Enhancing wireless information freshness via physical-layer network coding and non-orthogonal multiple access

(Note: Dr. Tse-Tin Chan was the Principal Investigator of this project. After Dr. Chan moved from HSUHK to EdUHK on August 1, 2022, the Principal Investigator was transferred to Dr. Hai Liu to meet the funding eligibility. Currently, Dr. Chan is the Co-Investigator of this project.) This project is supported by the Faculty Development Scheme of the Research Grants Council of Hong Kong under Grant UGC/FDS14/E02/21 in the amount of HKD 1,143,229. The Internet of Things (IoT) is an emerging wireless communication and networking technology that can be utilized to connect billions of devices and establish a close connection between our physical world and computer networks. Many time-critical applications, such as autonomous vehicles and industrial control, require the support of ultra-reliable low-latency communications (URLLC) to convey fresh information updates. However, information freshness cannot be accurately quantified by traditional metrics such as throughput and delay. Therefore, the age of information (AoI) metric has recently received extensive attention from researchers. AoI is defined as the elapsed time since the most recently received packet was generated. Literature shows that replacing traditional performance metrics with AoI may lead to fundamental changes in the communication system designs. Most AoI research has focused on the upper layers of communication networks. Lower-layer solutions, such as multiple access schemes for the medium access control (MAC) layer and multi-user interference cancellation schemes for the physical (PHY) layer, have not been thoroughly studied for their impact on information freshness. Existing lower-layer designs cannot guarantee good information freshness when a large number of users access complicated and unreliable wireless channels. This problem seriously hinders the development of time-critical IoT applications. Moreover, information update packets in the IoT networks are usually very short. Shannon’s channel capacity formula in information theory assumes an infinite blocklength and is therefore not suitable for characterizing the performance of short-packet communications. The purpose of this project is to fill the above-mentioned research gaps. To begin with, we would like to develop a theoretical framework for AoI analyses in various error-prone short-packet wireless communication models. Based on the developed framework, we then design lower-layer algorithms to enhance information freshness by physical-layer network coding (PNC) and non-orthogonal multiple access (NOMA). PNC alleviates the multi-user interference problem by utilizing the network-coded packets decoded from superimposed signals. NOMA improves spectral efficiency by serving multiple users at the same time and frequency. Our preliminary simulations show that PNC and NOMA can significantly improve the AoI performance of many channel models. To the end, we would investigate the combination of PNC and NOMA to improve the AoI performance further. If this research achieves favorable outcomes, it will be a solid step in the theory and practice of enhancing information freshness in the next-generation IoT networks.

Funding Source: UGC - Other Specific Funds/Earmarked Grants^