Tejashri Kuber has been selected as the recipient of the IEEE Communications Society Phoenix ISS Award for academic year 2020-2021. The IEEE Communications Society Phoenix ISS Award was established to encourage engineering student to participate in professional activities. Awards are to be given to full-time or part-time students to cover expenses for students to attend the International Switching Symposium, or other IEEE Communications Society Conferences.
Congratulations Tejashri !
The title and abstract of Tejashri's paper follows:
Traffic Prediction by Augmenting Cellular Data with Non-Cellular Attributes
Abstract—Prediction of user traffic in cellular networks is one of the promising ways to improve resource utilization among base stations. In this study, we employ deep learning techniques, specifically a long-short-term memory module to forecast cellular traffic. We consider traffic from neighboring cells and other non- cellular traffic-related attributes such as weather, busy period data from open-source API as features to augment the cellular traffic data and improve prediction. Specifically, we augment cellular traffic data from the City of Milan and its surroundings and we perform two types of analyses: (i) a one-step prediction or a point-by-point forecast of traffic and (ii) a trend analysis which is the forecast of traffic over an extended period. We compare the results with existing statistical methods such as auto- regression integrated moving averages (ARIMA) and exponential smoothing and observe gains in the trend analysis by providing the augmented data, whereas the one-step prediction is not much impacted.