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Reduced PAPR Model Predictive Control based FBMC/OQAM signal for NB-IoT paradigm

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Abstract

The competent class of fifth-generation mobile network used in NB-IoT demands for an extended and efficient massive device to a device communication system that exhibits narrow band with a focus on maximum spectrum resource usage, time and frequency synchronization and minimum out of band leakage. Filter bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) based systems act as a remarkable candidate for the fulfilment of application requirements of NarrowBand Internet of Things (NB-IoT) but suffer high peak to average power ratio (PAPR). Due to the overlapping of signals in the FBMC/OQAM structure, the basic SLM and PTS techniques cannot be applied on FBMC/OQAM systems. Therefore, in this work, a novel, cost-effective and low computation complexity solution SLM-MPC scheme is proposed which utilizes Model Predictive Control (MPC) algorithm for the optimization of PAPR of FBMC/OQAM transmitted signal and a significant reduction in PAPR of the FBMC/OQAM signal has been observed with negligible change in the BER of the system. The mathematical analysis is provided which justifies the simulation results and addresses the effectiveness of the proposed technique achieving a PAPR reduction of 1.61 dB and 1.2 dB for the number of sub-carriers as 128 and 256, respectively in comparison of SLM based FBMC/OQAM system.

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This Research Received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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The authors have developed a novel low computation complexity solution SLM-MPC scheme which utilizes Model Predictive Control (MPC) algorithm for the optimization of PAPR of FBMC/OQAM transmitted signal and a significant reduction in PAPR of the FBMC/OQAM signal has been observed with negligible change in the BER of the system.

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Correspondence to Achyut Shankar.

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Sharma, P., Shankar, A. & Cheng, X. Reduced PAPR Model Predictive Control based FBMC/OQAM signal for NB-IoT paradigm. Int. J. Mach. Learn. & Cyber. 12, 3309–3323 (2021). https://doi.org/10.1007/s13042-020-01263-8

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