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Stochastic Modeling and Optimization for Cognitive Radio Networks

Project Information

  • “NeTS: Collaborative Research: Cognitive Capacity Harvesting Networks (CNS-1147851),” PI. National Science Foundation, September 1, 2011–August 31, 2014
  • This webpage and the materials provided here are based upon work supported by the National Science Foundation under Grant No. CNS-1147851.

Project Summary

Due to the emergence of ever increasing diversified applications provided by the smart devices such as smart phones, traditional telecommunications systems such as the wireless cellular systems no longer meet the ever exploding traffic demand, and cannot effectively deal with the shortage of available spectrum or congestion over wireless systems. On the other hand, tremendous temporal and spatial network resources, such as spectrum and computational capability, are severely under-utilized. Obviously, how to proactively harvest such residual resources and utilize them opportunistically to support diversified user traffic is an important yet challenging research direction. Although cognitive radio networks are to address this pressing issue, there is lack of viable network architecture in taking full advantage of the opportunistic spectrum access and there exist many practical design issues to be resolved. In this project, we investigate a flexible Cognitive Capacity Harvesting (CCH) network architecture to intelligently harvest network resources in both time and space and develop the corresponding technologies to support users’ services effectively. Moreover, we demonstrate the proposed CCH network along with the newly developed networking technologies can enable non-cognitive devices to significantly gain benefits from cognitive radio networks and provide innovative approaches to the cognitive radio networks design and optimization. This project research will open a new school of thoughts in better utilizing the residual network resources and potentially changes the design approaches for next-generation telecommunications systems.

Personnel at MSU

  • Pan Li, PI
  • Ming Li, Ph.D. Student
  • Weixian Liao, Ph.D. Student
  • Sergio Salinas, Ph.D. Student
  • Arun, Thapa, Ph.D. Student
  • Kaijin Zhang, Ph.D. Student

Personnel at Collaborative Institution

  • Yuguang Fang, University of Florida, PI
  • Savo Glisic, University of Oulu, PI

Publications

Journal Papers

  1. Arun Thapa, Ming Li, Sergio Salinas, and Pan Li, “Asymmetric Social Proximity Based Private Matching Protocols for Online Social Networks,” to appear in IEEE Transaction on Parallel and Distributed System.
  2. Ming Li, Sergio Salinas, Pan Li, Xiaoxia Huang, Yuguang Fang, and Savo Glisic, “Optimal Scheduling for Multi-radio Multi-channel Multi-hop Cognitive Cellular Networks,” to appear in IEEE Transactions on Mobile Computing.
  3. Ming Li, Pan Li, Xiaoxia Huang, Yuguang Fang, and Savo Glisic, “Energy Consumption Optimization for Multihop Cognitive Cellular Networks,” to appear in IEEE Transactions on Mobile Computing.
  4. Miao Pan, Pan Li, Yang Song, Yuguang Fang, Phone Lin, and Savo Glisic, “When Spectrum Meets Clouds: Optimal Session Based Spectrum Trading under Spectrum Uncertainty,” IEEE Journal on Selected Areas in Communications – Cognitive Radio Series, Vol. 32, No. 3, pp. 615-627, March 2014.
  5. Ming Li, Sergio Salinas, and Pan Li, “Locaward: A Security and Privacy Aware Location-Based Rewarding System,” IEEE Transactions on Parallel and Distributed Systems – Special Issue on Trust, Security, and Privacy, Vol. 25, No. 2, pp. 343-352, February 2014.
  6. Ming Li and Pan Li, “Crowdsourcing in Cyber-Physical Systems: Stochastic Optimization with Strong Stability,” IEEE Transactions on Emerging Topics in Computing – Special Issue on Cyber-Physical Systems, Vol 1, No. 2, pp. 218-231, December 2013.
  7. Sergio Salinas, Ming Li, Pan Li, “Privacy-Preserving Energy Theft Detection in Smart Grids: A P2P Computing Approach,” IEEE Journal on Selected Areas in Communications – Special Issue on Emerging Technologies in Communications, Vol. 31, No. 9, pp. 257-267, September 2013.
  8. Sergio Salinas, Ming Li, Pan Li, “Multi-Objective Optimal Energy Consumption Scheduling in Smart Grids,” IEEE Transactions on Smart Grid, Vol. 4, No. 1, pp. 341-348, March 2013.
  9. Pan Li and Yuguang Fang, “On the Throughput Capacity of Heterogeneous Wireless Networks,” IEEE Transactions on Mobile Computing, Vol. 11, No. 12, pp. 2073-2086, December 2012.
  10. Pan Li, Miao Pan, and Yuguang Fang, “Capacity Bounds of Three-Dimensional Wireless Ad Hoc Networks,” IEEE Transactions on Networking, Vol. 20, No. 4, pp. 1304-1315, August 2012.
  11. Miao Pan, Pan Li, and Yuguang Fang, “Cooperative Communication Aware Link Scheduling for Cognitive Vehicular Ad-hoc Networks,” IEEE Journal on Selected Areas in Communications – Special Issue on Broadband Wireless Communications for High Speed Vehicles, Vol. 30, No. 4, pp. 760-768, May 2012.
  12. Pan Li, Yuguang Fang, Jie Li, and Xiaoxia Huang, “Smooth Trade-offs Between Throughput and Delay in Mobile Ad Hoc Networks,” IEEE Transactions on Mobile Computing, Vol. 11, No. 3, pp. 427-438, March 2012.

Conference Papers

  1. Weixian Liao, Ming Li, Sergio Salinas, Pan Li, and Miao Pan, “Optimal Energy Cost for Strongly Stable Multi-hop Green Cellular Networks,” IEEE International Conference on Distributed Computing Systems (ICDCS’14), Madrid, Spain, June 30-July 3, 2014. (Acceptance ratio: 66/500 = 13%)
  2. Ming Li, Pan Li, Miao Pan, and Jinyuan Sun, “Economic-Robust Transmission Opportunity Auction in Multi-hop Wireless Networks,” IEEE International Conference on Computer Communications (INFOCOM’13), Turin, Italy, April 14-19, 2013. (Acceptance ratio = 280/1613 = 17%)
  3. Ming Li, Sergio Salinas, Arun Thapa, and Pan Li, “n-CD: A Geometric Approach to Preserving Location Privacy in Location-Based Services,” IEEE International Conference on Computer Communications (INFOCOM’13), Turin, Italy, April 14-19, 2013. (Acceptance ratio = 280/1613 = 17%)
  4. Miao Pan, Hao Yue, Pan Li and Yuguang Fang, “Throughput Maximization of Cooperative Wireless Mesh Networks Using Directional Antennas,” IEEE International Conference on Communications in China (ICCC’12), Beijing, China, August 15-18, 2012.
  5. Sergio Salinas, Ming Li, Pan Li, “Privacy-Preserving Energy Theft Detection in Smart Grids,” IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’12), Seoul, Korea, June 18-21, 2012.
  6. Dianjie Lu, Xiaoxia Huang, Pan Li, Jianping Fan, “Connectivity of Large-Scale Cognitive Radio Ad Hoc Networks,” IEEE International Conference on Computer Communications (INFOCOM’12), Orlando, Florida, March 25-30, 2012. (Acceptance ratio = 278/1547 = 18%)
  7. Miao Pan, Pan Li, Yang Song, Yuguang Fang, and Phone Lin, “Spectrum Clouds: A Session Based Spectrum Trading System for Multi-hop Cognitive Radio Networks,” IEEE International Conference on Computer Communications (INFOCOM’12), Orlando, Florida, March 25-30, 2012. (Acceptance ratio = 278/1547 = 18%)

Outreach and Education Activities for Broader Impact

Our major results have been disseminated through presentations and publications in meetings, conferences, and journals. A substantial quantity of the materials of this project have also been made publicly available online here.

Besides, the research in this project is one of the main topics in the split-level courses ECE 4813/6813: Communication Theory and ECE 4823/6823: Digital Communications, and the graduate courses ECE 8990: Wireless Communications and ECE 8990: Advanced Topics in Wireless Networks at MSU. The second graduate course is also offered to long-distance students. PI Li has also developed two new graduate courses ECE 8990: Advanced Topics in Cloud Computing and ECE 8990: Advanced Topics in Cognitive Radio Networks, which were offered in Spring 2013 for the first time. These two courses focused on fundamental challenges and hot topics in cloud computing and cognitive radio networks, respectively, and were highly received by both EE and CS graduate students. The research outcomes and network design methodologies developed in this project have been channelized into the classroom.

In addition, two of the Ph.D. students who worked on this project, i.e., Ming Li and Arun Thapa, graduated in Summer 2014. Ming joined the Department of Computer Science and Engineering at University of Nevada, Reno, and Arun joined the Department of Electrical Engineering at Tuskegee University, respectively, as a tenure-track Assistant Professor. Continuing collaborations with them has extended the impact of this project to universities in Nevada and Alabama as well.

 

nets11.txt · Last modified: 2015/10/09 12:34 by lipanleo