Modified SLM and PTS approach to reduce PAPR in MIMO OFDM

In current communication systems growing demand of multimedia services and the growth of Internet related contents lead to increasing interest to high speed communications. Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is one of the most attractive candidates for fourth generation (4G) mobile radio communication. It effectively combats the multipath fading channel and improves the bandwidth efficiency. However, the main drawback of MIMO-OFDM system is high peak-to-average power ratio (PAPR) for large number of sub-carriers, which result in many restrictions for practical applications. Recently, space time block codes (STBC) have gained much attention as an effective transmit diversity technique to provide reliable transmission with high peak data rates to increase the capacity of wireless communication systems. In this paper, the performance of BER vs. SNR in normal STBC OFDM is compared to that of STBC-OFDM systems with versions of SLM using simulations. Selective Mapping (SLM) is used as the existing system. In this technique, the minimum PAPR signal is selected and transmitted by generating different representations of OFDM symbols using different phase sequences. A new concurrent PAPR reduction algorithm based on the property of orthogonal space-time block coded (STBC) is proposed in this paper. We proved that the conjugate symbols transmitted on two antennas have same PAPR property, with which, the computational complexity cost of the proposed algorithm can be reduced significantly compared with the conventional concurrent PAPR reduction algorithms, such as concurrent partial transmit sequences (PTS). Furthermore, a criterion of minimum maximum (minmax) is proposed, which shows better PAPR performance than the criterion of minimum average (minaverage) in conventional concurrent algorithms. Simulation results demonstrate that the performance of the proposed algorithm outperforms the conventional concurrent algorithms.