EARS: Machine Learning and Social Protocols for Enhancing Spectrum Access for Wireless Communications

Janne Lindqvist received an NSF award for $300,000.00 from the NSF Enhancing Access to the Radio Spectrum (EARS) program. Richard Howard of WINLAB is senior personnel on the grant.

Janne would like to acknowledge the help of Shridatt “James” Sugrim for helping with parts of the proposal.

The abstract is given below.

Title: EARS: Machine Learning and Social Protocols for Enhancing Spectrum Access for Wireless Communications

PI: Janne Lindqvist. Senior Personnel: Richard Howard

This project designs, implements and studies novel protocols for enhancing access to radio spectrum by taking advantage of the fundamentals of human behavior.It produces technologies, theories, and guidelines for protocols that are expected to significantly improve the efficiency of spectrum access. This may lead to substantial societal impact by allowing more work to be done with the same resources. Success will also benefit the environment by limiting the infrastructure needed for the required data traffic; both energy and infrastructure investment can be minimized. Further, the project creates a model of human behavior in a specific technical context that can serve as a basis for similar projects in other technology areas involving resource optimization. This interdisciplinary project applies and develops expertise from areas of social computing, machine learning, wireless technology, security engineering, physical analogs, mobile systems, and user-centered design. The project implements and deploys a system that enables efficient bandwidth sharing with machine learning and social protocols that goes beyond what is possible with technology alone. Social protocols are cooperative yet discretionary methods that allow to users distribute access more fairly using inherently natural decision-making processes as opposed to externally imposed ones. The project consists of three major activities: (1) study the main approaches to social protocols, including persuasive computing, clinical behavior change theories, and micro-tasks; (2) develop a system that observes users trying to access the network and facilitates control rules that allow maximum value to users in the most transparent way; and (3) deploy these social protocols and the system in live networks to evaluate the approaches in real-life settings.