A Fuzzy Inference System for Automatic Setting of the Processing Threshold in an IEEE 802.11 Cognitive Radio
Cognitive radios learn from the environment in order to modify their parameters, enhancing the efficiency in the communication. Fuzzy inference systems allow to reproduce nonlinear and uncertainty relationships among multiple variables, representing a major development opportunity in cognitive radios. This approach is explored in the present work to build the first IEEE802.11 cognitive radio where an automatic real-time setting of the receiver processing threshold is carried out. The paper presents the design and implementation of a fuzzy inference system that adapts a no-conventional parameter in the OFDM physical layer (processing threshold) of an IEEE 802.11 radio to the environmental conditions through four characteristic wireless network parameters. The fuzzy system is tuned up through a novel technique which includes experts’ knowledge and problem behaviour, allowing to keep semantic descriptions, while the membership functions are adjusted and the number of rules reduced. The proposed development is implemented in GNU Radio including the IEEE802.11/a/g/p flowgraph. In addition, a proof-of-concept over USRP N210 is carried out in order to evaluate and analyze the fuzzy system performance in the cognitive radio. Results show how a fuzzy system reproduces properly non-linear relations in the threshold assignment decision. The design is deeply described and shared under GNU V3 license in order to encourage the inclusion of fuzzy systems in cognitive radio.
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Z. Li, B. Liu, J. Si, F. Zhou, Optimal Spectrum Sensing Interval in Energy-Harvesting Cognitive Radio Networks, IEEE Transactions on Cognitive Communications and Networking, Volume 3, (Issue 2), June 2017, Pages 190-200.
H. Cao, H. Tian, J. Cai, A. S. Alfa, S. Huang, Dynamic Load-Balancing Spectrum Decision for Heterogeneous Services Provisioning in Multi-Channel Cognitive Radio Networks, IEEE Transactions on Wireless Communications, Volume 16, (Issue 9), September 2017, Pages 5911-5924.
Esenogho, E., Srivastava, V., Channel Assembling Strategy in Cognitive Radio Networks: a Queuing Based Approach, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (1), pp. 31-47.
Anusha, M., Srikanth, V., An Efficient Mac Protocol for Reducing Channel Interference and Access Delay in Cognitive Radio Wireless Mesh Networks, (2016) International Journal on Communications Antenna and Propagation (IRECAP), 6 (1), pp. 14-18.
H. M. Almasaeid, A. E. Kamal, Receiver-Based Channel Allocation in Cognitive Radio Wireless Mesh Networks, IEEE/ACM Transactions on Networking, Volume 23, (Issue 4), August 2015, Pages 1286-1299.
H. M. Almasaeid, A. E. Kamal, Exploiting Multichannel Diversity for Cooperative Multicast in Cognitive Radio Mesh Networks, IEEE/ACM Transactions on Networking, Volume 22, (Issue 3), June 2014, Pages 770-783.
Hashmi, S., Sattar, S., Soundararajan, K., Multi Objective Coordination Approach for Resource Utilization in Heterogeneous Cognitive Radio Network, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (1), pp. 72-79.
Orumwense, E., Afullo, T., Srivastava, V., Using Massive MIMO and Small Cells to Deliver a Better Energy-Efficient Cognitive Radio Network, (2016) International Journal on Communications Antenna and Propagation (IRECAP), 6 (5), pp. 274-281.
Z. Li, S. Cheng, F. Gao, Y. C. Liang, Sequential Detection for Cognitive Radio With Multiple Primary Transmit Power Levels, IEEE Transactions on Communications, Volume 65, (Issue 7), July 2017, Pages 2769-2780.
A. Tsakmalis, S. Chatzinotas, B. Ottersten, Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks, IEEE Transactions on Wireless Communications, Volume 16, (Issue 7), July 2017, Pages 4654-4668.
J. Oksanen, B. Kaufman, V. Koivunen, H. V. Poor, Robotics Inspired Opportunistic Routing for Cognitive Radio Using Potential Fields, IEEE Transactions on Cognitive Communications and Networking, Volume 1, (Issue 1), March 2015, Pages 45-55.
M. Matinmikko, T. Rauma, M. Mustonen, I. Harjula, H. Sarvanko, A. Mämmelä, Application of Fuzzy Logic to Cognitive Radio Systems, IEICE Transactions on Communications, Volume E92.B, Issue (12), 2009, Pages 3572-3580.
R. Elgadi, A. R. Hilal, O. Basir, A fuzzy logic approach for cooperative spectrum sensing in cognitive radio networks,IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Vol. 30, pp. 1-4, Windsor, ON, May 2017.
A. Al-Saadi, R. Setchi, Y. Hicks, Semantic Reasoning in Cognitive Networks for Heterogeneous Wireless Mesh Systems, IEEE Transactions on Cognitive Communications and Networking, Volume 3,(Issue3), June 2017, Pages 1-10.
T. Vilches and D. Dujovne, GNUradio and 802.11: performance evaluation and limitations, IEEE Network, Volume 28, (Issue 5), October 2014, Pages 27-31.
J.-J. van de Beek, M. Sandell, P. O. Borjesson, ML estimation of time and frequency offset in OFDM systems, IEEE Transactions on Signal Processing, Volume 45, (Issue 7),July 1997,Pages 1800–1805.
T. Schmidl, D. Cox, Robust frequency and timing synchronization for OFDM, IEEE Transactions on Communications, Volume 45, (Issue 12),December 1997,Pages 1613–1621.
P. Fuxjäger, A. Costantini, D. Valerio, P. Castiglione, G. Zacheo, T. Zemen, F. Ricciato, IEEE 802.11p Transmission Using GNURadio,Karlsruhe Workshop on Software Radios (WSR), Vol. 6, pp. 1-4, Karlsruhe, Germany, March 2010.
B. Bloessl, M. Segata, C. Sommer, F. Dressler, An IEEE 802.11a/g/p OFDM Receiver for GNU Radio, ACM SIGCOMM Workshop of Software Radio Implementation Forum, Vol. 2,pp. 9-16, Hong Kong, China, August 2013.
L. Chia-Horng, On the design ofOFDM signal detection algorithms for hardware implementation, IEEE GLOBECOM, Vol. 7, pp. 596-599, San Francisco, CA, December 2003.
E. Sourour, H. El-Ghoroury, D. McNeill, Frequency offset estimation and correction in the IEEE 802.11a WLAN,IEEE Vehicular Technology Conference, Vol. 60, pp. 4923-4927, Los Angeles, CA,September 2004.
T. Hrycak, S. Das, G. Matz, H. G. Feichtinger, Practical Estimation of Rapidly Varying Channels for OFDM Systems, IEEE Transactions on Communications, Volume 59, (Issue 11), November 2011, Pages 3040–3048.
C. D. R. Rodríguez, G. P. Leguizamón and C. S. Fajardo, "Processing threshold in an IEEE 802.11a/g/p receiver over GNU radio: A fuzzy logic application," 2017 IEEE Symposium Series on Computational Intelligence, Honolulu, USA, 2017, pp. 1-8.
J. M. Mendel, Fuzzy logic systems for engineering: a tutorial, Proceedings of the IEEE, Volume 83, (Issue 3), March 1995, Pages 345-377.
M. Cloughley, K. M. Muttaqi M H. Du, Damping of low-inertia machine oscillations using Takagi-Sugeno fuzzy stabiliser tuned by genetic algorithm optimisation to improve system stability, IET Generation, Transmission & Distribution, Volume 8, (Issue 2), February 2014, Pages 339-352.
T. J. Ho, Urban Location Estimation for Mobile Cellular Networks: A Fuzzy-Tuned Hybrid Systems Approach, IEEE Transactions on Wireless Communications, Volume 12, (Issue 5), May 2013, Pages 2389-2399.
Li-Xin Wang, A course in Fuzzy Systems and Control (Prentice-Hall International, 1997, Pages 59-116).
R. Storn, K. Price, Differential Evolution a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization, Volume 11, (Issue 4), December1997, Pages341-359.
J. E. Nash, J. V. Sutcliffe, River flow forecasting through, Part I: A conceptual models discussion of principles, Journal of Hydrology, Volume 10, (Issue 3), April 1970, Pages 282–290.
Mohanty, K., Fuzzy Control of Wind Cage Induction Generator System, (2017) International Journal on Energy Conversion (IRECON), 5 (4), pp. 122-129.
Shouman, M., El Bayoumi, G., Adaptive Robust Control of Satellite Attitude System, (2015) International Review of Aerospace Engineering (IREASE), 8 (1), pp. 35-42.
Ghernaout, A., Liazid, A., Benamar, A., Zerhouni, N., Abductive Approach Using Fuzzy Petri Nets for Industrial System Diagnosis, (2015) International Review of Civil Engineering (IRECE), 6 (5), pp. 133-144.
Abadi, I., Musyafa, A., Soeprijanto, A., Type-2 Fuzzy Logic Controller Based PV Passive Two-Axis Solar Tracking System, (2015) International Review of Electrical Engineering (IREE), 10 (3), pp. 390-398.
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