EARS: Efficient Spectrum Allocation Auctions in Secondary Markets with Dynamic Random Supply and Demand

Michigan State University, East Lansing MI

Investigators

Abstract

The increasing spectrum demand from wireless applications cannot be satisfied with existing fixed spectrum allocation techniques. To provide dynamic spectrum access, auction based spectrum trading has received considerable attention. In spectrum trading, licensed users rent their unused spectrum to unlicensed users in exchange for money. The availability of unused spectrum depends on the usage pattern of licensed users and varies over time, frequency, and spatial domain. Unlicensed users can bid for this spectrum according to their needs which again vary by time and location. Thus, the auction scenario has to deal with the dynamic user demand and dynamic spectrum supply. Existing research on spectrum trading consider either a static auction environment or at most one side of the dynamics of the auction environment. Therefore, for improving spectrum unitization, it is critical to consider an auction scenario where bidders with heterogeneous demand arrive dynamically and bid for dynamically available spectrum units. This project studies a dynamic auction framework and undertakes the development of auction rules and regulations designed to achieve an efficient spectrum allocation. The study consists of four major components. (1) Developing the dynamic auction framework with dynamic arrival of users with variable lifetime and dynamic spectrum supply. (2) Tackling the design challenges associated with the diversity in demand and supply in terms of units and duration and devising a truthful auction mechanism. (3) Designing a generic auction framework to fit the auction rules and regulations and define the communication method using a secure communication protocol. (4) Developing a testbed to implement, experiment and validate the auction framework. This research will enhance radio spectrum sharing and utilization, providing more spectrum opportunities for existing and new applications. This project provides interdisciplinary research opportunities for graduate students working on the proposed research. This project incorporates research results into courses and to recruit students from underrepresented backgrounds.