Aligning Spectrum-User Objectives for Maximum Inelastic-Traffic Reward

We develop objective functions for large-scale distributed dynamic spectrum access (DSA) networks that, by means of any learning algorithm, enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or "rewards" to be more general). We show that the proposed functions are: (i) optimal by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributed by being implementable in a decentralized manner.