WIRELESS SEMINAR SERIES
Date and time: Wednesday, Oct 4, 2:00 pm
Location: Carleton University, 4359 ME (Mackenzie Building). Campus map.
Title: MASSIVE MACHINE TYPE COMMUNICATION WITH DATA AGGREGATION AND RESOURCE SCHEDULING
Speaker: Dr. Jing Guo, Research Fellow, Research School of Engineering, Australian National University
ABSTRACT: To enable massive machine type communication (mMTC), data
aggregation is a promising approach to reduce the congestion caused by
a massive number of machine type devices (MTDs). In this talk, I
present my recent work on the data aggregation for mMTC. We
investigate a two-phase cellular-based mMTC network where MTDs
transmit to aggregators (i.e., aggregation phase) and the aggregated
data is then relayed to base stations (i.e., relaying phase). Due to
the limited resources, the aggregators not only aggregate data, but
also schedule resources among MTDs. Two scheduling schemes are
considered: random resource scheduling (RRS) and channel-aware
resource scheduling (CRS). By leveraging the stochastic geometry, we
present a tractable analytical framework to investigate the
signal-to-interference ratio (SIR) for each phase, thereby computing
the MTD success probability, the average number of successful MTDs and
probability of successful channel utilization, which are the key
metrics characterizing the overall mMTC performance. The results
suggest that the provision of more resources at the aggregation phase
is not always beneficial to the mMTC performance.
Speaker bio: Dr. Jing Guo is a research fellow at the Research School of
Engineering, Australian National University, Canberra, Australia. She
received her BSc (first class honours) in electronics and
telecommunications engineering from the Australian National
University, Australia and the Beijing Institute of Technology, China
in 2012, and PhD degree in telecommunications engineering from the
Australian National University in 2016. Her research interest lies in
the field of wireless communications, including machine-to-machine
communications and the application of stochastic geometry to wireless
networks.