Software-defined radios and associated tools like GNU Radio allow researchers to simulate, implement, and evaluate digital communication algorithms. This document discusses cognitive radio technologies including software-defined radios that can sense spectrum usage and rapidly shift frequencies to avoid interference. It provides examples of using GNU Radio to receive AM and FM signals and modify a digital communication system. Research is exploring applications like autonomous airborne networks and underwater acoustic networks that have challenges with propagation delays.

1. _{}@buffalo.edu
Digital Communication Theory [3]
• At the core of cognitive radio operations (data
transmission, digital modulation/demodulation techniques,
pulse shaping, etc.)
Software Simulation Tools
• GNU Radio and Octave
• Free and open-source with active communities
• Wireless system visualization using “flowgraphs”
• Rapid reconfigurability
• Ever growing libraries of signal processing blocks
• Ability to create/implement custom signal processing
blocks
(C++ or Python)
• Data parsing and plotting
Hardware
• National Instruments USRP-N210: a software-
programmable radio
• FPGA-based radio that supports broad range of
frequencies
• Allows for rapid adaptation due to its ability to shift
between different frequencies of the spectrum [2]
• Digital Communication Theory
• GNU Radio Tutorials
• Created flowgraphs to receive AM and FM signals using
modulating/demodulating schemes
• Modified a digital communication system to use random data
patterns and a non-square pulse shape (Binary-Phase-Shift
Keying)
• Universal Software Radio Peripheral (USRP-N210 and
USRP-B210)
• Software-defined cognitive radios offer self-reconfigurability through
modular and programmable radio architectures
• Software tools such as GNU Radio, and hardware such as USRPs
are used to simulate, implement, and evaluate digital communication
and signal processing algorithms in the real-world
Platform-Independent System Design [4]
• Rapid iteration between simulation and prototyping
• Throughput and latency support for LTE-A and 802.11
networking standards
• Software-programmable wireless networks
Software-Defined Autonomous Airborne Networks [4]
• Variable network dynamics across geographically or
hierarchically dispersed and mobile wireless nodes
• SDR architecture with self-reconfigurable functionalities
that can be controlled through a program
Real-Time Configurable Underwater Acoustic Networks [4]
• Suffer from high path loss, long propagation delay and
Doppler
• Lack in standardization and energy efficiency
• Software-defined acoustic modems that intelligently
evaluate and adapt their communication parameters to
maximize spectral efficiency
[1] C. Nealon, "Making wireless 10 times faster - University at Buffalo", Buffalo.edu, 2014. [Online]. Available:
https://www.buffalo.edu/news/releases/2014/05/004.html. [Accessed: 07- Jul- 2016].
[2] C. Nealon, "Living in the ‘90s? So are underwater wireless networks - University at Buffalo", Buffalo.edu, 2016. [Online].
Available: http://www.buffalo.edu/news/releases/2016/01/034.html. [Accessed: 07- Jul- 2016].
[3] S. Haykin, Communication Systems, 4th ed. New York: John Wiley & Sons, Inc., 2001, pp. 1-337.
[4] G. Sklivanitis, A. Gannon, S. N. Batalama, and D. A. Pados, "Addressing next-generation wireless challenges with
commercial software-defined radio platforms," in IEEE Communications Magazine, vol.54, no.1, pp.59-67, Jan. 2016.
[5] GNU Radio Companion. The GNU Radio Foundation, Inc., 2008. Print.
[6] G. Sklivanitis, E. Demirors, S. N. Batalama, D. A. Pados, and T. Melodia, "ROCH: Software-defined radio toolbox for
experimental evaluation of All-spectrum cognitive networking," in Proceedings of the 21st ACM International Conference on
Mobile Computing and Networking (MobiCom 2015), 4th ACM Workshop of Software Radio Implementation Forum (SRIF
2015), Paris, France, Sept. 2015.
[7] E. Demirors, G. Sklivanitis, T. Melodia, and S. N. Batalama, "RcUBe: Real-time Reconfigurable Radio framework with self-
optimization capabilities," in Proceedings of the 12th IEEE International Conference on Sensing, Communication, and
Networking, (SECON), Seattle, WA, USA, June 2015.
[8] G. Sklivanitis, E. Demirors, A. Gannon, S. N. Batalama, D. A. Pados, and T. Melodia, "All-spectrum cognitive channelization
around narrowband and wideband primary stations," in Proceedings of the IEEE Global Communications Conference
(GLOBECOM), San Diego, CA, USA, Dec. 2015.
Figure 1: GNU Radio flowgraph for an FM transmitter
Basic Principles
Method
Conclusion
Future Challenges
References
Cognitive Wireless Communications on Software-Defined Radios
Jonathan Bressler, George Sklivanitis, Dr. Stella N. Batalama
Department of Electrical Engineering
Signals, Communications, and Networking Group
{jbressle, gsklivan, batalama}@buffalo.edu
Figure 2: GNU Radio FFT Plot for signal source
• More wireless devices -> congested spectrum -> slower
wireless communication [1]
• AM/FM radios cannot switch between frequencies without
being disassembled and rewired [2]
• Cognitive radios: intelligently analyze and sense spectrum to
avoid interference with other devices
• Efficient use of the spectrum yields faster wireless
communication
• The University at Buffalo Signals, Communications, and
Networking group is developing cognitive radio algorithms
that will maximize spectral efficiency [1]
Motivation
Figure 3: GNU Radio Plot for modulated signal
I would like to thank George Sklivanitis, Dr. Stella Batalama, and the graduate students in
the Signals, Communications and Networking group for helping me throughout this
research project. Also, thanks to the UB LSAMP program for giving me the opportunity to
perform research this summer.
Acknowledgements
Figure 4: GNU Radio flowgraph for an FM receiver
Figure 6: GNU Radio FFT Plot for recovered signal
Figure 7: USRP B210 and GNU Radio flowgraph for an FM receiver
Figure 6: FM Radio tower
Experimental FM Receiver
FM Receiver Simulation
Figure 5: GNU Radio Plot for demodulated signal