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Efficient reconfigurable parallel switching for low-density parity-check encoding and decoding
Divyashree, Yamadur Venkatesh, Komala, Mallikarjunaiah, Mallikarjunaswamy, Srikantaswamy

Efficient reconfigurable parallel switching for low-density parity-check encoding and decoding

Science & Mathematics Published 2025 778.3 KB Verification Pending

Overview & Synopsis

In the evolution of next-generation communication systems, the demand for higher data integrity and transmission efficiency has brought low-density parity-check (LDPC) codes into focus, particularly for their error-correcting prowess. Traditional LDPC encoding and decoding techniques, such as the belief propagation (BP), Min-Sum, and Sum-Product algorithms, are hampered by high computational complexity and latency. Our research introduces a groundbreaking approach: an efficient, reconfigurable high speed parallel switching operation for a complexity-optimized low-density parity-check encoding and decoding model (CoLDPC-EC). This method leverages advanced parallel processing and reconfigurable computing to drastically enhance operational speed and efficiency. It significantly outperforms conventional algorithms by optimizing key parameters like decoding throughput and power consumption, ensuring swift, energy-efficient error correction ideal for cutting-edge communication technologies. Our comparison with traditional methods underscores our solution's superior speed, flexibility, and efficiency, promising a leap forward in reliable, high speed data transmission for next-generation networks. As per the simulation analysis, the proposed system shows better performance compared to conventional methods by 10.35%, 3.56%, and 2.36% in terms of decoding throughput, power consumption, and energy efficiency error correction, respectively.

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Document Information

Document Title Efficient reconfigurable parallel switching for low-density parity-check encoding and decoding
Author / Organization Divyashree, Yamadur Venkatesh, Komala, Mallikarjunaiah, Mallikarjunaswamy, Srikantaswamy
Subject & Discipline Scientific Research & Data
Categories Science & Mathematics
Publication Year 2025
License / Rights cc-by-4.0
Resource Verification Listed PDF (Verification Pending)
Original Host Website Zenodo (zenodo.org)
File Format application/pdf (Portable Document Format)
Reported File Size 778.3 KB (797,029 bytes)
Language EN
Catalog ID DOC-124656
Original Source Link Visit Source Page on zenodo.org »