Haichuan Ding

Users First: Service-Oriented Spectrum Auction With a Two-Tier Framework Support

Auction-based secondary spectrum market provides a platform for spectrum holders to share their under-utilized licensed bands with secondary users (SUs) for economic benefits. However, it is challenging for SUs to directly participate due to their limited battery power and capability in computation and communications. To shift complexity away from users, in this paper, we propose a novel multi-round service-oriented combinatorial spectrum auction with two-tier framework support. In Tier I, we introduce several secondary service providers (SSPs) to provide end-users with services by using purchased licensed bands even if the end-users do not have cognitive radio capability. When an SU submits its service request with certain bidding allowance to its SSP, the SSP will help find out which bands within its area are available and bid for the desired ones from the market in Tier II. Specifically, we formulate the bidding process at the SSP as an optimization problem by considering interference management, spectrum uncertainty, flow routing, and budget allowance. In Tier II, considering two possible manners of the seller, we propose two social-welfare-maximizing auction mechanisms accordingly, including the winner determination based on weighted conflict graph and the Vickrey-Clarke-Groves-styled price charging mechanism. Extensive simulations have been conducted and the results have demonstrated the higher revenue of the proposed scheme compared with the traditional commodity-oriented single-round truthful schemes.

Cognitive Capacity Harvesting Networks: Architectural Evolution Toward Future Cognitive Radio Networks

Cognitive radio technologies enable users to opportunistically access unused licensed spectrum and are viewed as a promising way to deal with the current spectrum crisis. Over the last 15 years, cognitive radio technologies have been extensively studied from algorithmic design to practical implementation. One pressing and fundamental problem is how to integrate cognitive radios into current wireless networks to enhance network capacity and improve users’ experience. Unfortunately, existing solutions to cognitive radio networks (CRNs) suffer from many practical design issues. To foster further research activities in this direction, we attempt to provide a tutorial for CRN architecture design. Noticing that an effective architecture for CRNs is still lacking, in this tutorial, we systematically summarize the principles for CRN architecture design and present a novel flexible network architecture, termed cognitive capacity harvesting network (CCHN), to elaborate on how a CRN architecture can be designed. Unlike existing architectures, we introduce a new network entity, called secondary service provider, and deploy cognitive radio capability enabled routers, called cognitive radio routers, in order to effectively and efficiently manage resource harvesting and mobile traffic while enabling users without cognitive radios to access and enjoy CCHN services. Our analysis shows that our CCHN aligns well to industrial standardization activities and hence provides a viable approach to implementing future CRNs. We hope that our proposed design approach opens a new venue to future CRN research.

A Secure Collaborative Machine Learning Framework Based on Data Locality

Advancements in big data analysis offer cost-effective opportunities to improve decision-making in numerous areas such as health care, economic productivity, crime, and resource management. Nowadays, data holders are tending to sharing their data for better outcomes from their aggregated data. However, the current tools and technologies developed to manage big data are often not designed to incorporate adequate security or privacy measures during data sharing. In this paper, we consider a scenario where multiple data holders intend to find predictive models from their joint data without revealing their own data to each other. Data locality property is used as an alternative to multi-party computation (SMC) techniques. Specifically, we distribute the centralized learning task to each data holder as local learning tasks in a way that local learning is only related to local data. Along with that, we propose an efficient and secure protocol to reassemble local results to get the final result. Correctness of our scheme is proved theoretically and numerically. Security analysis is conducted from the aspect of information theory.

Smart Cities on Wheels: A Newly Emerging Vehicular Cognitive Capability Harvesting Network for Data Transportation

With the emergence of IoT and smart cities, wireless data traffic is exponentially increasing, motivating telecom operators to search for new solutions. In this article, we propose a solution based on the premise that vehicles are equipped with CR routers, specially designed powerful devices with agile communication interfaces, rich computing resources, and abundant storage space. To fully exploit the capabilities of such vehicles, we propose a V-CCHN architecture where CR router enabled vehicles are employed to connect end devices to the V-CCHN and transport data to intended locations via storage of on-board CR routers and spectrum resources harvested from various systems. Considering the abundant storage of on-board CR routers and a wide range of under-utilized spectrum, the V-CCHN is expected to effectively transport substantial amounts of data between end devices and data networks, which offers an effective solution to handling the explosive wireless data traffic and well complements existing telecommunications networks.

Collaborative Spectrum Trading and Sharing for Cognitive Radio Networks

Spectrum trading is one of the most promising approaches to enabling dynamic spectrum access (DSA) in cognitive radio networks (CRNs). With this approach, unlicensed users (a.k.a. secondary users) offer licensed users (a.k.a. primary users) with monetary rewards or improved quality of services (QoSs) in exchange for spectrum access rights. In this chapter, we present a comprehensive introduction to spectrum trading. First, we provide a brief introduction to DSA and CRNs as the background and motivation for the spectrum trading. Then, X. Li ( ) School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China e-mail: lixuanheng@mail.dlut.edu.cn H. Ding • Y. Fang Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA e-mail: dhcbit@gmail.com; fang@ece.ufl.edu M. Pan Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA e-mail: mpan2@uh.edu P. Li Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA e-mail: lipan@case.edu X. Huang Shenzhen Institutes of Advanced Technology, China Academy of Sciences, Shenzhen, Guangdong, China e-mail: xx.huang@siat.ac.cn S. Glisic Department of Communication Engineering, University of Oulu, Oulu, Finland e-mail: savo.glisic@ee.oulu.fi

Energy-Efficient Secondary Traffic Scheduling with MIMO Beamforming

When equipped with multiple antennas, secondary users in cognitive radio networks are able to communicate even when neighboring primary users are active by transmitting in the null space of the communication channel occupied by primary users. In this case, the throughput of a secondary link is limited by the transmission power and the dimension of the null space, i.e., the number of active primary users nearby. Since the number of active primary users is time-varying, the required transmission power to support certain data rate changes from time to time. Thus, secondary users could adapt their transmission to the variation of the primary traffic to improve energy efficiency. In view of that, we develop an energy-efficient traffic scheduling scheme for secondary users equipped with multiple antennas. By formulating the traffic scheduling problem as a Markov decision problem, an energy-efficient transmission scheme is derived from linear programming. The analytical results are verified by simulations and the impacts of various parameters are discussed. The superiority of the derived scheme is also shown by comparing with a randomized scheme.