An efficient Spotted Hyena Optimizer based Multi-user Detection for Polar Encoder

Biju George George Alexander, Ablin Rajathurai, Albert Raj Anthonymuthu

Abstract


Polar codes are among the most efficient types of error correction coding. Currently, these codes are employed in 5G communication networks and are the leading contender for 6G. Symmetry is significant in coding and decoding techniques for polar codes. However, some algorithms have high latency, and low throughput but suffer from high computational complexity. To overcome this issue a novel efficient Spotted Hyena Optimizer based Multi-User Detection for Polar Encoder (SHO-MUD) has been proposed for enhancing the throughput and reduce the latency. To increase spectrum, throughput, and energy efficiency, the SHO-MUD technique that has been suggested combines a polar encoder (PE) multiplexed with OFDMA with parity check polar coding (PCPC). PCPC-PE uses a system-configurable transmission rate to increase diversity gain and coding process dependability.  To achieve optimum resource use over several data blocks, users are scheduled using the Spotted Hyena Optimizer (SHO) approach in conjunction with the MPA.  The SHO scheduling efficiently allocates and schedules resources, resulting in a throughput gain of 0.6 bits per second. The suggested system provides user fairness by assuring an equal throughput of 1.55 bits/sec for all users.

Keywords


Polar Encoder; Multi-User Detection; Spotted Hyena Optimizer; parity check polar coding; Message Passing Algorithm

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References


J. Wang, and C. Ling, “Polar sampler: A novel Bernoulli sampler using polar codes with application to integer Gaussian sampling,” Designs, Codes Cryptogr. vol. 91, no. 5, pp. 1779-1811, 2023. http://dx.doi.org/10.1007/s10623-022-01164-7

K. B. Priya Iyer, S. Ramesh, J. Sathiamoorthy, & A. Ahilan, "Tetra Optimization Based Hybrid Parameters for OFDM Modulated Wireless Sensor Network", IETE J. Res., vol. 70, no. 5, pp. 4509–4522, 2023, https://doi.org/10.1080/03772063.2023.2225471

C.J. Pang, “Resource-constrained Coding for Communication and Computation Applications (Doctoral dissertation)”. 2023. http://dx.doi.org/10.1007/978-3-642-20520-0_6

M. Prabhu, B. Muthu Kumar & A. Ahilan, Slime Mould Algorithm based Fuzzy Linear CFO Estimation in Wireless Sensor Networks. IETE J. Res., 1-11 (2023).https://doi.org/10.1080/03772063.2023.2194279

K. Niu, P. Zhang, J. Dai, Z. Si, and C. Dong, “A golden decade of polar codes: From basic principle to 5G applications,” China Commun. vol. 20, no. 2, pp. 94-121, 2023. http://dx.doi.org/10.23919/jcc.2023.02.015

Y. Deng, S. Wu, J. You, J. Jiao, N. Zhang, and Q. Zhang, “Optimizing age of information in Polar coded status update system,” IEEE Internet Things J. 2023. http://dx.doi.org/10.1109/jiot.2023.3290644

H. Hematkhah, Y. Kavian, and E. Namjoo, “PoCH: automatic HDL code generator tool for Polar channel coding decoders in multimedia communication systems,” Multimedia Tools Appl. vol. 82, no. 24, pp. 36739-36768, 2023. http://dx.doi.org/10.1007/s11042-023-14507-w

M. Fletcher, E. Paulz, D. Ridge, and A.J. Michaels, “Low-Latency Wireless Network Extension for Industrial Internet of Things,” Sens. vol. 24, no. 7, pp. 2113, 2024. http://dx.doi.org/10.3390/s24072113

A. Alashqar, J. Alkasassbeh, R. Mesleh, and A. Al-Qaisi, “SDR implementation and real-time performance evaluation of 5G channel coding techniques,” AEU Int. J. Electron. Commun. vol. 170, pp. 154852, 2023. http://dx.doi.org/10.1016/j.aeue.2023.154852

K. Pelekanakis, P. Paglierani, A. Alvarez, and J. Alves, “Comparison of error correction codes via optimal channel replay of high north underwater acoustic channels,” Comput. Network. 110270, 2024. http://dx.doi.org/10.1016/j.comnet.2024.110270

D. Yang, Y. Mao, and X. Liu, “Judgement of error frames using frozen bits and its applications in decoding of polar codes,” IET Commun. vol. 17, no. 14, pp. 1760-1772, 2023. http://dx.doi.org/10.1049/cmu2.12651

T. Jiang, Y. Liu, L. Xiao, W. Liu, and G. Liu, “PCC polar codes for future wireless communications: Potential applications and design guidelines,” IEEE Wireless Commun. 2023.http://dx.doi.org/10.1109/mwc.017.2200586

S. Lydia, Y.K. Gultom, S. Alam, I. Surjati, A. Alkina, A. Mekhtiyev, Y. Neshina, T. Serikov, P. Madi, K. Sansyzbay, and A. Yurchenko, “Polar Code Performance Analysis for High-Speed Wireless Data Communication System,” Journal of Theoretical and Applied Information Technology, vol. 100, no. 05, 2022.http://dx.doi.org/10.21883/tpl.2022.08.55116.19200

M. Maksimovi, and M. Forcan, “Application of 5G channel coding techniques in smart grid: LDPC vs. polar coding for command messaging,” In 7th International Conference on Electronics, Telecommunications, Computing, Automatics and Nuclear Engineering-IcETRAN, pp. 8-10, 2020. http://dx.doi.org/10.1016/j.segan.2021.100495

N.A. Mohammed, A.M. Mansoor, R.B. Ahmad, and S.R.B. Azzuhri, “Deployment of polar codes for mission-critical machine-type communication over wireless networks,” arXiv preprint arXiv:2110.02938. 2021.http://dx.doi.org/10.32604/cmc.2022.020462

F.G. Krasser, M.C. Liberatori, L. Coppolillo, L. Arnone, and J.C. Moreira, “Fast and efficient FPGA implementation of Polar Codes and SoC test bench,” Microprocess, Microsyst. vol. 84, pp. 104264, 2021. http://dx.doi.org/10.1016/j.micpro.2021.104264

Y. Zhai, J. Li, H. Feng, and F. Hong, “Application research of polar coded OFDM underwater acoustic communications,” EURASIP J. Wireless Commun. Networking, vol. 2023, no. 1, pp. 26, 2023. http://dx.doi.org/10.1186/s13638-023-02236-5

C. Pillet, I. Sagitov, V. Bioglio, and P. “Giard Shortened Polar Codes under Automorphism Ensemble Decoding,” IEEE Commun. Lett. 2024. http://dx.doi.org/10.1109/lcomm.2024.3365091

A. Shreshtha, and S. R. Sarangi, “Efficiently Using Polar Codes in 5G Base Stations to Enhance Rural Connectivity.” arXiv preprint arXiv:2306.15476. 2023. http://dx.doi.org/10.1063/pt.5.028530

A. Sharma, and M. Salim, “Polar code appropriateness for ultra-reliable and low-latency use cases of 5G systems,” Int. J. Networked Distrib. Comput. vol. 7, no. 3, pp. 93-99, 2019. http://dx.doi.org/10.2991/ijndc.k.190702.005

Y. Liao, S.A. Hashemi, J.M. Cioffi, and A. Goldsmith, “Construction of polar codes with reinforcement learning,” IEEE Trans. Commun. vol. 70, no. 1, pp. 185-198, 2021. http://dx.doi.org/10.1109/tcomm.2021.3120274

W. Xu, X. Ta, Y. Be’ery, Y.L. Ueng, Y. Huang, X. You, and C. Zhang, “Deep learning-aided belief propagation decoder for polar codes,” IEEE J. Emerging Sel. Top. Circuits Syst. vol. 10, no. 2, pp. 189-203, 2020. http://dx.doi.org/10.1109/jetcas.2020.2995962

S.M. Tseng, W.C. Hsu, and F. Tseng, “Deep learning-based decoding for polar codes in Markov Gaussian memory impulse noise channels,” Wireless Pers. Commun. vol. 122, no. 1, 737-753, 2022. http://dx.doi.org/10.1007/s11277-021-08923-0

C. Wen, J. Xiong, L. Gui, Z. Shi, and Y. Wang, “A novel decoding scheme for polar code using convolutional neural network,” In 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp. 1-5, 2019. IEEE. http://dx.doi.org/10.1109/bmsb47279.2019.8971888

S. A. Hebbar, V.V. Nadkarni, A.V. Makkuva, S. Bhat, S. Oh, and P. Viswanath, “CRISP: Curriculum based Sequential neural decoders for Polar code family,” In International Conference on Machine Learning, pp. 12823-12845, 2023. PMLR. http://dx.doi.org/10.1109/isit50566.2022.9834589




DOI: https://doi.org/10.33180/InfMIDEM2025.206

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