Ismail Kaya

Experimental Ber Performance Evaluation of OFDM and Single Carrier Transmissions in Real-Time Wimax Radio

Field trails of orthogonal frequency division modulation (OFDM) and single carrier (SC) transmissions are presented in this paper which puts forward experimentally obtained BER performances ahead of simulation works submitted in the literature, instead of giving raw error performance results, experimental studies are also extended to calculate matched filter bound for both OFDM and SC systems, which matches the theoretical limit of real-time wireless medium.

Implementation of a HIPERLAN/1 compatible CMF-DFE equaliser

The channel matched filter decision feedback equaliser (CMF-DFE) is a high performance equalisation method with reduced computational complexity, which is essential for high-speed communication systems with severe intersymbol interference. The method exploits the fact that matched filtering of a wideband channel results in a symmetrical channel profile centred on a real-valued peak while exploiting multipath diversity. This paper describes the implementation of a HIPERLAN/1 compatible equaliser using the CMF-DFE method. The performance of the implemented algorithm and the implementation benchmarks are given for the HIPERLAN/1 standard. Results are also given for a number of different modulation schemes.

Experimental BER Performance Evolution of Equalizations and Matched Filter Bounds for Single-Carrier WiMax Radio in 3.5 GHz

This paper presents an experimental evaluation of bit-error-rate (BER) performances for adaptive training algorithms and obtaining matched filter bound in real-time WiMax (3.5 GHz) radio channels. Without involving with any modified or improved versions, two conventional adaptive equalizer training methods, the least mean squares (LMS) and recursive least squares (RLS), are implemented in a C code running over sampled received sequence and their performances are compared in different modulation techniques before and after coding. In experimental trails the obtained channel impulse responses were generally non-minimum phase and the obtained BER performance of receiver using LMS training has been quite close to whom using RLS particularly after coding, in real-time channel conditions.

Periodic variation method for blind symbol rate estimation

In order to obtain unknown symbol rate of incoming signal at a receiver, in this paper, cyclostationary features of linear digitally modulated signals are exploited by proposed periodic variation method. A low complexity but highly accurate symbol rate estimation technique is obtained. The proposed method is based on a superposed epoch analysis over autocorrelations obtained blindly in different sampling frequencies. The obtained autocorrelations are analyzed in the frequency domain, and it is seen that there are large oscillations when the autocorrelation is obtained around the symbol rate. Then, a superposed epoch analysis is developed in order to estimate symbol rate based of the periodic variations on the frequency responses of autocorrelations. The proposed algorithm is quite accurate in the noisy environment because the noise is having no frequency component after taking Fourier transform of autocorrelations in all sampling rates, and this feature is also valid for the offset frequency that the purposed estimation is not affected by offset frequency. Thus, a successful blind symbol rate estimation algorithm is obtained, and it performs much better error performance than those using the well-known cyclic correlation based symbol rate estimations, as it is proven by the obtained performances presented in the paper. Copyright

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.

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.

Using Wigner-Ville distribution in ECG arrhythmia detection for telemedicine applications

While the worlds population is growing, average life expectancy is increasing. As a result, the growing elderly population is profoundly affecting the delivery of healthcare for everyone and in particular for those with chronic diseases. The remote monitoring of chronic patients may be achieved by a telemedicine system utilizing todays information and mobile communication technologies. In this study, an ECG arrhythmia detection algorithm based on Wigner-Ville distribution is proposed. The performance of the method is tested on a large dataset obtained from the PhysioNet database. Compared to other studies, the proposed method yields better accuracy, sensitivity and specificity results. Furthermore the computation time is suitable for telemedicine applications.

Investigation the effect of the time difference of arrival sets on the positioning accuracy for source localization

Electromagnetic, acoustic, or seismic sources can be positioned using direction, time, frequency or time/frequency difference of their transmitted signals. Source coordinates are found using time difference of arrival (TDOA) method that depends on the time differences of spatially separated receivers. In these methods, two different TDOA sets are used, including independent/spherical and full. In this study, positioning with particle swarm optimization (PSO) is achieved using independent set and full sets and positioning error is compared with Cramer-Rao lower bound (CRLB). In addition, it is proven that CRLB can be exceeded using the PSO algorithm that uses the full TDOA set.