No abstract

Blind equalization is a technique for adaptive equalization of a communication channel without the aid of the usual training sequence. Although the Constant Modulus Algorithm (CMA) is one of the most popular adaptive blind equalization algorithms, it suffers from slow convergence rate. A novel enhanced blind equalization technique based on a supervised CMA (S-CMA) is proposed in this paper. The technique is employed to initialize the coefficients of a linear transversal equalizer (LTE) filter in order to provide a fast startup for blind training. It also presents a computational study and simulation results of this newly proposed algorithm compared to other CMA techniques such as conventional CMA, Normalized CMA (N-CMA) and Modified CMA (M-CMA). The simulation results have demonstrated that the proposed algorithm has considerably better performance than others.

This paper proposes a tracking control method for a certain type of differential-drive wheeled mobile robot, called automated guided vehicles (AGVs), using a continuous mode approach of sliding mode control (SMC). The SMC applied produces a continuous signal in between the U and -U control signals instead of discrete ones. The controller is applied to control the velocity and direction angle of the vehicle in order to keep it on a desired path. The obtained algorithm is applied to the experimental system under load and disturbance to show its robustness. The experimental results are satisfactory and verify the performance of the control algorithm.

The Constant Modulus Algorithm (CMA), although it is the most commonly used blind equalization technique, converges very slowly. The convergence rate of the CMA is quite sensitive to the adjustment of the step size parameter used in the update equation as in the Least Mean Squares (LMS) algorithm. A novel approach in adjusting the step size of the CMA using the fuzzy logic based outer loop controller is presented in this paper. Inspired by successful works on the variable step size LMS algorithms, this work considers designing a training trajectory that it overcomes hurdles of an adaptive blind training via controlling the level of error power (LOEP) and trend of error power (TOEP) and then obtains a more robust training process for the simple CMA algorithm. The controller design involves with optimization of training speed and convergence rate using experience based linguistic rules that are generated as a part of FLC. The obtained results are compared with well-known versions of CMA;

No abstract