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

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. This paper undertakes a review and analysis of advanced channel coding techniques with the aim of their scalability towards B5G/6G networks scenario. A comparative study is presented for channel coding candidates of futuristic networks. Concatenated and derived codes of convolutional, polar, LDPC, Rateless, and Space-time coding schemes are reviewed with their advantages and limitations. Also, the performance of mainstream decoders for advanced channel codes is analysed. Further, the role of Machine learning in optimisation of wireless networks is explored. Future challenges pertaining to the application of channel codes are addressed and open research areas for further enhancement in error correction techniques mentioned. image