Work

Iteration Overlap and Dual-Sided LSOVA for Efficient Parallel Turbo Decoding Architectures

Year: 2025

Type: article

Abstract: An efficient turbo decoder architecture is proposed by jointly exploring advancements in code design, decoding algorithms, and hardware implementation to achieve high-throughput and low-latency performance. At the algorithmic level, we introduce Dual-Sided Local-SOVA (DS-LSOVA), an enhanced variant of Local-SOVA that significantly lowers the complexity of extrinsic information computation while preserving decoding accuracy. To further improve implementation efficiency, iteration overlap interleavers are developed specifically for fully pipelined, iteration-unrolled UXMAP decoders, enabling full iteration overlap. This design reduces decoding latency and boosts hardware area efficiency by up to 22%. Importantly, these gains are achieved without sacrificing error-correcting performance: DS-LSOVA matches the Frame Error Rate (FER) of conventional max-Log-MAP decoding, while the proposed interleavers deliver FER results comparable to the Quadratic Permutation Polynomial (QPP) interleavers used in LTE.

Source: IEEE Access

DOI: 10.1109/access.2025.3590986

Authors: Stefan Weithoffer, Charbel Abdel Nour

Institutions: Centre National de la Recherche Scientifique, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, IMT Atlantique

Language: English

Topic: Error Correcting Code Techniques

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Agence Nationale de la Recherche