Birol Soysal

A Supervised Constant Modulus Algorithm for Blind Equalization

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.

Design of a Fuzzy Based Outer Loop Controller for Improving the Training Performance of LMS Algorithm

Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is a dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3], variable step size (VSS) LMS-DFE [4], fuzzy LMS-DFE [5,6] and RLS-DFE [7]. The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.

An adaptive compensator for a vehicle driven by DC motors

Abstract A vehicle system driven by two independent DC motors is presented here, one of which is used for the right wheel and the other is used for the left wheel. An adaptive compensator using Takagi–Sugeno fuzzy systems is proposed to control the vehicle system. The compensator includes an adaptive model identifier and adaptive controller. An online method is used to adjust the parameters of the identifier model to match the behavior model of the vehicle system. Then, the parameters of the identifier model are employed in a standard parallel-distributed compensator to provide asymptotically stable equilibrium for the closed-loop vehicle drive system, in which the velocity and direction angle of the vehicle are controlled. Results demonstrate that the proposed controller structure is robust to load changes and follows different trajectories very well.

A fuzzy logic based adaptive whitening for blind channel equalization

The use of a blind adaptive whitening filter to improve performance of a blind equalizer adapted by the constant modulus algorithm (CMA) is investigated in this paper. Since the desired performance can not be achieved by the least mean squares (LMS) algorithm for linear estimation in adaptive whitening filter, it is aimed that a fuzzy logic is adapted to increase convergence rate. The simulation results show that the proposed method increases the performance of blind equalizer significantly with whitening filter adapted by LMS algorithm.

Sensorless control of an automated guided vehicle based on extended Kalman filter observer

In this paper, sensorless velocity and direction angle control of an automated guided vehicle (AGV) is performed by using extended Kalman filter (EKF) observer. The speed information of each motor is estimated by using armature current data measured from dc motor and command voltages applied to motors. The command voltages are used rather than the measured ones therefore only current sensors are needed. The estimated speeds are used to estimate velocity and direction angle of vehicle. The observed velocity and direction angle are used in the closed loop instead of measured ones for sensorless control of AGV. The experimental results show that the EKF observer can perfectly be implemented for sensorless control without using mechanical sensor.

Experimental performances of blind adaptive equalization algorithms for single carrier real-time WiMAX radio

Experimental performances of blind adaptive training algorithms have been evaluated in real-time WiMax (3.5GHz) radio channels in this paper. Inter Symbol Interference (ISI) cancellation and Mean Square Error (MSE) performances of the most commonly used blind equalization techniques, Constant Modulus Algorithm (CMA), and CMA based modified CMA (M-CMA) and normalized CMA (N-CMA) algorithms are investigated in study.

A Novel Approach for Blind Channel Equalization

The Constant Modulus Algorithm (CMA), while 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. It also presents a computational study and simulation results of this newly proposed algorithm compared to other variable step size CMA such as conventional CMA, Normalized CMA (N-CMA) [1], Modified CMA (M-CMA) [2], CMA-Soft Decision Directed (CMA-SDD) [3]. The simulation results have demonstrated that the proposed algorithm has considerably better performance than others.

The Effects of Fuzzy Logic on Carrier Frequency Offset Tracking Performance of Single and Multi Carrier Communication Systems

In this paper, carrier frequency offset (CFO) tracking performances of channel equalizers for single (SC) and multi carrier (MC) systems used in frequency selective Rayleigh fading channels are investigated. The performances of both systems for different values of CFO are compared. Since the desired performance cannot be achieved by CFO tracking ability of conventional LMS for SC and MC systems used in time varying channels, a fuzzy logic based LMS (F-LMS) algorithm is proposed. CFO tracking performance with the proposed F-LMS algorithm becomes very close to the performance of RLS algorithm with a small and negligible increment in computational complexity. Computer simulations using conventional LMS, normalized LMS, modified LMS, variable step size LMS-DFE, B-LMS-DFE and conventional RLS-DFE algorithms are performed in SC systems. The same algorithms are also used in MC (OFDM) systems in which the channel is estimated in time domain then used in frequency domain channel equalizer. For performance comparisons, the HIPERLAN/1 (SC) and HIPERLAN/2 (OFDM) standards are used as the working platforms during simulations. The obtained and compared results are given in Section 3

A method for the channel estimation and tracking of OFDM systems based on a neural network outer loop controller for the LMS training algorithm

The OFDM system is found to be very robust for high data rate communication over multipath channels with a significant intersymbol interference (ISI). The time-domain channel estimation for an OFDM system results in a 3 dB better BER performance. However, the training speed of the LMS needs to be improved since the training sequence has a shorter length and variations on amplitude. In particular, the known waveform of the training sequence forces the LMS to be unstable and perform a poor training when the amplitude is smaller in three regions of the training sequence. This study proposes a neural network (NN) type experience based learning algorithm to design a training trajectory for the LMS algorithm by an outer loop controller. The outer loop controller uses the magnitude of simultaneous error function and time in order to learn a training route for the LMS. The obtained results show that the NN-based LMS performs much better when it is compared to those using the conventional LMS algorithm in the time domain channel estimation of the OFDM system. The introduced complexity does not prohibit the technique since the current DSP processing capabilities are significant for such considered applications.

A time domain carrier frequency offset estimation method for OFDM systems

A time domain carrier frequency offset estimation method is proposed for OFDM systems. The essence of the proposed method is based on the correlation features of short and long synchronisation symbols, which appear at the beginning of each frame as the training sequence. The noise effect on the estimation is minimised by averaging over several estimations. The time-domain operations and correcting bigger carrier frequency offsets compared to the work by P.H. Moose (see IEEE Trans. Commun., vol.42, no.10, p.2908-14, 1994) are the important features of this study. The proposed method has a lower complexity and better BER performance for higher frequency offset values compared to the referenced studies.