With various emerging latency-sensitive applications, e.g. V2V (vehicle-to-vehicle) automation, smart power systems (i.e. smart grids), Internet of things (IoT) and smart factory (industry 4.0) low-latency communication is becoming more and more important. For convenience, we call these schemes with high requirement on reliability and latency high-reliability low-latency (HRLL) communications. Common approaches to achieve reliability are forward error-control (FEC) coding. However, due to finite code length, it is rather challenging to achieve high reliability and low latency simultaneously, since powerful FEC, e.g. LDPC codes, Polar codes normally need large code length to achieve high reliability. Moreover, in many scenarios, high data rates are also needed. Thus, in this project, we will study key technologies which can boost reliability, latency and rate performance simultaneously. Network coding is one of the technologies, which show potentially in rate and reliability in the networks.
Project results will be quite valuable for future digitalization industries and society. For instance, in industrial critical control, the requirement on reliability may be a PER (packet error rates) of 10-6 or lower (may down to 10-9 for some applications) and meanwhile a latency of a few micro-seconds (10-6 seconds, may down to 10-9 seconds sometime) is required. To achieve these targets, we need to use all our available strategies, including the methods will be studied in the project.
Thus, the overall objectives of the action is to systematically investigate network coding for HRLL (high-reliability low-latency) communications. More specifically, we will find the fundamental limits of HRLL networks with network coding. We will optimize network coding schemes and propose efficient communication strategies to improve latency and reliability performance under practical constraints. Our results seek to develop HRLL communication technologies and network coding theorems, which will boost the future latency and reliability critical applications.
In addition to tradition coding optimization, such as distance or rank based measure, we also used data-driving approaches for optimization coding. For instance, in paper 10, we propose reinforced learning based code optimisation for wireless caching. In publication 11, we have studied the coding for mobile fog computing, which use batched codes (a special type of network coding) to reduce the impacts of straggler nodes. Similarly, in publication 15, we proposed MDS codes for large scale machine learning algorithm ADMM. In paper 13, we study how network coding can be used in the consensus of ADMM to improve the reliability (and also latency). In publication 12, we used analog network coding (NOMA) to improve the latency and reliability under security constraint. In paper 14, we analyse the fundamental limits by error exponents for finite blocklength region, which is the base of the HRLL communications.
