Demand for wireless data services will continue to rise rapidly in the foreseeable future. The goal of this project is to develop new advanced solutions for wireless access networks with the objectives of increasing network throughput and maximizing overall network utilities. Long-serving model-based solutions are facing severe limitations due to delays in tracking the ever-changing radio and network environment as well as measurement inaccuracies. The main novelty of this project is to bring new tools based on artificial neural networks (ANN) to meet those challenges. In particular, this project will investigate when, how, and why ANN-based learning techniques can be applied to a wide range of wireless networking problems with realistic constraints. This project will pursue transformative solutions that aim to benefit academia and industry alike.
Specifically, this project will marry supervised and unsupervised learning techniques with time-tested models of physical resources, channels, traffic, and network utilities. An important task is to exploit commonalities of ANN-based solutions for a number of subproblems to develop a set of principled, holistic solutions for the overall wireless networking problem, seeking solutions that are scalable, computationally efficient, and highly adaptive. Pertinent learnability and complexity theories backing the solutions will also be developed, in order to offer generalizable design principles. The ANN-based solutions developed are expected to be a major building block of next generation wireless access networks with associated economic benefits.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
