Database-assisted Spectrum Access in Dynamic Networks: A Distributed Learning Solution

TL;DR

This paper presents a distributed learning approach for dynamic, database-assisted spectrum access in TV white spectrum networks, effectively handling the challenges of no central control and changing user sets.

Contribution

It formulates spectrum access as potential games and proposes a distributed learning algorithm to reach near-optimal Nash equilibria in dynamic environments.

Findings

The proposed algorithm achieves throughput close to the optimal solution.

The spectrum access game is proven to be an ordinal potential game.

The method effectively handles dynamic user sets without central coordination.

Abstract

This paper investigates the problem of database-assisted spectrum access in dynamic TV white spectrum networks, in which the active user set is varying. Since there is no central controller and information exchange, it encounters dynamic and incomplete information constraints. To solve this challenge, we formulate a state-based spectrum access game and a robust spectrum access game. It is proved that the two games are ordinal potential games with the (expected) aggregate weighted interference serving as the potential functions. A distributed learning algorithm is proposed to achieve the pure strategy Nash equilibrium (NE) of the games. It is shown that the best NE is almost the same with the optimal solution and the achievable throughput of the proposed learning algorithm is very close to the optimal one, which validates the effectiveness of the proposed game-theoretic solution.