Channel estimation and PAPR reduction in OFDM based on dual layers-superimposed training

Abstract

Superimposed Training (ST) is one of the most appealing channel estimation techniques for Orthogonal Frequency Division Multiplexing (OFDM), to be possibly exploited in 6G. The data and pilot symbols are sharing the same time and frequency resources, and hence, the overhead is significantly reduced. Moreover, the superimposed pilots can be also used for the reduction of the Peak-to-Average Power Ratio (PAPR). However, a joint channel estimation and PAPR reduction procedure has not been addressed yet. In this work, a novel scheme denoted as Dual Layers-Superimposed Training (DL-ST) is proposed for this joint purpose. The Training Sequence (TS) of the first layer is targeted to perform channel estimation, while the TS of a second layer is designed for PAPR reduction and it is made transparent to the first one. Both layers can be independently processed, which implies a reduced complexity. To verify the performance of the proposed technique, the analytical expression of the channel estimation Mean Squared Error (MSE) is derived. Finally, several numerical results further illustrate the performance of the proposal, showing how the MSE and achievable rate are improved while significant PAPR reductions are attained with negligible complexity.