Browsing by Author "El Nahal W."

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    New blind equalization technique for Constant Modulus Algorithm (CMA)
    (2010) Nassar A.M.; El Nahal W.; Electronics and Communication Dept.; Faculty of Engineering; Cairo University; Giza; Egypt; MSA University; Communication Dept.; 6th October; Egypt
    Equalization plays an important role for the communication system receiver to correctly recover the symbol send by the transmitter, where the received signals may contain additive noise and intersymbol interference (ISI). Blind equalization is a technique of many equalization techniques at which the transmitted symbols over a communication channel can be recovered without the aid of training sequences, recently blind equalizers have a wide range of research interest since they do not require training sequence and extra bandwidth, but the main weaknesses of these approaches are their high computational complexity and slow adaptation, so different algorithms are presented to avoid this nature. The most popular blind algorithm which has a wide acceptance is the Constant Modulus Algorithm (CMA). The performance of CMA suffers from slow convergence rate or adaptation which corresponds to various transmission delays especially in wireless communication systems, which require higher speed and lower bandwidth. This paper introduces a new blind equalization technique, the Exponentially Weighted Step-size Recursive Least Squares Constant Modulus Algorithm (EXP-RLS-CMA), based upon the combination between the Exponentially Weighted Step-size Recursive Least Squares (EXP-RLS) algorithm and the Constant Modulus Algorithm (CMA), by providing several assumptions to obtain faster convergence rate to an optimal delay where the Mean Squared Error (MSE) is minimum, and so this selected algorithm can be implemented in digital system to improve the receiver performance. Simulations are presented to show the excellence of this technique, and the main parameters of concern to evaluate the performance are, the rate of convergence, the mean square error (MSE), and the average error versus different signal-to-noise ratios. �2010 IEEE.