PAPR Reduction Scheme In MIMO-OFDM Systems With Efficient Embedded Signaling

TL;DR

This paper introduces two innovative blind techniques for reducing PAPR in MIMO-OFDM systems by implicitly embedding side information, enhancing performance without increasing system complexity or reducing data rate.

Contribution

The paper proposes two novel blind PAPR reduction methods, BSTBC and BSLM-STBC, that embed side information implicitly, avoiding explicit signaling and improving system performance.

Findings

Significant PAPR reduction achieved with the proposed methods

Perfect signal recovery demonstrated at the receiver

Enhanced system performance without additional data rate loss

Abstract

Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) is a promising transmission scheme for high performance broadband wireless communications. However, this technique suffers from a major drawback which is the high Peak to Average Power Ratio (PAPR) of the output signals. In order to overcome this issue, several methods that require the transmission of explicit Side Information (SI) bits have been proposed. In fact, the transmitted bits must be channel-encoded as they are particularly critical to the performance of the considered OFDM system. This channel-encoding highly increases the system complexity and also decreases the transmission data rate. For these reasons, we propose in this paper, two robust blind techniques that embed the (SI) implicitly into the OFDM frame. First, we investigate a new technique referred as Blind Space Time Bloc Codes (BSTBC) that is inspired from the conventional Selected Mapping (SLM) approach. This technique banks on an adequate embedded signaling that mainly consist on a specific Space Time Bloc Codes (STBC) patterns and a precoding sequences codebook. Second, in order to improve the signal detection process and the PAPR gain, we propose a new efficient combined Blind SLM-STBC (BSLM-STBC) method. Both methods have the benefit of resulting in an optimized scheme during the signal estimation process that is based on the Max-Log-Maximum A Posteriori (MAP) algorithm. Finally, the obtained performance evaluation results show that our proposed methods result in a spectacular PAPR reduction and furthermore lead to a perfect signal recovery at the receiver side.