Chunsheng Xin (PI), "EARS: Collaborative Research: Enhancing Spectral Access via Directional Spectrum Sensing Employing 3D Cone Filterbanks: Interdisciplinary Algorithms and Prototypes," NSF, Grant # ECCS-1247853, $125K, 09/2012 - 08/2015. A joint project with University of Akron ($225K), University of Toledo ($100K), and Ohio Northern University ($50K).
Chunsheng Xin (PI), "NeTS: Collaborative Research: Learning to help: Trading spectrum ownership for performance," NSF, Grant # CNS-1017172, $141K, 09/2010 - 08/2013. A joint project with University of Delaware ($200K).
Chunsheng Xin (PI), "Collaborative Research: NeTS-WN: Toward High Performance Mesh Networks," NSF, Grant # CNS-0721313, $172.5K, 09/2007 - 08/2010. A joint project with University of Delaware ($220K) and Argon ST Network Systems ($27K).
Chung-Chu Hsieh (PI), Chunsheng Xin (Co-PI), "Collaborative Research: Implementation of Vertically Integrated Curriculum for Cognitive Radio Communications," NSF, Grant # DUE-0919856, $95K, 09/2009 - 08/2011. A joint project with Virginia Tech ($80K).
Wireless technology plays a key role in both the national economy and our everyday life. In 2010, there are already 3 billion wireless devices around, and this number is projected to reach 100 billions by 2025. In addition, wireless applications are also growing quickly. Such huge number of wireless devices and quickly growing wireless applications all need one common resource - radio spectrum. However, the radio spectrum is a limited and extremely valuable natural resource, and is being exhausted due to inefficient allocation. This spectrum scarcity problem has drawn the attention of the highest level, which calls on "to explore innovative spectrum-sharing technologies". This promotes significant research in cognitive radio communications and networking in the last decade.
We are working on several projects to address this national need, including security for cognitive radio networks, cognitive radio network architectures, anti-jamming rendezvous algorithms, and theoretical modeling. Our research methodology is deeply rooted in theory, in that our research is guided by fundamental theories in relevant fields, and in turn develops mathematical models for gaining new insights.
Security for Cognitive Radio Networks
The powerful capability of cognitive radio to reconfigure the waveform, and the dynamic manner of spectrum access raise two new security threats to cognitive radio networks, primary user emulation (PUE) and spectrum sensing data falsification (SSDF). A malicious user can launch PUE attacks to cause denial of service (DoS) to secondary users, such that the network performance is significantly degraded.
In collaborative spectrum sensing, a fusion node determines the availability of a channel based on sensing data from other nodes. A malicious user can launch an SSDF attack, i.e., sending false sensing data to mislead the fusion node, which would cause either DoS to secondary users, or harmful interference to primary users.
We are designing effective countermeasure algorithms and mechanisms to address the PUE and SSDF attacks to build secure cognitive radio networks.
Cognitive Radio Network Architectures
The existing architecture of cognitive radio networks fails to offer incentives to primary users. Hence, secondary users have to conduct spectrum sensing extensively and behave conservatively, which results in large overhead, and a much lower spectrum utilization than expected. Moreover, the existing architecture is vulnerable to the PUE and SSDF attacks. Together, these two issues can reduce the benefit of cognitive radio networks to a marginal level.
Guided by the theories of network coding and dirty paper coding, we are designing new cognitive radio network architectures to incentivize primary users to cooperate with secondary users for spectrum sharing, and relieve the PUE and SSDF attacks, such that both the performance of both primary users and secondary users are dramatically improved.
Furthermore, capitalizing on the recent evolution of spectrum policies, we am designing a novel architecture termed on-demand spectrum access (ODSA), which eliminates the PUE and SSDF attacks and enables users to securely and efficiently share spectrum.
Anti-Jamming Rendezvous for Cognitive Radio Networks
Rendezvous refers to how a transmitter finds the channel of a receiver. In traditional wireless networks such as WiFi, this is a trivial problem, because all nodes are on the same channel. However, it is a totally different story for cognitive radio networks.
In cognitive radio networks, the secondary user or cognitive radio nodes typically operate on different channels due to the dynamic and spatially heterogeneous availability of channels. This situation raises a challenge for MAC Protocols: how do two nodes rendezvous? Many studies assumed using a common control channel. However, this is vulnerable to jamming and traffic congestion.
We are devising proactive anti-jamming rendezvous algorithms, such that cognitive radio nodes are able to proactively find the peers, to achieve efficient, lightweight, and near-optimal rendezvous.
Theoretical Modeling of Cognitive Radio Networks
The objective of theoretical modeling is to obtain fundamental understanding of cognitive radio networks. For instance, what is the network capacity of a cognitive radio network under dynamic spectrum sharing? Can we develop a model to analyze the packet delay? What are the spectrum sensing performance bounds and tradeoffs? Can we formulate the dynamic, heterogeneous resource to facilitate routing and other functions? For these problems, we have developed either closed-form solutions or mathematical models.