A low-complexity dual trellis decoding algorithm for high-rate convolutional codes

Vinh Hoang Son Le (1, 2) , Charbel Abdel Nour (1, 2) , Catherine Douillard (1, 2) , Emmanuel Boutillon (3, 4)

1 IMT Atlantique - ELEC - Département Electronique
2 Lab-STICC_IMTA_CACS_IAS
3 UBS - Université de Bretagne Sud
4 Lab-STICC_UBS_CACS_IAS

Decoding using the dual trellis is considered as a potential technique to increase the throughput of soft-input soft-output decoders for high coding rate convolutional codes. However, the dual Log-MAP algorithm suffers from a high decoding complexity. More specifically, the source of complexity comes from the soft-output unit, which has to handle a high number of extrinsic values in parallel. In this paper, we present a new low-complexity sub-optimal decoding algorithm using the dual trellis, namely the dual Max-Log-MAP algorithm, suited for high coding rate convolutional codes. A complexity analysis and simulation results are provided to compare the dual Max-Log-MAP and the dual Log-MAP algorithms. Despite a minor loss of about 0.2 dB in performance, the dual Max-Log-MAP algorithm significantly reduces the decoder complexity and makes it a first-choice algorithm for high-throughput high-rate decoding of convolutional and turbo codes.

Mots clés: convolutional codes; high coding rate; dual trellis; high-throughput decoder; low-complexity decoder; turbo codes

Domaines: Théorie de l'information [cs.IT]; Traitement du signal et de l'image [eess.SP]; Electronique; Réseaux et télécommunications [cs.NI]; Architectures Matérielles [cs.AR]

Vinh Hoang Son Le, Charbel Abdel Nour, Catherine Douillard, Emmanuel Boutillon. A low-complexity dual trellis decoding algorithm for high-rate convolutional codes. WCNC 2020 : IEEE Wireless Communications and Networking Conference, May 2020, Seoul, South Korea. ⟨10.1109/WCNC45663.2020.9120466⟩. ⟨hal-02502291⟩