The concept of cognitive radio (CR) focuses on devices that can sense their environment, adapt configuration parameters, and learn from past behaviors. Architectures tend towards simplified decision-making algorithms inspired by human cognition. Initial works defined cognitive engines (CEs) founded on heuristics, such as genetic algorithms (GAs), and case-based reasoning (CBR) experiential learning algorithms. This hybrid architecture enables both long-term learning, faster decisions based on past experience, and capability to still adapt to new environments. This paper details an autonomous implementation of a hybrid CBR-GA CE architecture on a universal serial radio peripheral (USRP) software-defined radio focused on link adaptation. Details include overall process flow, case base structure/retrieval method, estimation approach within the GA, and hardware-software lessons learned. Unique solutions to realizing the concept include mechanisms for combining vector distance and past fitness into an aggregate quantification of similarity. Over-the-air performance under several interference conditions is measured using signal-to-noise ratio, packet error rate, spectral efficiency, and throughput as observable metrics. Results indicate that the CE is successfully able to autonomously change transmit power, modulation/coding, and packet size to maintain the link while a non-cognitive approach loses connectivity. Solutions to existing shortcomings are proposed for improving case-base searching and performance estimation methods. 1. Introduction Wireless communication devices and networks face outside influences that degrade performance and have potential to render links useless. New advances in the area of cognitive radio (CR), inspired by artificial intelligence integration with reconfigurable platforms, enable devices and networks to observe, make a decision and learn from past experience. Key problems faced by CR are how to effectively integrate both learning and decision onto software-defined radio (SDR) over-the-air platforms such that they can react to situations quickly and effectively. Specifically this paper address the realization and implementation of a cognitive engine (CE) on an SDR platform for the purpose of link adaptation. The problems addressed include incorporating mechanisms for system observation, triggering the engagement of a CE, architecting the CE such that it can both make decision when faced with new situations, and learn from past experience. Prior art has defined CE architectures based on heuristic decision making, such as GA, as
References
[1]
A. He, J. Gaeddert, K. Bae, et al., “Development of a case-based reasoning cognitive engine for IEEE 802.22 wran applications,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 13, no. 2, pp. 37–48, 2009.
[2]
J. Mitola, Cognitive radio—an integrated agent architecture for software defined radio, Ph.D. dissertation, Royal Institute of Technology (KTH), 2000.
[3]
A. Amanna and J. H. Reed, “Survey of cognitive radio architectures,” in Proceedings of the IEEE SoutheastCon Conference: Energizing Our Future, pp. 292–297, Charlotte, NC, USA, March 2010.
[4]
C. J. Rieser, Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking, Ph.D. dissertation, Virginia Tech, Blacksburg, Va, USA, 2004.
[5]
Y. Zhao, J. Gaeddert, L. Morales, K. Bae, J.-S. Um, and J. H. Reed, “Development of radio environment map enabled case- and knowledge-based learning algorithms for IEEE 802.22 wran cognitive engines,” in Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom '07), pp. 44–49, Orlando, Fla, USA, August 2007.
[6]
A. Aamodt and E. Plaza, “Case-based reasoning: foundational issues, methodological variations, and system approaches,” AI Communications, vol. 7, no. 1, pp. 39–59, 1994.
[7]
A. MacKenzie, J. Reed, P. Athanas et al., “Cognitive radio and networking research at virginia tech,” Proceedings of the IEEE, vol. 97, no. 4, pp. 660–686, 2009.
[8]
D. Goldberg, Genetic Algorithms in Search Optimization, and Machine Learning, Addison-Weslet Publishing, 1989.
[9]
J. D. Gaeddert, Facilitating wireless communications through intelligent resource management on software-defined radios in dynamic spectrum environments, Ph.D. dissertation, Virginia Polytechnic Institute & State University, Blacksburg, Va, USA, 2011.
[10]
B. P. Lathi, Modern Digital and Analog Communication Systems, Oxford University Press, 3rd edition, 1998.
[11]
M. Ettus, “Ettus research,” 2010, http://www.ettus.com.
[12]
T. Newman and J. Evans, “Parameter sensitivity in cognitive radio adaptation engines,” in Proceedings of the 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '08), pp. 1–5, Chicago, Ill, USA, October 2008.
[13]
B. Hilburn, “Cognitive radio open source system,” 2010, http://cornet.wireless.vt.edu/trac/wiki/Cross.
[14]
A. Amanna, D. Ali, M. Gadhiok, M. J. Price, and J. H. Reed, “Statistical framework for parametric optimization of cognitive radio systems,” in Proceedings of the Software Defined Radio Forum Technical Conference and Product Exposition (SDR '11), 2011.
[15]
T. Schmid, O. Sekkat, and M. B. Srivastava, “An experimental study of network performance impact of increased latency in software defined radios,” in Proceedings of the 2nd ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WinTECH '07), pp. 59–66, ACM, New York, NY, USA, 2007.
[16]
T. W. Rondeau, Application of artificial intelligence to wireless communications, Ph.D. dissertation, Virginia Polytechnic Institute & State University, Blacksburg, Va, USA, 2007.
[17]
A. He, J. Gaeddert, K. K. Bae, et al., “Development of a case-based reasoning cognitive engine for ieee 802.22 wran applications,” SIGMOBILE Mobile Computing and Communications Review, vol. 13, pp. 37–48, 2009.
[18]
S. Wess, K. Dieter Althoff, and G. Derwand, “Using k-d trees to improve the retrieval step in case-based reasoning,” in Proceedings of the Selected papers from the 1st European Workshop on Topics in Case-Based Reasoning (EWCBR '93), S. Wess, K. Dieter Althoff, and M. M. Richter, Eds., pp. 167–181, Springer, London, UK, 1993.
[19]
N. C. Tas, Link adaptation in wireless networks: a cross-layer approach, Ph.D. dissertation, University of Maryland, College Park, Md, USA, 2010.
[20]
K. Jayanthi, Some investigations on quality improvement using link adaptation techniques in cellular mobile networks, Ph.D. dissertation, Pondicherry University, 2010.
[21]
A. Soysal, S. Ulukus, and C. Clancy, “Channel estimation and adaptive m-qam in cognitive radio links,” in Proceedings of the IEEE International Conference on Communications (ICC '08), pp. 4043–4047, Beijing, China, May 2008.
[22]
H. Volos, C. Phelps, and R. Buehrer, “Initial design of a cognitive for mimo systems,” in Proceedings of the SDR Forum Technical Conference and Product Exposition (SDR '07), 2007.
[23]
H. Volos, C. Phelps, and R. Buehrer, “Physical layer cognitive engine for multi-antenna systems,” in Proceedings of the IEEE Military Communications Conference (MILCOM '08), pp. 1–7, San Diego, Calif, USA, November 2008.
[24]
H. Volos and R. Buehrer, “Cognitive engine design for link adaptation: an application to multi-antenna systems,” IEEE Transactions on Wireless Communications, vol. 9, no. 9, pp. 2902–2913, 2010.
[25]
H. Volos and R. Buehrer, “Robust training of a link adaptation cognitive engine,” in Proceedings of the IEEE Military Communications Conference (MILCOM '10), pp. 1442–1447, San Jose, Calif, USA, November 2010.
