Main Article Content

Abstract

This research presents an optimized architecture for Fifth Generation (5G) communication systems that includes a Mixture Encoder to support multiple combinations of Digital Signal Processing (DSP) operations required for 5G baseband processing. This allows flexible encoding with lower computational overhead, in contrast to traditional polar encoders that rely on fixed arithmetic structures and sizable lookup tables. The lookup table complexity is greatly reduced, resulting in lower memory consumption and faster access, and it also maps ranges to compact intervals. A Built-In Self-Test (BIST) module is integrated before the Mixture Encoder to ensure dependable data feeding and fault tolerance. Furthermore, a virtual channel method developed with Virtual RAM technology eliminates explicit channel processing by allowing direct memory access, bypassing redundant channel operations, and allowing conditional decoding termination before execution. This virtualized method enables early-stage error correction while increasing processing speed, reducing switching activity, and optimizing memory usage. At the receiver, a Successive Cancellation (SC) polar decoder is used to achieve low-latency, energy-efficient decoding. Removing unnecessary operations and enabling sequential recursive decoding reduces arithmetic complexity. According to the FPGA synthesis results, the combination of a mixture encoder, virtual memory access, and SC decoding results in lower power consumption, nanosecond-scale delay, improved decoding precision, and scalable hardware utilization, making the proposed architecture ideal for DSP-intensive communication systems and 5G networks.

Keywords

5G communication system BIST module Digital signal processing FPGA synthesis Virtual RAM technology

Article Details

How to Cite
Parthasarathy, T., & Krishnamoorthy, N. . (2026). Mixture encoder and virtual RAM-based polar decoder architecture for high-speed 5G communication systems. Future Technology, 5(3), 1–16. Retrieved from https://fupubco.com/futech/article/view/859
Bookmark and Share

References

  1. N. Islam and S. Shin, “Deep learning in physical layer: Review on data driven end-to-end communication systems and their enabling semantic applications,” IEEE Open Journal of the Communications Society, vol. 5, pp. 4207-4240, 2024. DOI: https://doi.org/10.1109/OJCOMS.2024.3425314.
  2. G. Fernandes, H. Fontes and R. Campos, “Semantic communications: the new paradigm behind beyond 5G technologies,” arXiv preprint arXiv:2406.00754, 2024. DOI: https://doi.org/10.48550/arXiv.2406.00754.
  3. L. Nguyen, Q. B. Phan and T. T. Nguyen, “Highly reliable and secure system with multi-layer parallel LDPC and Kyber for 5G communications,” IEEE Access, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3485875.
  4. B. Mejmaa, M. A. Marktani, I. Akharraz and A. Ahaitouf, “An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA,” Computers, vol. 13, no. 8, pp. 195, 2024. DOI: https://doi.org/10.3390/computers13080195.
  5. D. Y. Venkatesh, K. Mallikarjunaiah, M. Srikantaswamy and K. Huang, “Enhancing 5G LTE communications: A novel LDPC decoder for next-generation systems,” Inf. Dyn. Appl, vol. 3, no. 1, pp. 47-63, 2024. DOI: https://doi.org/10.56578/ida030104.
  6. A. Yuan, Z. Yang and Z. Sun, “Evolution of Satellite Communication Systems Toward 5G/6G for 2030 and Beyond,” Engineering, 2025. DOI: https://doi.org/10.1016/j.eng.2025.06.025.
  7. Y. Hui, “Application of improved P-BEM in time varying channel prediction in 5G high-speed mobile communication system,” Nonlinear Engineering, vol. 14, no. 1, pp. 20240085, 2025. DOI: https://doi.org/10.1515/nleng-2024-0085.
  8. L. Yuan, H. Chen, Q. Huang and J. Gong, “Resource allocation Of 5G mmWave communication under random interference,” Scientific Reports, vol. 15, no. 1, pp. 9120, 2025. DOI: https://doi.org/10.1038/s41598-025-93593-2.
  9. B. Rajangam, M. Alagarsamy, C. R. Radhakrishnan, T. A. Assegie, A. O. Salau, A. Quansah, N. M. Chowdhury and I. J. Chowdhury, “Security-based low-density parity check encoder for 5G communication,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 4, pp. 2707-2715, 2024. DOI: https://doi.org/10.11591/eei.v13i4.7019.
  10. S. Sabapathy, S. Maruthu and D. N. K. Jayakody, “Multi-User Sparse Vector Coding for eXtreme Ultra-Reliable Low-Latency Communication in Beyond 5G,” IEEE Access, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3551398.
  11. M. Hernandez and F. Pinero, “5G LDPC Linear Transformer for Channel Decoding,” arXiv preprint arXiv:2501.14102, 2025. DOI: https://doi.org/10.48550/arXiv.2501.14102.
  12. S. T. Hayle, H. Y. Hsu, C. P. Wang, H. H. Lu, J. M. Lu, W. W. Hsu, Y. C. Chung, Y. Y. Bai and K. Okram, “High-speed FSO-5G wireless communication system with enhanced loss compensation using high-power EDFA,” Scientific Reports, vol. 15, no. 1, pp. 379, 2025. DOI: https://doi.org/10.1038/s41598-024-84436-7.
  13. P. Vinayagam, R. Yuvaraaj, S. Sathiyanandham, and S .Kanagamalliga, “Parallel VLSI architectures for high-throughput image processing in medical imaging,” Procedia Computer Science, vol. 233, pp. 851-860, 2024. DOI: https://doi.org/10.1016/j.procs.2024.03.27.4.
  14. L. N. Wang, H. Wei, Y. Zheng, J. Dong and G. Zhong, “Deep Error-Correcting Output Codes,” Algorithms, vol. 16, no. 12, pp. 555, 2023. DOI: https://doi.org/10.3390/a16120555.
  15. S. Matsenko, O. Borysenko, S. Spolitis, A. Udalcovs, L. Gegere, A. Krotov, O. Ozolins and V. Bobrovs, “FPGA-Implemented Fractal Decoder with Forward Error Correction in Short-Reach Optical Interconnects,” Entropy, vol. 24, no. 1, pp. 122, 2022. DOI: https://doi.org/10.3390/e24010122.
  16. W. Bai, X. Zou, P. Li, J. Ye, Y. Yang, L. Yan, W. Pan and L. Yan, “Photonic millimeter-wave joint radar communication system using spectrum-spreading phase-coding,” IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 3, pp. 1552-1561, 2022. DOI: https://doi.org/10.1109/TMTT.2021.3138069.
  17. I. Pyatin, J. Boiko and O. Eromenko, “Algorithmization and Hardware Implementation of Polar Coding for 5G Telecommunications,” Transport and Telecommunication Journal, vol. 25, no. 3, pp. 300-310, 2024. DOI: 10.2478/ttj-2024-0022.
  18. M. Singh, J. Kříž, M. M. Kamruzzaman, V. Dhasarathan, A. Sharma and S. A. Abd El-Mottaleb, “Design of a high-speed OFDM-SAC-OCDMA-based FSO system using EDW codes for supporting 5G data services and smart city applications,” Frontiers in Physics, vol. 10, pp. 934848, 2022. DOI: https://doi.org/10.3389/fphy.2022.934848.
  19. K. W. Kwon, K. Kim, K. Doo, H. Chung and J. W. Lee, “Low-Complexity Architecture for High-Speed 50G-PON LDPC Decoder,” IEEE Access, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3540450.
  20. J. Wang, J. Yang, G. Zhang, X. Zeng and Y. Chen, “Efficient layered parallel architecture and application for large matrix ldpc decoder,” Electronics, vol. 12, no. 18, pp. 3784, 2023. DOI: https://doi.org/10.3390/electronics12183784.
  21. B. Mejmaa, I. Akharraz and A. Ahaitouf, “A field-programmable gate array-based quasi-cyclic low-density parity-check decoder with high throughput and excellent decoding performance for 5G new-radio standards,” Technologies, vol. 12, no. 11, pp. 215, 2024. DOI: https://doi.org/10.3390/technologies12110215.
  22. B. N. Tran-Thi, T. T. Nguyen-Ly and T. Hoang, “An FPGA design with high memory efficiency and decoding performance for 5G LDPC decoder,” Electronics, vol. 12, no. 17, pp. 3667, 2023. DOI: https://doi.org/10.3390/electronics12173667.
  23. Y. Li, Y. Li, N. Ye, T. Chen, Z. Wang and J. Zhang, “High throughput priority-based layered QC-LDPC decoder with double update queues for mitigating pipeline conflicts,” Sensors, vol. 22, no. 9, pp. 3508, 2022. DOI: https://doi.org/10.3390/s22093508.
  24. M. Andronic, and G. A. Constantinides, "PolyLUT: learning piecewise polynomials for ultra-low latency FPGA LUT-based inference," arXiv preprint arXiv:2309.02334, 2023. DOI: https://doi.org/10.48550/arXiv.2309.0233.4.
  25. A. K. Tiwary, S. Gajawada, J. Shah, and N. Rao, "LUTAccel: Look-up-Table Based Vector Systolic Accelerator on FPGAs,” In 2025 26th International Symposium on Quality Electronic Design (ISQED), pp. 1-7, 2025. DOI: https://doi.org/10.1109/ISQED65160.2025.11014348.
  26. I. Ramírez, F. J. Garcia-Espinosa, D. Concha, and L. A. Aranda, “LLNN: A Scalable LUT-Based Logic Neural Network Architecture for FPGAs,” IEEE Transactions on Circuits and Systems I: Regular Papers, 2025. DOI: https://doi.org/10.1109/TCSI.2025.3606054.
  27. M. A. Farooq, G. Di Guglielmo, A. Rajagopala, N. Tran, V. A. Chhabria, and A. Arora, “LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout,” arXiv preprint arXiv:2512.07808, 2025. DOI: https://doi.org/10.48550/arXiv.2512.07808.
  28. Y. Xie, Z. Li, D. Diaconu, S. Handagala, M. Leeser, and X. Lin, “LUTMUL: Exceed conventional FPGA roofline limit by LUT-based efficient multiplication for neural network inference,” In Proceedings of the 30th Asia and South Pacific Design Automation Conference, pp. 713-719, 2025. DOI: https://doi.org/10.1145/3658617.3697687.