Block Orthogonal Sparse Superposition Codes

Jeonghun Park, Jinseok Choi, Wonjae Shin, Namyoon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper introduces block orthogonal sparse su-perposition (BOSS) codes for efficient short-packet commu-nications over Gaussian channels. Unlike conventional sparse superposition codes, an encoder of BOSS code uses multiple unitary matrices as a fat dictionary matrix and maps information bits such that multiple subgroups of codewords are orthogonal. Exploiting this orthogonal property per group, a two-stage maximum a posteriori (MAP) decoding algorithm is presented. The key idea of the two-stage MAP decoder is to successively estimate the non-zero alphabets corresponding to the orthogonal columns in a dictionary matrix and the index of a sub-dictionary matrix containing the columns. This decoding algorithm achieves a near-optimal decoding performance while requiring polynomial time complexity in blocklength. Via simulations, we show that the proposed encoding and decoding techniques achieve enhanced block-error-rate performances in the short blocklength regime compared to the state-of-the-art coded modulation methods.

Original languageEnglish
Title of host publication2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728181042
Publication statusPublished - 2021
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: 2021 Dec 72021 Dec 11

Publication series

Name2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings


Conference2021 IEEE Global Communications Conference, GLOBECOM 2021

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics


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