The document discusses enabling innovative wireless solutions through the design of wireless sensor networks for spectrum sensing and cognitive communication (CROPS project). It outlines the challenges of dynamic spectrum access and the need for knowledge of spectrum availability. The CROPS project objectives are to design spectrum sensing wireless sensor networks that address spectrum sensing requirements, sensor data fusion, cooperative transmission, and multihop communication, as well as sensing hardware design.

1. ENABLING INNOVATIVE WIRELESS SOLUTIONS
CROPS – WIRELESS SENSOR NETWORKS FOR
SPECTRUM SENSING AND COGNITIVE
COMMUNICATION
Viktoria Fodor, Mikael Skoglund (KTH)
Geir Øien (NTNU)
Jussi Ryynänen (Aalto)
http://www.ee.kth.se/commth/projects/CROPS/

2. The design of spectrum sensing WSNs
• To enable new and innovative wireless solutions
• Challenge:
- The large part of the radio spectrum is assigned to traditional
technologies/services (like mobile networks)
- Small IMF band can not accommodate all new solutions (WiFi,
personal networks, sensor networks, device to device communication)
• Solution: Spectrum access with cognitive radio
- Dynamic spectrum access of licensed bands
- Efficient spectrum sharing in open spectrum
• Knowledge about spectrum availability is needed
• Project objective: design of spectrum sensing wireless
sensor networks
- Spectrum sensing requirements
- Sensor data fusion
- Cooperative transmission and multihop communication
- Sensing hardware design
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3. Spectrum sensing requirements
• For sensing accuracy sensors have to share information
- Challenge: efficient sensing of transmitter with unknown location
- Proposed solution: sensor density and cooperation requirements for
efficient sensing
• Interference level at the primary receiver
have to be managed
- Challenge: unknown terminal positions
- Proposed solution: sum interference
models that can be used to evaluate other
perf. metrics
• Result: requirements towards efficient sensing, data fusion and
communication
• Selected publications:
- V. Fodor, I. Glaropoulos and L. Pescosolido, "Detecting low-power primary signals via distributed sensing to support
opportunistic spectrum access," submitted to IEEE ICC, 2009.
- I. Glaropoulos, V. Fodor, "On the efficiency of distributed spectrum sensing in ad-hoc cognitive radio networks," in Proc. of ACM
CoRoNet 2009, September 2009
- N. H. Mahmood, F. Yilmaz, and M.-S. Alouini, “A generalized and parameterized interference model for cognitive radio
networks,” in 12th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC ’11), San
Francisco, USA, June 2011.
- N. H. Mahmood, F. Yilmaz, M.-S. Alouini, and G. E. Øien, “On the Performance Analysis of Legacy Systems in the Presence of
Next Generation Interference,” Submitted for publication (Journal), 2011.
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4. Sensor data fusion
• The highly correlated sensor measurements have to be collected at an
aggregation point or shared by the sensors
- Challenge: efficiency with low computational complexity and delay
- Proposed solution: New techniques for distributed source-channel coding
and quantization for orthogonal and simultaneous transmission.
• Spectrum sensor data processing is easier if the location of the transmitter
is known
- Challenge: source localization under spatially correlated shadowing
- Proposed solution: algorithms that are robust in erroneous assumptions on
the channel parameters
• Selected publications:
- N. Wernersson, J. Karlsson and M. Skoglund, "Distributed quantizers over noisy channels," IEEE Transactions on Communications, June
2009.
- N. Wernersson, M. Skoglund and T. Ramstad, "Polynomial based analog source-channel codes," IEEE Transactions on Communications,
September 2009.
- N. Wernersson and M. Skoglund, "Nonlinear coding and estimation for correlated data in wireless sensor networks," IEEE Transactions
on Communications, October 2009.
- J. Karlsson and M. Skoglund, "Optimized low-delay source-channel-relay mappings," IEEE Transactions on Communications, May 2010.
- John T. Flåm, Ghassan M. Kraidy and Daniel J. Ryan: “Using a Sensor Network to Localize a Source under Spatially Correlated
Shadowing”, IEEE VTC Taipei 2010.
- John T. Flåm, Joakim Jaldén and Saikat Chatterjee: “Gaussian mixture modeling for source localization”, IEEE ICASSP 2011.
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5. Cooperative transmission
• Sensor and relay nodes need to collaborate in the
transmission of data to improve energy-efficiency
- Challenge: Joint optimization gives gains, but can be
complex and sensitive to design assumptions
- Proposed solution: New collaborative transmission
protocols based on relaying. Improved performance and
significant energy-saving demonstrated
• In case of simultaneous transmissions the power
allocation has to be fair and efficient
- Challenge: computational complexity and signaling
overhead has to be low, interference has to be maintained
- Proposed solution: Distributed power allocation based on
novel utility functions
• Selected publications:
- S. Yao and M. Skoglund, "Hybrid digital-analog relaying for cooperative transmission over slow fading channels," IEEE
Transactions on Information Theory, March 2009
- M. Khormuji and M. Skoglund, "On instantaneous relaying," IEEE Transactions on Information Theory, vol. 56, no. 7, pp.
3378-3394, July 2010.
- J. Karlsson and M. Skoglund, "Design and performance of optimized relay mappings," IEEE Transactions on
Communications, vol. 58, no. 9, pp. 2718-2724, September 2010.
- N. H. Mahmood, U. Salim, G. E. Øien, “Relative Rate Utility based Distributed Power Allocation Algorithm for Cognitive
Radio Network,” Poster presented in 2011 IEEE Communication Theory Workshop (CTW’11), Sitges, Spain, June 2011.
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6. Networking for spectrum sensing
• Routing for optimum overall energy efficiency to increase network
lifetime
- Challenge: existing solutions are too complex (NP-hard)
- Proposed solution: adaptive routing and transmission control heuristics
based on novel utility function
• Applying cooperative transmission techniques in mesh networks
- Challenge: cooperative transmission may increase interference in the
networks
- Proposed solution: co-optimized
relaying and channel access
scheme
• Selected publications:
- L. Yin, C. Wang, and G. E. Øien, “An Energy-Efficient Routing Protocol for Event-Driven Dense Wireless Sensor Networks,”
Springer International Journal of Wireless Information Networks, 2009 (invited paper).
- C. Wang, L. Yin, and G. E. Øien, "Energy-Efficient Route Configuration for Adaptive MPSK-Based Wireless Sensor Networks,”
EURASIP Journal of Wireless Communications and Networking, 2011.
- Liping Wang, Viktoria Fodor, Mikael Skoglund, Using cooperative transmission in wireless Multihop Networks, in Proc. of IEEE
International Symposium On Personal, Indoor and Mobile Radio Communications, September 2009
- Liping Wang, Viktoria Fodor, Cooperative geographic routing in wireless mesh networks, in Proc. of IEEE International Workshop
on Enabling Technologies and Standards for Wireless Mesh Networking (MeshTech), November 2010
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7. Sensing radio HW design
• Sensing HW needs to be able to sense narrow band channels over a wide
spectrum range
- Challenge:
• Radio HW capable of receiving in broad frequency range does not exist
- Proposed solution: CROPS implementation of WB synthesizer
• Outperforms recently proposed solutions considering tuning range, energy
consumption and noise
• 12th most downloaded paper in IEEE Aug. 2010
High Linearity Receiver
( SENDORA project) Algorithm HW Implementation
( SENDORA project)
Hardware Description Methods
for System Development
(CROPS project)
• 2 patent applications Wideband ADPLL
( CROPS project)
• Selected publications:
- A. Immonen, A. Pärssinen, S. Kiminki, V. Hirvisalo, M. Talonen and J. Ryynänen, "A Reconfigurable Multi-standard Radio
Platform", International Workshop on Energy Efficient and Reconfigurable Tran-sceivers, September, 2010
- L. Xu, K. Stadius, and J. Ryynänen, "A wide-band digitally controlled ring oscillator," in proc Int. Symp. Circuits Syst.,
May 2010, pp. 1983-1986.
- L. Xu, S. Lindfor, K. Stadius and J. Ryynänen, "A 2.4-GHz low-power all-digital phase-locked loop," IEEE J. Solid-State
Circuits, vol. 45, no. 8, pp. 1513-1521, Aug. 2010.
Viktoria Fodor – Nordite, June 2011 7

8. Relevance
• Scientific content
- New insights into the design of energy-efficient wireless
sensor networks
- With application to spectrum sensing
• Collaboration
- New highly successful Nordic research collaboration that
would not have happened otherwise
• High volume of research results and publications
• Visits and co-authored papers
• Several graduated PhD’s
• Spin-off projects, e.g. the EU/Fp7 projects “SENDORA”
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9. Exploitation
• What could be exploited by industry now?
- The concepts of cooperative sensing and transmission
- New wireless sensor network design principles and applications
- Improved spectrum sensing HW for opportunistic spectrum
sharing (2 patent applications)
• What steps are missing to make the results available for the
industry?
- Some of our results need to be tested in more realistic practical
scenarios
- Some of our suggested schemes do not fit present standards
• What kind of partners would we need for this?
- Telecom (Ericsson, Telenor)
- Industrial automation (ABB)
- System integrators and sensor developers
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