Ensemble-based Dimensionality Reduction Model for Wireless Time-Series

The new wireless network technology will provide users with a higher communication quality. However, we will face two critical problems: the wireless traffic will increase considerably, and the wireless signals will contain noise. The Wifi signals are represented as time series, but processing and removing noise from such huge-volume, high-dimensional and complex data pose great challenges. Learning from time-series is an ambitious problem, and the curse of dimensionality makes machine learning algorithms incompetent.

Dimensionality Reduction Algorithms (DRAs) can effectively address the problems above. Still, the DRA application to time series has been limited due to data complexity. We will first examine the characteristics of the wireless data and investigate which DRAs are the most suited. Next, we will define strategies to combine DRAs, a challenging task but significant to improve the DRA accuracy. By conducting an empirical analysis, we will develop the ensemble DRA that best maximizes the network performance.

Faculty Supervisor:

Samira Sadaoui

Student:

Farzana Anowar

Partner:

Discipline:

Computer science

Sector:

University:

University of Regina

Program:

Accelerate