PAPR Reduction and Data Security Improvement for OFDM Technique Using Chaos System

Orthogonal Frequency Division Multiplexing (OFDM) is the most popular multicarrier technique because it produces several advantages such as higher spectral efficiency, high transmission rate, robustness to fading channel and etc.. In this technique, the data is carrying by multiple orthogonal subcarriers. If all subcarriers are adding together with the same phase, it will result high Peak to Average Power Ratio (PAPR). Higher value of PAPR makes low power efficiency, several degradation of performance in the transmit power amplifier and increase the complexity of converters. It is important to decrease PAPR for avoid these problems. Another requirement of the modern communication system is the security of transmission data. All these issues make strong motivation for building algorithm to improve performance and security of OFDM system. In this paper, a proposed algorithm is presented to both reduce PAPR and secure the OFDM signal by generating several Aperiodic PseudoRandom Binary Sequences (APRBSs) using chaos system. The proposed algorithm is scrambling the information by APRBSs, and one sequence is chosen for transmission which has smallest PAPR value. To inform receiver which sequence had been sent, a Side Information (SI) is enclosed with the transmitted sequence. Because SI very important at receiver, convolutional code with Viterbi-Soft Decision Decoding (V-SDD) is used to protect it against channel distortion. Simulation results state the proposed algorithm produces excellent PAPR reduction performance and approximately gives the same Bit Error Rate (BER) of the conventional OFDM system over AWGN and fading channels. In addition to get better performance, the proposed algorithm is providing a good data security due to chaos system. MATLAB program is used to build the proposed OFDM system and get the simulation results

Effect of the initial population construction on the DBMEA algorithm searching for the optimal solution of the traveling salesman problem

There are many factors that affect the performance of the evolutionary and memetic algorithms. One of these factors is the proper selection of the initial population, as it represents a very important criterion contributing to the convergence speed. Selecting a conveniently preprocessed initial population definitely increases the convergence speed and thus accelerates the probability of steering the search towards better regions in the search space, hence, avoiding premature convergence towards a local optimum. In this paper, we propose a new method for generating the initial individual candidate solution called Circle Group Heuristic (CGH) for Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is built with aid of a simple Genetic Algorithm (GA). CGH has been tested for several benchmark reference data of the Travelling Salesman Problem (TSP). The practical results show that CGH gives better tours compared with other well-known heuristic tour construction methods