Reconfigurable PAPR Reduction Technique For MIMO-OFDM Systems

Abstract

The Orthogonal Frequency Division Multiplexing of Multiple-Input Multiple-Output (MIMO-OFDM) is a potentially useful model in communication systems that are wireless for future generations.  MIMO-OFDM offers several benefits like robustness increased spectral efficiency and channel capacity etc. However, the peak power-related issues that arise in the OFDM system gives complicated working methodologies with the inclusion of multi stream spatial antenna. The well-known schemes for optimizing the PAPR are adoptive tone reservation (ATR), clipping, probabilistic mapping, and partial transmit sequences (PTS) are needed to be simplified and directed for least computational complexity overhead. In this work hybrid SLM-PTS, the technique is used which combines SLM and PTS to reduce the required computational complexity. Based on statistical PAPR threshold bound appropriate PAPR reduction technique is selected for optimization.  Here, the selection of schemes using the machine learning approach shows the performance of applying simplified approaches in a MIMO-OFDM system with reduced complexity trade off measure. Finally, this work also investigates the error rate performance of applying value bound directed PAPR approach that makes the switch over dynamically from one approach