Cognitive Radio Evolution from Agile Platforms to Omniscient Networks: the Road from Dreams to Prototypes

1. Awareness Sensing and Modeling Learning Building and Retaining Knowledge Radio Hardware Adapting Evolution and Optimization Cognitive Radio Evolution from Agile Platforms to Omniscient Networks: the Road from Dreams to Prototypes Charles W. Bostian Virginia Tech bostian@vt.edu

2. Acknowledgements This project is supported by Award No. 2005-IJ-CX-K017 awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the author(s) and do not necessarily reflect the views of the Department of Justice. This material is based upon work supported by the National Science Foundation under Grant No. CNS-0519959. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF). Defense Advanced Research Projects Agency Strategic Technology Office DARPA Order AF89-00 This work is also supported by Air Force Institute of Technology (AFIT). The views expressed in this article are those of the author and do not reflect the official policy or position of the Air Force, Department of Defense or the U.S. Government. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. Government. Center for Wireless Telecommunicationswww.cognitiveradio.wireless.vt.edu

3. Acknowledgment: The VT Team

4. An (old) radio guy’s vision of cognitive radio: A universal transceiver (all modes and all frequencies) capable of discovering radios like itself and working cooperatively to negotiate frequencies, waveforms, and protocols to optimize performance subject to user needs based on the radio’s awareness of its environment and its past experience.

5. The VT Public Safety Cognitive Radio • Recognize any P25 Phase 1 waveforms • Identify known networks • Interoperate with legacy networks • Provide a gateway between incompatible networks • Serve as a repeater when necessary – useful when infrastructure has been destroyed or does not exist.

6. In developing this prototype, we have solved some hard problems in rapid reconfiguration of a radio platform and in signal recognition and synchronization. Find a signal of interest Configure this in real time and operate it.

7. Cognitive Engine + SDR = Cognitive Radio

8. The relatively easier part – realization of the cognitive engine General Implementations: A restricted implementation: the VT Public Safety Cognitive Radio

9. FCC Worries: Code correctness, insecure memory accesses, tamper resistance. Off-line unit testing and formal verification plus light-weight yet effective anti-tampering methods to ensure that any module replacement is compliant. Ensures that any replacement of the modules, including over-the-air updates is done by trusted parties.

10. The harder part – building a “universal” radio platform

12. The GPP Problem – Latency and Inability to Control Timing OK for narrowband waveforms with simple timing requirements. A real problem for wideband waveforms and MACs requiring precise timing.

13. The solution that we are developing now: A hybrid architecture containing fixed and reconfigurable subsystems. • Embedded GPP performs cognitive functions and determines radio configuration • Reconfigurable FPGA and ASICs perform PHY and MAC layer operations • Accelerators implement application layer functions

14. System Overview of PSCR (hybrid implementation)‏ ADC/DAC FPGA Analog RF DSP GPP VTSDCSS RF front-end PGA ADC DDC Spectrum Sweeper Signal Classifier Waveform Recognition GUI & Center Controller Waveform Knowledge Base Case-based Waveform Solution Maker RX RF front-end PGA ADC DDC Filter Gain Demod configure FEC Decoder De-packet Binary Data MAC Layer Protocol MAC Carrier Sense Algorithm Packet Binary Source TX FEC Encoder RF front-end PGA DUC DAC Filter Gain Mod

15. My student Ying Wang will demonstrate some of our current spectrum, waveform identification, and radio configuration technology as part of this meeting. She and my student Qinqin Chen invented the system we will demonstrate and many others in our group contributed to the implementation.

16. What is wrong with this picture? • One radio platform can’t do it all. • Focuses on interactions of two nodes. • Ignores network issues. • Ignores applications that the radio will run.

17. The reality: • Multiple networks • Multiple protocols • Multiple applications • Dynamic Spectrum Access All this leads to the concept of an application and network driven integrated architecture for a cognitive node

18. Architecture for An Application and Network Driven Integrated Cognitive Node Conceived by my student Feng Andrew Ge to capture the overall cognitive radio efforts of our group.

19. An important part of the implementation: The Universal Cognitive Gateway, dissertation topic of my student Qinqin Chen

20. Another application: dissertation work of my student Mark Silvius

21. Dynamic Cellular Cognitive Radio (Ying Wang) PCN PPCN CMT Basic Concept 700M Hz Application Scenario Software Structure

22. Contact Information Charles W. Bostian Alumni Distinguished Prof. Virginia Tech bostian@vt.edu 540-231-5096 http://www.cognitiveradio.wireless.vt.edu