Increasing the Performance of OFDM Systems by PAPR Reduction in PTS Technique using Election Optimization Algorithm

Orthogonal Frequency Division Multiplexing (OFDM) is a useful technology in wireless communications that provides high-rate data transmission in multipath fading channels. The advantages of OFDM systems are the high spectral efficiency and strong resistance to frequency selective fading. In OFDM systems, a large number of sub-carriers are used to modulate the symbols causing the time-domain OFDM signal to have a large dynamic range, or a high peak-to-average power ratio (PAPR). When the signals are applied to a nonlinear power amplifier, the OFDM systems’ performance is degraded by the high PAPR. In recent years, several works have been done to reduce the PAPR of OFDM systems. One of the most well-known methods is a partial transmit sequence (PTS). Regardless of the PTS advantages, it suffers from a high computational complexity. Because it requires an exhaustive search over all possible combinations of phase factors. The computational complexity of the PTS increases with increasing the number of phase factors and sub-blocks. There are several approaches to overcome the computation complexity issue of the PTS technique. The majority of these methods mainly employed swarm intelligence and evolutionary optimization algorithms to resolve the PTS shortcoming. These methods report encouraging results, however, their performance is far from the ideal state. This highlights that improving the performance of PTS is an open problem and there is room for more improvement. As an element of research, we propose an optimization approach based on the election algorithm (EA) to overcome the computational complexity of the PTS technique. To realize this goal, we improve the EA algorithm by introducing a new version of positive advertisements operator. The new operator efficiently improves the search capability of the EA through balancing between the exploration and exploitation power of the algorithm. The proposed EA based PTS (EA-PTS) approach, by searching the optimal phase factors, imposes less computational complexity on the system and reduces the PAPR to an acceptable level. The proposed method is compared with the optimal PTS (O-PTS), genetic algorithm-based PTS (GA-PTS) and imperialism competition algorithm based PTS (ICA-PTS) techniques. Simulation results show that the proposed EA-PTS has better performance in simultaneously reducing the PAPR and computational complexity.