Advanced channel coding schemes for B5G/6G networks: State-of-the-art analysis, research challenges and future directions

Abhilasha Gautam, Prabhat Thakur, Ghanshyam Singh

Research output: Contribution to journalArticlepeer-review


As a consequence of continuing demand for additional capacity and higher quality data transmission, channel coding as a key component of a digital communication network has progressed from a classical single pair, point-to-point information theoretic application to a class of coding techniques with diverse parameters. The heterogeneous requirements of B5G/6G networks technology have made flexibility of code parameters the main desired feature while designing channel codes. Owing to this, channel coding techniques have evolved to support enabling applications that depend on different factors such as latency, reliability, energy efficiency and throughput. We review and discuss the application of new techniques, modifications in the design and construction of modern channel coding schemes, including block, convolutional, rateless and space-time codes. Further, the error correction performance of mainstream channel decoders for LDPC, polar and turbo codes is compared and analysed for their attainable error rate. The aim of this study is to highlight the adaptability of the discussed coding schemes for the forthcoming cellular network landscape. This article also addresses the implementational challenges of high throughput, low latency and machine type communication scenarios. Moreover, some recent studies exploring the role of machine learning for optimisation of B5G/6G networks are classified and discussed. Concluding, research areas pertaining to the scalability of error control coding for next generation networking are addressed.

Original languageEnglish
JournalInternational Journal of Communication Systems
Publication statusAccepted/In press - 2024


  • advanced channel codes
  • B5G/6G networks
  • challenges
  • channel coding
  • derived coding schemes
  • future direction
  • machine learning
  • performance analysis
  • variants

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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