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Optimized intelligent framework for peak-to-average power ratio reduction in filter bank multicarrier

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Abstract

Nowadays, the wireless multiple-input multiple-output (MIMO) system performance was advanced with filter bank multicarrier (FBMC) approach. However, peak-average-power ratio (PAPR) reduction is one of the challenging issues in FBMC. Thus, a novel hybrid chimp-based deep belief framework (CbDBF) was developed in this study to reduce PAPR in wireless communication systems. Initially, a MIMO-FBMC system was modelled with the required antennas. Then, the PAPR is predicted for the transmitted signal using the deep neural function. Further, the Chimp fitness function is incorporated to minimize the PAPR. The Chimp function tunes the PAPR without losing the information. Thus, the throughput is enhanced in the developed model, which is 124.4 Mbps. Then, the communication parameters are determined and verified with the current techniques in a comparative analysis. Furthermore, the comparative analysis shows that the developed model attained better outcomes than the existing techniques. Hence, the proposed model has optimized the PAPR up to 0.37 dB, and the BER was optimized by 3.07 × 10–5.

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References

  1. Zohdi M, Rafiee M, Kayvanfar V, Salamiraad A (2022) Demand forecasting based machine learning algorithms on customer information: an applied approach. Int J Inf Technol 14(4):1937–1947. https://doi.org/10.1007/s41870-022-00875-3

    Article  Google Scholar 

  2. Rajarajeswarie B, Sandanalakshmi R (2022) Machine learning based hybrid precoder with user scheduling technique for maximizing sum rate in downlink MU-MIMO system. Int J Inf Technol 14(5):2399–2405. https://doi.org/10.1007/s41870-022-00902-3

    Article  Google Scholar 

  3. Londhe GD, Hendre VS (2023) Interference reduction in hybrid beamforming using 2-D overlapped partially connected subarray structure. Int J Inf Technol 15(3):1407–1415. https://doi.org/10.1007/s41870-023-01168-z

    Article  Google Scholar 

  4. Mukherjee M, Noronha K (2022) Experimental analysis of received power for OOK-NRZ visible light communication system using off-the-shelf components. Int J Inf Technol 14(6):2839–2853. https://doi.org/10.1007/s41870-022-01079-5

    Article  Google Scholar 

  5. Kumar A, Singh S, Rawal V, Garg S, Agrawal A, Yadav S (2022) CNN-based device-free health monitoring and prediction system using WiFi signals. Int J Inf Technol. https://doi.org/10.1007/s41870-022-01023-7

    Article  Google Scholar 

  6. Aghdam MH, Sharifi AA (2019) PAPR reduction in OFDM systems: an efficient PTS approach based on particle swarm optimization. ICT Express 5(3):178–181. https://doi.org/10.1016/j.icte.2018.10.003

    Article  Google Scholar 

  7. Wang B, Si Q, Jin M (2020) A novel tone reservation scheme based on deep learning for PAPR reduction in OFDM systems. IEEE Commun Lett 24(6):1271–1274. https://doi.org/10.1109/LCOMM.2020.2980832

    Article  Google Scholar 

  8. Prasad S, Jayabalan R (2020) PAPR reduction in OFDM systems using modified SLM with different phase sequences. Wirel Pers Commun 110:913–929. https://doi.org/10.1007/s11277-019-06763-7

    Article  Google Scholar 

  9. Liu X, Zhang X, Xiong J, Gu F, Wei J (2019) An enhanced iterative clipping and filtering method using time-domain kernel matrix for PAPR reduction in OFDM systems. IEEE Access 7:59466–59476. https://doi.org/10.1109/ACCESS.2019.2915354

    Article  Google Scholar 

  10. Azarnia G, Sharifi AA, Emami H (2020) Compressive sensing based PAPR reduction in OFDM systems: modified orthogonal matching pursuit approach. ICT Express 6(4):368–371. https://doi.org/10.1016/j.icte.2020.07.004

    Article  Google Scholar 

  11. Xing Z, Liu K, Liu Y (2020) Low-complexity companding function design for PAPR reduction in OFDM systems. IET Commun 14(10):1581–1587. https://doi.org/10.1049/iet-com.2019.0812

    Article  Google Scholar 

  12. Liu K, Liu Y (2021) Adjustable nonlinear companding transform based on scaling of probability density function for PAPR Reduction in OFDM systems. IEEE Trans Broadcast 67(2):524–537. https://doi.org/10.1109/TBC.2021.3051520

    Article  Google Scholar 

  13. Zhang SY, Shahrrava B (2018) A selected mapping technique using interleavers for PAPR reduction in OFDM systems. Wirel Pers Commun 99:329–338. https://doi.org/10.1007/s11277-017-5101-7

    Article  Google Scholar 

  14. Liang HY, Jiang HY (2019) The modified artificial bee colony-based SLM scheme for PAPR reduction in OFDM systems. In: 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp 504–508. https://doi.org/10.1109/ICAIIC.2019.8669020

  15. Satyavathi K, Rama Rao B (2019) Modified Phase Sequence in Hybrid Pts Scheme for PAPR Reduction in OFDM Systems. In: Saini H, Singh R, Patel V, Santhi K, Ranganayakulu S (eds) Innovations in electronics and communication engineering. Lecture notes in networks and systems, vol 33. Springer, Singapore

    Google Scholar 

  16. Goel A, Poddar PG, Agrawal M (2019) A novel quadrilateral companding transform for PAPR reduction in OFDM systems. Digit Signal Process 85(113–123):1051–2004. https://doi.org/10.1016/j.dsp.2018.11.002

    Article  Google Scholar 

  17. Jawhar YA, Audah L, Taher MA, Ramli KN, Shah NSM, Musa M, Ahmed MS (2019) A review of partial transmit sequence for PAPR reduction in the OFDM systems. IEEE Access 7:18021–18041. https://doi.org/10.1109/ACCESS.2019.2894527

    Article  Google Scholar 

  18. Cui X, Liu K, Liu Y (2021) Novel linear companding transform design based on linear curve fitting for PAPR reduction in OFDM systems. IEEE Commun Lett 25(11):3604–3608. https://doi.org/10.1109/LCOMM.2021.3107410

    Article  Google Scholar 

  19. Arbi T, Nasr I, Geller B (2020) Near Capacity RCQD Constellations for PAPR Reduction of OFDM Systems. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 5110–5114. https://doi.org/10.1109/ICASSP40776.2020.9053923

  20. Abdalla HF, Hassan ES, Dessouky MI, Elsafrawey AS (2021) Three-layer PAPR reduction technique for FBMC based VLC systems. IEEE Access 9:102908–110291. https://doi.org/10.1109/ACCESS.2021.3098776

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Ramavath S, Samal UC (2021) Theoretical analysis of PAPR companding techniques for FBMC systems. Wirel Pers Commun 118:2965–2981. https://doi.org/10.1007/s11277-021-08164-1

    Article  Google Scholar 

  23. Sidiq S, Sheikh JA, Mustafa F, Malik BA, Sofi IB (2021) PAPR Minimization of FBMC/OQAM scheme by hybrid SLM and PTS using artificial: bee-colony phase—optimization. Arab J Sci Eng 46:9925–9934. https://doi.org/10.1007/s13369-021-05625-4

    Article  Google Scholar 

  24. Hesham H, Ismail T (2022) Hybrid NOMA-based ACO-FBMC/OQAM for next-generation indoor optical wireless communications using LiFi technology. Opt Quantum Electron 54:201. https://doi.org/10.1007/s11082-022-03559-1

    Article  Google Scholar 

  25. Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338. https://doi.org/10.1016/j.eswa.2020.113338

    Article  Google Scholar 

  26. Tian J, Liu Y, Zheng W, Yin L (2022) Smog prediction based on the deep belief - BP neural network model (DBN-BP). Urban Clim 41:101078. https://doi.org/10.1016/j.uclim.2021.101078

    Article  Google Scholar 

  27. Aboul-Dahab MA, Fouad MM, Roshdy RA (2019) Generalized discrete Fourier transform for FBMC peak to average power ratio reduction. IEEE Access 7:81730–81740. https://doi.org/10.1109/ACCESS.2019.2921447

    Article  Google Scholar 

  28. Lavanya P, Satyanarayana P, Mohatram M (2021) RETRACTED ARTICLE: peak to average power ratio reduction of ZT DFT-s-OFDM signals using improved monarch butterfly optimization-PTS scheme. J Ambient Intell Humaniz Comput 12:5005–5015. https://doi.org/10.1007/s12652-020-01940-0

    Article  Google Scholar 

  29. Gao S, Zheng J (2020) Peak-to-average power ratio reduction in pilot-embedded OTFS modulation through iterative clipping and filtering. IEEE Commun Lett 24(9):2055–2059. https://doi.org/10.1109/LCOMM.2020.2993036

    Article  Google Scholar 

  30. Sudharshini Kataksham V, Siddaiah P (2022) PAPR reduction for FBMC systems with ant bee colony optimization. Int J Adv Trends Comput Sci Eng 10(1):221–227

  31. Sobehy A, Renault E, Mühlethaler P (2020) CSI-MIMO: K-nearest neighbor applied to indoor localization. In: ICC 2020–2020 IEEE International Conference on Communications (ICC). IEEE, pp 1–6. DOI: https://doi.org/10.1109/ICC40277.2020.9149443

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  1. Department of Electronics and Communications Engineering, ANU College of Engineering and Technology, Guntur, Andhra Pradesh, 522510, India

    V. Sudarshani Kataksham & P. Siddaiah

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  1. V. Sudarshani Kataksham
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Correspondence to V. Sudarshani Kataksham.

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Kataksham, V.S., Siddaiah, P. Optimized intelligent framework for peak-to-average power ratio reduction in filter bank multicarrier. Int. j. inf. tecnol. 15, 2917–2927 (2023). https://doi.org/10.1007/s41870-023-01366-9

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