Collaborative Research: CCSS: Coding for 5G and Beyond: Limits and Efficient Algorithms

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During the next couple of years, an exponential increase of data use per capita will be experienced. For example, predictions by the cellular industry state that the global per-capita information usage will grow from 15 GB in 2014 to around 37 GB in 2019. Without significant technological advances to increase its capacity, the existing telecommunications infrastructure will be unable to support this vast data increase. The advent of modern error-correcting codes, such as turbo codes, low-density parity-check codes, and polar codes, has represented a quantum leap in error-correcting performance for wireless systems, allowing reliable communication of information over noisy channels at data rates close to capacity. However, improvements in this direction have been mostly limited to the point-to-point case, and maximizing gains for the point-to-point channel setup will not be sufficient to satisfy the high throughput and low delay requirements of emerging communication systems, in particular of 5G cellular systems and beyond. This project aims to tackle these issues by extending coding schemes to the wireless network setting, thereby yielding additional throughput gains beyond the classical point-to-point case. The proposed research promises to provide a significant transformative impact on many critical applications employing reliable networked wireless communication, for example in the fields of healthcare, environmental monitoring, finance, and so on.In this project, a comprehensive framework of practical low-complexity, low-latency, capacity-approaching sparse graph codes is proposed. These codes are able to leverage network gains for error correction, thereby significantly reducing the amount of transmitted data. The project aims to study the fundamental limits of these schemes as well as to investigate practical coding algorithms to approach these limits. The proposed research involves several fundamental themes related to the application of sparse graph-based and polar codes to emerging future communication systems which are not present in previous studies: analysis and design of nested codes for canonical network communication problems; analysis of how performance scales with various code and decoder design parameters; a theoretical understanding of the implementation complexity versus performance trade-offs between algebraic and random codes; an analytical investigation of iterative decoding failure events; and the development of a novel high-speed field-programmable gate array hardware decoding architecture.