Peak-to-Average Power Ratio Reduction Using Active Constellation Extension-Projection Onto Convex Sets Combined with Particle Swarm Optimization Added to Space Time Bloc Coding in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing System
- 1 Cadi Ayyad University, Morocco
Copyright: © 2020 Ahmed Ouqour, Abdelhamid Louliej, Younes Jabrane, Brahim Ait Es Said and Abdellah Ait Ouahman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
When generated Orthogonal Frequency Division Multiplexing (OFDM) signal is transmitted through a number of antennas in order to achieve diversity or cap any gain (higher transmission rate) then it is known as Multiple Input Multiple Output (MIMO)-OFDM. The full form of MIMO is multi input and multi output where signals are transmitted via multiple antennas instead of only one antenna like FDM. This technique has the potential of dramatic increase of data transmission in wireless environment. However, the transmitted signal still has high Peak-to-Average Power Ratio (PAPR) because of OFDM characteristics. The proposal algorithm in this study describes a new technique using Active Constellation Extension-Projection Onto Convex Sets (ACE-POCS) combined with the Particle Swarm Optimization (PSO) and Space Time Bloc Coding (STBC) for PAPR reduction. Apply the ACE-POCS technique and independently on each transmitted antenna, was effective to reduce PAPR, but converges slowly. Therefore, the Particle Swarm Optimization (PSO) was introduced to fight this slowness, by searching and well reducing the PAPR. Thanks of the PSO-ACE-POCS algorithm; the transmitted sequence was selected with best PAPR reduction over all transmission antennas. The results of simulation and BER performance show that the PSO-ACE-POCS method added to STBC minimizes the PAPR with a fast convergence and low computational complexity in comparison with the ACE-POCS method applied to STBC.
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- Peak to Average Power Ratio (PAPR)
- Single Input Single Output (SISO)
- Particle Swarm Optimization (PSO)
- Binary Error Rate (BER)
- High Power Amplifier (HPA)