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A Novel Distributive Population-Based Differential Evolution Algorithm for SLM Scheme to Reduce PAPR in Massive MIMO-OFDM Systems

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Abstract

To achieve the ever rising demand for high data rates with high spectral efficiency, the energy efficient massive Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) will be essential and useful for future wireless communication systems as well as Industrial network applications to reduce the operational costs significantly. But the high Peak to Average Power Ratio (PAPR) in a massive MIMO-OFDM system is identified as a critical issue that causes the non-linear signal distortion and restricts the efficiency of the power amplifier. Among different PAPR reduction schemes, Selected Mapping (SLM) is a distortionless technique and highly attractive due to its simple implementation capability. However, the conventional SLM scheme is inefficient for the massive MIMO-OFDM antenna system for its enormous Side Information (SI) burden and large computational complexity to search the best set of phase factors. In this paper, a Distributive Population based Switching Differential Evolution strategy is incorporated in the SLM scheme which eventually enhances the searching ability. The algorithm is employed in different antenna grouping (homogeneous and heterogeneous) based massive MIMO systems. The antenna grouping concept in the massive MIMO system minimizes the SI burden. The proposed algorithm for SLM techniques outperforms the existing techniques in terms of PAPR, Bit Error Rate, SI burden, energy efficiency and computational complexity.

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References

  1. Tramarin F, Vitturi S. Strategies and services for energy efficiency in real-time ethernet networks. IEEE Trans Industr Inf. 2015;11(3):841–52.

    Article  Google Scholar 

  2. Yang L, Song K, Siu YM. Iterative clipping noise recovery of ofdm signals based on compressed sensing. IEEE Trans Broadcast. 2017;63(4):706–13.

    Article  Google Scholar 

  3. Kang C, Liu Y, Hu M, Zhang H. A low complexity papr reduction method based on fwft and pec for OFDM systems. IEEE Trans Broadcast. 2017;63(2):416–25.

    Article  Google Scholar 

  4. Cho Y, Kim K, Woo J, Lee K, No J, Shin D. Low-complexity pts schemes using dominant time-domain samples in OFDM systems. IEEE Trans Broadcast. 2017;63(2):440–5.

    Article  Google Scholar 

  5. Rakshit M, Bhattacharjee S, Sanyal J, Chakrabarti A. Hybrid turbo coding pts with enhanced switching algorithm employing de to carry out reduction in papr in aul-based MIMO-OFDM. Arab J Sci Eng. 2020;45(3):1821–39.

    Article  Google Scholar 

  6. Bielinski M, Sosa G, Wanuga K, Primerano R, Kam M, Dandekar KR. Bit-loaded papr reduction for high-data-rate through-metal control network applications. IEEE Trans Industr Electron. 2014;61(5):2362–9.

    Article  Google Scholar 

  7. Lee BM, Kim Y. Interference-aware papr reduction scheme to increase the energy efficiency of large-scale MIMO-OFDM systems. Energies. 2017;10(8):1184.

    Article  MathSciNet  Google Scholar 

  8. Naeiny MF, Marvasti F. Selected mapping algorithm for papr reduction of space-frequency coded ofdm systems without side information. IEEE Trans Veh Technol. 2011;60(3):1211–6.

    Article  Google Scholar 

  9. Li C, Wang S, Chan K. Low complexity transmitter architectures for sfbc MIMO-OFDM systems. IEEE Trans Commun. 2012;60(6):1712–8.

    Article  Google Scholar 

  10. Jiang T, Ni C, Guan L. A novel phase offset slm scheme for papr reduction in alamouti MIMO-OFDM systems without side information. IEEE Signal Process Lett. 2013;20(4):383–6.

    Article  Google Scholar 

  11. Fallahzadeh M, Ferdosizadeh M. Blind slm for papr reduction of alamouti dsfbc systems. IET Commun. 2017;11(3):451–7.

    Article  Google Scholar 

  12. Hu W, Huang W, Ciou Y, Li C. Reduction of papr without side information for sfbc mimo-ofdm systems. IEEE Trans Broadcast. 2018;65(2):316–25.

    Article  Google Scholar 

  13. Lee BM. Energy efficient selected mapping schemes based on antenna grouping for industrial massive MIMO-OFDM antenna systems. IEEE Trans Industr Inf. 2018;14(11):4804–14.

    Article  Google Scholar 

  14. Das S, Suganthan PN. Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput. 2011;15(1):4–31.

    Article  Google Scholar 

  15. Zhao S-Z, Suganthan P, Das S. Self-adaptive differential evolution with multi-trajectory search for large-scale optimization. Soft Comput. 2011;15:2175–85.

    Article  Google Scholar 

  16. Gong W, Cai Z, Ling CX, Li H. Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans Syst Man Cybernet Part B (Cybernetics). 2011;41(2):397–413.

    Article  Google Scholar 

  17. Islam SM, Das S, Ghosh S, Roy S, Suganthan PN. An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans Syst Man Cybernet Part B (Cybernetics). 2012;42(2):482–500.

    Article  Google Scholar 

  18. Ghosh S, Das S, Vasilakos AV, Suresh K. On convergence of differential evolution over a class of continuous functions with unique global optimum. IEEE Trans Syst Man Cybernet Part B (Cybernetics). 2012;42(1):107–24.

    Article  Google Scholar 

  19. Gong W, Cai Z. Differential evolution with ranking-based mutation operators. IEEE Trans Cybernet. 2013;43(6):2066–81.

    Article  Google Scholar 

  20. Ghosh A, Das S, Mullick SS, Mallipeddi R, Das AK. A switched parameter differential evolution with optional blending crossover for scalable numerical optimization. Appl Soft Comput. 2017;57:329–52.

    Article  Google Scholar 

  21. Das S, Ghosh A, Mullick SS. A switched parameter differential evolution for large scale global optimization—simpler may be better. In: Radek M, editor. Mendel 2015. Cham: Springer International Publishing; 2015. p. 103–25.

    Chapter  Google Scholar 

  22. Rakshit M, Bhattacharjee S, Sil S, Chakrabarti A. Modified switching de algorithm to facilitate reduction of papr in OFDM systems. Phys Commun. 2018;29:245–60.

    Article  Google Scholar 

  23. Cai Z, Gong W, Ling CX, Zhang H. A clustering-based differential evolution for global optimization. Appl Soft Comput. 2011;11(1):1363–79.

    Article  Google Scholar 

  24. Tasgetiren MF, Suganthan PN, Pan Q-K. An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem. Appl Math Comput. 2010;215(9):3356–68.

    Article  MathSciNet  Google Scholar 

  25. Halder U, Das S, Maity D. A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments. IEEE Trans Cybernet. 2013;43(3):881–97.

    Article  Google Scholar 

  26. Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H. Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci. 2016;329:329–45 Special issue on Discovery Science.

    Article  Google Scholar 

  27. Lee BM. Calibration for channel reciprocity in industrial massive MIMO antenna systems. IEEE Trans Industr Inf. 2018;14(1):221–30.

    Article  Google Scholar 

  28. Lee BM, Yang H. Massive MIMO for industrial internet of things in cyber-physical systems. IEEE Trans Industr Inf. 2018;14(6):2641–52.

    Article  Google Scholar 

  29. Bjrnson E, Larsson EG, Marzetta TL. Massive MIMO: ten myths and one critical question. IEEE Commun Mag. 2016;54(2):114–23.

    Article  Google Scholar 

  30. Yang H, Marzetta TL. Performance of conjugate and zero-forcing beamforming in large-scale antenna systems. IEEE J Sel Areas Commun. 2013;31(2):172–9.

    Article  Google Scholar 

  31. Yang H, Marzetta TL. Energy efficient design of massive MIMO: How many antennas? In: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring). 2015. pp. 1–5. https://doi.org/10.1109/VTCSpring.2015.7145809.

  32. Kageyama T, Muta O, Gacanin H. Enhanced peak cancellation with simplified in-band distortion compensation for massive MIMO-OFDM. IEEE Access. 2020;8:73420–31. https://doi.org/10.1109/ACCESS.2020.2986280.

    Article  Google Scholar 

  33. Yao M, Carrick M, Sohul MM, Marojevic V, Patterson CD, Reed JH. Semidefinite relaxation-based papr-aware precoding for massive MIMO-OFDM systems. IEEE Trans Veh Technol. 2019;68(3):2229–43.

    Article  Google Scholar 

  34. Zayani R, Shaiek H, Roviras D. Papr-aware massive MIMO-OFDM downlink. IEEE Access. 2019;7:25474–84.

    Article  Google Scholar 

  35. Wang Y, Chen W, Tellambura C. A papr reduction method based on artificial bee colony algorithm for ofdm signals. IEEE Trans Wireless Commun. 2010;9(10):2994–9.

    Article  Google Scholar 

  36. Taspinar N, Yildirim M. A novel parallel artificial bee colony algorithm and its papr reduction performance using slm scheme in ofdm and mimo-ofdm systems. IEEE Commun Lett. 2015;19(10):1830–3.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments which were of great help to improve the quality of this paper.

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Authors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India

    Mahua Rakshit & Amlan Chakrabarti

  2. Department of Electronics and Communication Engineering, Techno International New Town, Kolkata, India

    Subhankar Bhattacharjee

  3. Computational Science Division, Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Kolkata, 700064, India

    Gautam Garai

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  1. Mahua Rakshit
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  2. Subhankar Bhattacharjee
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  3. Gautam Garai
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  4. Amlan Chakrabarti
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Correspondence to Mahua Rakshit.

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Rakshit, M., Bhattacharjee, S., Garai, G. et al. A Novel Distributive Population-Based Differential Evolution Algorithm for SLM Scheme to Reduce PAPR in Massive MIMO-OFDM Systems. SN COMPUT. SCI. 1, 292 (2020). https://doi.org/10.1007/s42979-020-00309-6

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