Guangen Wu

A Waiting-Time Auction Based Dynamic Spectrum Allocation Algorithm in Cognitive Radio Networks

Auction based dynamic spectrum access is an efficient approach to solve the spectrum shortage problem. However, conventional spectrum auction algorithms mainly concentrated on maximizing the revenue, under which the dynamic spectrum access (DSA) users with poorer cognitive abilities are usually hard to win the auction for spectrum access. In this paper, we propose a novel waiting-time auction (WTA) algorithm to improve the winning probabilities for the DSA users with poorer cognitive abilities. Specifically, we formulate the spectrum allocation as an auction game. In the auction, all game users bid with waiting time to obtain the spectrum access opportunities, unlike the conventional approaches where users bid by using money. Correspondingly, users with poorer cognitive ability, usually having the lower time cost, can win the auction with higher probabilities through bidding longer waiting time. We prove that there exists a unique Nash equilibrium (NE) in the WTA based spectrum-allocation game and the NE is desirable for all game users. Both theoretical and simulation analyses show that our proposed WTA algorithm can effective improve the winning probabilities of users with lower cognitive abilities.

Dynamic Spectrum Auction with Time Optimization in Cognitive Radio Networks

Dynamic spectrum auction has been considered as one of potential approaches on spectrum allocation in cognitive radio networks. As an modified version of traditional static or quasi-static spectrum auction, dynamic spectrum auction should not only increase the auction revenue of the owner of spectrum, but also improve spectrum utilization on fine time granularity. We propose a dynamic spectrum auction algorithm with time optimization (DSA-TO) in 802.22 networks. First, the effects of auction period on auction revenue and spectrum utilization are argued and optimized. Then we give a complete spectrum auction procedure where an adaptive reservation price is used to balance the revenue and utilization of spectrum auction. Performance analyses show that the DSA-TO algorithm can resist collusion effectively and has low complexity as well as good revenue and utilization. Simulation results show that the DSA-TO algorithm is reasonable in the optimization of auction period and can keep a fine spectrum utilization and bring more revenue for both single unit spectrum auction and multi-unit spectrum auction.

A DOF‐based dynamic spectrum auction algorithm in cognitive femtocell

Dynamic spectrum auction (DSA) has been considered as one of potential spectrum allocation approaches in cognitive femtocell networks. As a modified version of traditional spectrum auction, DSA should not only increase auction revenue but also improve spectrum utilization on finer time granularity. We propose a DSA algorithm based on a double optimization framework (DOF), which focuses on the optimization of auction revenue and spectrum utilization. The optimization processing consists of two stages. Firstly, a proper auction period is selected to balance the expected spectrum utilization and auction revenue. Then, the cognitive femtocell base station adjusts its reserve price with the repetition of auction to leverage over instant revenue and spectrum utilization. At the same time, the bidders can adjust their bidding price to improve utilities. Performance analysis shows that the DOF‐based DSA algorithm has low complexity and can resist collusion, so it can be carried out frequently with small overhead. On the other hand, it is better than the greedy algorithm and Vickrey–Clarke–Groves auction on revenue. Simulation results show that the DOF‐based DSA algorithm can keep a fine spectrum utilization and bring the cognitive femtocell base station more revenue in both single‐unit award spectrum auction and multi‐unit awards spectrum auction. Copyright