convergence analysis of the constant modulus algorithm

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2 Author(s)
O. Dabeer ; Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, CA, USA ; E. Masry

We study the global convergence of the stochastic gradient constant modulus algorithm (CMA) in the absence of channel noise as well as in the presence of channel noise. The case of fractionally spaced equalizer and/or multiple antenna at the receiver is considered. For the noiseless case, we show that with proper initialization, and with small step size, the algorithm converges to a zero-forcing filter with probability close to one. In the presence of channel noise such as additive Gaussian noise, we prove that the algorithm diverges almost surely on the infinite-time horizon. However, under suitable conditions, the algorithm visits a small neighborhood of the Wiener filters a large number of times before ultimately diverging.

Published in:

IEEE Transactions on Information Theory  (Volume:49 ,  Issue: 6 )