Research Projects

Recent research investigations have identified Age of Information (AoI) as a suitable metric to quantify the freshness of a data packet in a communication network. Conceptually, AoI at a monitoring terminal is defined as the time elapsed from the generation time of last successfully received packet. Thus, networks preforming on the AoI metric aim to ensure that the packets are delivered as timely as possible. Applications for timely delivery of packets include, cyber physical systems, federated learning, active learning, distributed processing etc. Notice that due to the uncertainties involved in the generation of packets and the practical dynamics of a network in delivering the packets, the AoI over time can be modelled as a random process. The statistical characterization of the AoI process aides in developing new tools for the analysis, specifically computation of moments and bounds on the metric helps to evaluate and improve the performance of the serving network. However, compared to the traditional metrics such as, latency and throughput, statistical characterization of AoI requires complex analysis and strict time synchronization between the source and the monitoring terminal. To this end, existing research work on characterization of AoI are mostly limited to queueing network with assumption on specific statistics of arrival process and service process. In fact, due to application specific uncertainties in generation of packets at the source and unreliability in the practical communication networks obtaining accurate statistics of arrival process and the service time are far from the reality. Moreover, with wireless networks emerging as primary communication infrastructure for delivery of sensory data, uncertainties involved in communication channel and the transmission over multi-access techniques such as Orthogonal Frequency Division Multiple Access (OFDMA), Non-orthogonal Multiple Access (NOMA), and Rate Split Multiple Access (RSMA), needs to be accounted in characterizing the AoI process. Thus, this project aims to develop a theoretical framework for statistical characterization of AoI in a multiple access wireless network. Specifically, the framework will focus on accounting various statistical characterizations of the wireless channel along with the specified scheduling policies in deriving the moments and moment generating function (MGF) of AoI process. Moreover, in order to have mathematical tractability, the investigation will take aide of tools developed in analysis of stochastic hybrid systems (SHS). Finally, deterministic upper and lower bounds will be also derived for the achievable AoI performance of the network. Hence the tools developed in the proposed framework will be fundamental in evaluating (i) the performance scheduling algorithms; (ii) impact of channel statistics; (iii) impact of throughput and latency; and, (iv) impact of allocated transmit power, on the AoI performance of a multi-access wireless network.