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

在今后几年中，人均数据使用量将呈指数级增长。例如，蜂窝行业的预测表明，全球人均信息使用量将从2014年的15gb增长到2019年的37gb左右。如果没有重大的技术进步来增加其容量，现有的电信基础设施将无法支持这种巨大的数据增长。现代纠错码的出现，如涡轮码、低密度奇偶校验码和极性码，代表了无线系统纠错性能的巨大飞跃，允许在噪声信道上以接近容量的数据速率进行可靠的信息通信。然而，这一方向的改进主要局限于点对点情况，并且最大化点对点信道设置的增益将不足以满足新兴通信系统的高吞吐量和低延迟要求，特别是5G蜂窝系统及以后的通信系统。该项目旨在通过将编码方案扩展到无线网络设置来解决这些问题，从而在传统的点对点情况下获得额外的吞吐量增益。这项提议的研究有望对许多采用可靠网络无线通信的关键应用产生重大的变革性影响，例如在医疗保健、环境监测、金融等领域。在这个项目中，提出了一个实用的低复杂度，低延迟，容量逼近稀疏图码的综合框架。这些代码能够利用网络增益进行纠错，从而显著减少传输的数据量。该项目旨在研究这些方案的基本极限，并研究接近这些极限的实用编码算法。提出的研究涉及几个基本主题，这些主题与基于稀疏图的编码和极性编码在新兴的未来通信系统中的应用有关，这些在以前的研究中没有出现：典型网络通信问题的嵌套编码的分析和设计；分析了不同编码和解码器设计参数对性能的影响；对代数和随机代码之间的实现复杂性与性能权衡的理论理解；迭代译码失效事件的分析研究并开发了一种新型的高速现场可编程门阵列硬件解码体系结构。

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.