Emin Tugcu

Novel composite method for determining the location of the transmitter using Particle Swarm Optimization

In this study, the Time Difference of Arrival Averaging (TDOAA) method is studied to minimize the estimation error and increase the accuracy for emitter location finding using Particle Swarm Optimization (PSO). We combined TDOAA method with both classic and improved version of PSO and found out a considerable performance increase on the location finding of the transmitter. The improved PSO is to combine different tasks of implementations of PSO for a single decision. Therefore the improved PSO also means more accuracy but price is paid for more complexity. However, the increase on complexity does not prohibit applying the technique, since coherence time for decision may well tolerate more complexity when using todays state of art microprocessors processing power.

Fast Convergence Algorithm for Blind Channel Estimation and Equalization Using CMF-DFE

A novel fast blind equalizer is obtained by using the direct calculations from a channel matched filter decision feedback equalizer (CMF-DFE). The proposed technique converts the inverse convolution operations of an equalizer into a linear finite impulse response estimation filter, which is more suitable for blind training. A novel error function is introduced for blind training which enables the use of fast algorithms such as LMS or RLS. The required auto-regression values for the CMF-DFE equalizer are calculated from the incoming data. The resulting performance with LMS training is close to that of non-blind techniques.

The effects of sea environmental conditions on the underwater acoustic communication systems

This research is focused on the effect of the most important environmental conditions (temperature, salinity and wind speed) and distance between transmitter and receiver for the underwater acoustic communication (UWAC) system. These conditions may change the characteristics of the communication channel and need to be taken into account during implementation process. As an example, we chose different regions from Turkey to figure out the most suitable areas for UWAC.

A novel variable step size constant modulus algorithm employing cross correlation between channel output and error signal

A novel variable step size constant modulus algorithm (VSS-CMA) employing cross correlation between channel output and error signal has been proposed as a solution to the problem of slow convergence of CMA algorithm. The new algorithm resolves the conflict between the convergence rate and low steady state error of the fixed step-size conventional CMA algorithm. Computer simulations have been performed to verify the performance of the proposed method in frequency selective Rayleigh fading channels. The proposed technique has been compared with the popular non-blind equalizer, Least Mean Squares (LMS) algorithm, and blind equalizer, the conventional CMA and Zhaos VSS-CMA as benchmarks. The obtained simulation results have demonstrated that the proposed VSS-CMA algorithm has considerably better performance than the conventional CMA and Zhaos VSS-CMA blind equalization algorithms, and the conventional LMS non-blind equalization algorithm.

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.

Particle swarm optimization algorithm based decision feedback equalizer for underwater acoustic communication

In this paper, we proposed particle swarm optimization (PSO) algorithm based adaptive decision feedback equalizer (DFE) for underwater acoustic communication (UWAC). In the literature, although ocean ambient noise is generally modeled as pink Gaussian noise, there is also site-specific ocean noise which can be modeled as pink Laplace noise. In this study we consider both noises. Rayleigh distributed, frequency selective fading channels (as UWAC channel) with Laplacian and Gaussian distributed, pink noise are considered. Unlike recursive least squares (RLS) and least mean squares (LMS) algorithms, PSO is independent from channel characteristics and has faster convergence. To the best of our knowledge PSO algorithm has not been used for adaptive DFE over UWAC channel. The communication performances and computational complexities of LMS, RLS and PSO based adaptive DFEs are compared. Although PSO has the highest computational complexity, our simulation results show PSO-DFE outperforms the other algorithms.

A novel PSO based blind channel estimation and equalization

As an alternative technique to well-known constant modulus algorithm (CMA), a Decision Feedback Equalizer via Channel Matched Filter (CMF-DFE) based blind channel estimation and equalization algorithm is proposed in this paper. The proposed technique employs Particle Swarm Optimization (PSO) in training, where the conventional CMA and least mean squares (LMS) based training algorithms are found slow and their convergence strictly depend on the step size parameter. On the other hand, if the PSO training algorithm is employed, it provides a faster convergence and less sensitivity to training parameters. Thus, a rapid converging high performance blind channel estimation and equalization method is obtained, as it is compared to CMF-DFE based blind LMS and CMA algorithm. Here, the price is paid for more complexity, however the obtained performance promises to compete with reference training sequence based parameter estimation techniques.

Novel cascaded Turbo-Permutation coding for frequency selective Rayleigh fading channels

Novel cascaded coding method is proposed to overcome inter symbol interference (ISI), composed of frequency selective Rayleigh fading channels in this paper. The proposed method consists of combined Turbo Coding (TC), Permutation Coding (PC) and M-FSK modulation. Computer simulations are performed to verify the efficiency of the proposed method (TPC) and compare with Reed-Solomon and Convolutional Coding (RS-CC) and Turbo coding (TC) in frequency selective Rayleigh fading channels. In fact, PC helps to remove the error floor of an equalizer and M-FSK helps to recover data in highly noisy environment, and finally TC is successful reducing error rate. Therefore, the proposed technique provides high SNR improvement of approximately 6 dB in AWGN and 5 dB in frequency selective channels.

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.