Maximizing Social Welfare in Operator-based Cognitive Radio Networks under Spectrum Uncertainty and Sensing Inaccuracy

TL;DR

This paper develops a spectrum auction framework for cognitive radio networks that accounts for spectrum uncertainty and sensing inaccuracies, proposing algorithms for maximizing social welfare under various information scenarios.

Contribution

It introduces a novel model combining spectrum uncertainty and sensing errors, along with optimal and greedy algorithms for social welfare maximization in dynamic settings.

Findings

Optimal dynamic programming algorithm for known requests

A greedy online algorithm with 1/2-competitiveness

Incentive-compatible payment scheme ensuring non-negative revenue

Abstract

In Cognitive Radio Networks (CRNs), secondary users (SUs) are allowed to opportunistically access the unused/under-utilized channels of primary users (PUs). To utilize spectrum resources efficiently, an auction scheme is often applied where an operator serves as an auctioneer and accepts spectrum requests from SUs. Most existing works on spectrum auctions assume that the operator has perfect knowledge of PU activities. In practice, however, it is more likely that the operator only has statistical information of the PU traffic when it is trading a spectrum hole, and it is acquiring more accurate information in real time. In this paper, we distinguish PU channels that are under the control of the operator, where accurate channel states are revealed in real-time, and channels that the operator acquires from PUs out of its control, where a sense-before-use paradigm has to be followed. Considering both spectrum uncertainty and sensing inaccuracy, we study the social welfare maximization problem for serving SUs with various levels of delay tolerance. We first model the problem as a finite horizon Markov decision process when the operator knows all spectrum requests in advance, and propose an optimal dynamic programming based algorithm. We then investigate the case when spectrum requests are submitted online, and propose a greedy algorithm that is 1/2-competitive for homogeneous channels and is comparable to the offline algorithm for more general settings. We further show that the online algorithm together with a payment scheme achieves incentive compatibility for the SUs while guaranteeing a non-negative revenue for the operator.