Novel Adaptive Tone Reservation algorithm for PAPR reduction in massive Point-to-Point MIMO-OFDM systems

Abstract

MIMO-OFDM is an emerging wireless technology that also covers the broadband aspect of communication. This technology has gained immense fame for its ability to adapt high-rate data transmission and robustness against multipath fading and other channel impairments. However, a major issue with a multicarrier system is their high peak-to-average ratio (PAPR) and, hence, massive MIMO-OFDM systems also suffer from high PAPR. Moreover, for massive MIMO-OFDM, where a large number of antennas are involved, the PAPR issue becomes more challenging. Different solutions have been proposed for PAPR reduction in MIMO-OFDM with some of them been extended to massive MIMO-OFDM systems; however, the proposed techniques have certain limitations, i.e., they are very complex. Herein, we propose an adaptive Beam Reservation (BR) algorithm for PAPR reduction in P-to-P Massive MIMO-OFDM systems. Using Singular value decomposition (SVD) for a P-to-P MIMO-OFDM system, the channel matrix can be diagonalized into unitary matrices and a diagonal matrix containing singular values. It has been found out that the last singular values of a channel gain matrix are very weak such that these eigenchannels are ignored in the case of data transmission. In our proposal, the weakest eigen channel in our system is kept aside to offer redundancy for PAPR reduction. A spiky function is then generated on that weakest eigen channel, which is then used for PAPR reduction. Simulations results show that the proposed technique has promising gains in terms of PAPR reduction with a negligible increase in the mean power of the transmit signal and has a very low-capacity loss. Moreover, our proposed technique outperforms the conventional Tone Reservation algorithm in terms of PAPR reduction, mean power increase and capacity loss.