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Improving Spectrum Sensing and Multiuser Cooperation in Wireless Cognitive Radio Networks.
详细信息   
  • 作者:Song ; Chengqi.
  • 学历:Doctor
  • 年:2010
  • 导师:Zhang,Qian,eadvisor
  • 毕业院校:Hong Kong University of Science and Technology
  • Department:hkust.
  • ISBN:9781124590264
  • CBH:3450395
  • Country:China
  • 语种:English
  • FileSize:3093259
  • Pages:121
文摘
Nowadays wireless spectrum resource is more and more scarce because most wireless spectrum bands have been allocated to licensed users,however measurements show that many bands are very underutilized. To solve this problem,Cognitive Radio (CR) is proposed to improve spectrum utility without interference to licensed users. CR need to keep aware of wireless environment by spectrum sensing and access unused bands without interference to licensed users. In this proposal we focus on two important issues in CR networks: spectrum sensing and multi-user cooperation. i) Spectrum sensing is the key component of CR technology. We improve spectrum sensing by studying spectrum usage prediction and cooperative spectrum sensing. Spectrum usage prediction can significantly reduce the sensing range basing on history information. We conduct a large scale empirical study basing on realworld measurement to reveal the predictability and ideal predictors for spectrum usage. We also propose a new spectrum sensing scheme assisted with spectrum prediction. Cooperative spectrum sensing is introduced to improve sensitivity with spatial diversity. We improve it by designing a Carrot-and-Stick based strategy to stimulate cooperation,relaxing it into asynchronous mode and introducing multi-channel coordination mechanism. ii) Because the wireless environment of CR networks is more complicated and unstable comparing with traditional wireless networks,the cooperation of multiple users is more important. Cooperative spectrum sensing is a good example and we also study another two important problems: stimulating data forwarding and CR network planning. For stimulating data forwarding,we show that the traditional solutions are context-based and our new proposed context-free solution is more lightweight and bug-free. For CR network planning,we show that our solution can support more clients than traditional planning method.

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