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Beyond Cognitive Radio: Lower Layer Protocols Venu Veeravalli Yingbin Liang.

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Presentation on theme: "Beyond Cognitive Radio: Lower Layer Protocols Venu Veeravalli Yingbin Liang."— Presentation transcript:

1 Beyond Cognitive Radio: Lower Layer Protocols Venu Veeravalli Yingbin Liang

2 What’s Special about Cognitive Radio Network  Network Structures: - Ad hoc vs Cellular - Network with infrastructures - Research are applicable to cellular networks  How is it different from existing WiFi - Cognition - WiFi is not aware of environment

3 Broader View  Broader View of Cognitive Radio - not just sensing and interference avoidance - Not necessarily primary and secondary, can be certain networks with open resources with some rules setting up for it - Cognition: learn environment and act

4 Learning  What should we learn online - traffic models of coexisiting networks - learning transmission environment/channel; - extrinsic interference environment - power levels; modulations  What a priori knowledge should/could be available - database - power level - codebook and modulation scheme

5 Learning  Metrics for Learning: - bits/sec/Hz/sqm - packet success rate - connection failure - to what extend that other users is not affected - how to translate higher level metrics to lower level

6 Future Topics  How to incorporate and exploit information (phy/mac) - depends on network structure - adaptive transmission based on environment and constraints - adaption of power, bandwidth, modulation

7 Questions to Be Addressed  How to improve sensing robustness, and how to trade off between robustness and overhead in cooperative sensing?  How to take into account geographical locations of radios to improve channel selection and access efficiency?  How to estimate and exploit time-varying fading channel state for better channel selection and access?  How to apply information theoretic insights to improve interference control and mitigation?  How to apply PHY insights for small networks to the design of large cognitive networks?  What are reasonable models for node mobility in cognitive networks, and how to design networks with node mobility?  How to optimally exploit feedback from networks


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