Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices
Abstract
:1. Introduction
2. Related Work and Contributions
2.1. Technology-Specific Detection Solutions
2.2. Existing Studies of the Distribution of RSSI
2.3. Existing Application of RSSI in Technology Recognition
2.4. Contributions
3. Multi-Modal Distribution of RSSI
3.1. Discontinuous Transmission
3.2. Variation in Carrier Allocation
Experiment
3.3. Summary
4. Characterization of Real-Life Signals
4.1. Signal Selection
- Wi-Fi (IEEE 802.11a): signal transmitted in random bursts, modulated with a constant amount of carriers;
- LTE: signal transmitted in very fine and regular intervals, modulated with a variable amount of carriers;
- DVB-T: signal transmitted continuously and modulated with a constant amount of carriers.
4.2. Experiments
4.2.1. Experiment with the Spectrum Analyzer
4.2.2. Experiment with Small-Scale RF Devices
4.3. Feature Space Design
4.3.1. Features from the RSSI Distribution
- : the standard deviation of the RSSI vector . It indicates the range of variation in the signal strength.
- : the number of peaks in the histogram of RSSI is a simple way to describe the shape of the distribution. A point in the histogram is recognized as a peak when it is above its two neighboring points.
- : the average power level of the noise, which corresponds to the location of the leftmost peak in the histogram, and it should be situated to the left of a certain threshold, denoted as .
- : the probability that the measured RSSI is equal to the average noise power level , i.e., . This is the amplitude of the noise peak in the RSSI histogram, which is proportional to the amount of time the signal is interrupted. It is identified when the peak corresponding to is above .
4.3.2. Features from RSSI Time Series
5. Automatic Signal Recognition
5.1. Sample Algorithm
- is the upper bound of the average noise level, obtained by the maximum of in the collected Wi-Fi traces plus the standard deviation of .
- determines the minimal amount of noise present in the Wi-Fi’s RSSI measurements. It is calculated by the smallest ‘noise peak’ minus the standard deviation of the noise peaks in the Wi-Fi’s RSSI measurements.
- - denotes the minimum standard deviation among the collected RSSI measurements of Wi-Fi, which is used to differentiate Wi-Fi from noise.
- - denotes the medium of the minimum and the maximum standard deviation of LTE and DVB-T’s RSSI measurements. It is used to differentiate LTE and DVB-T signal.
- - denotes the maximum number of peaks in the histograms of the RSSI measurements of DVB-T. It is used to exclude unknown signals from the DVB-T signals.
Algorithm 1 RSSI distribution-based technology recognition. |
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5.2. Validation
5.2.1. Analysis of N = 20, T = 1 s
5.2.2. Analysis of Variable N
5.2.3. Analysis of Variable T
5.2.4. Analysis of Practicality
5.3. Extended Validation for Mixed Signals
5.3.1. Dataset Extension for Mixed LTE-U and Wi-Fi Signals
5.3.2. Algorithm and Feature Space Extension
- : the standard deviation of the locations (indicated by grey flashes in Figure 8 and represented by vector ), where the histogram elements located to the right side of are equal to zero. It indicates the amount of discontinuities in the RSSI histogram.
- : the amplitude of the highest peak in the histogram apart from the noise peak.
Algorithm 2 Extension to distinguish Wi-Fi from MIX_LTE_Wi-Fi. |
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5.3.3. Result Analysis
6. Conclusions and Future Work
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Park, C.; Rappaport, T.S. Short-range wireless communications for next-generation networks: UWB, 60 GHz millimeter-wave WPAN, and ZigBee. Wirel. Commun. IEEE 2007, 14, 70–78. [Google Scholar] [CrossRef]
- Larsson, E.; Edfors, O.; Tufvesson, F.; Marzetta, T. Massive MIMO for next generation wireless systems. Commun. Mag. IEEE 2014, 52, 186–195. [Google Scholar] [CrossRef]
- Song, M.; Xin, C.; Zhao, Y.; Cheng, X. Dynamic spectrum access: From cognitive radio to network radio. Wirel. Commun. IEEE 2012, 19, 23–29. [Google Scholar] [CrossRef]
- Berlemann, L.; Mangold, S. Cognitive Radio and Dynamic Spectrum Access; Wiley Online Library: Hoboken, NJ, USA, 2009. [Google Scholar]
- Kruys, J. Co-Existence of Dissimilar Wireless Systems; Cisco Systems Report; Cisco: San Jose, CA, USA, 2003. [Google Scholar]
- Zhang, R.; Wang, M.; Cai, L.X.; Zheng, Z.; Shen, X. LTE-unlicensed: The future of spectrum aggregation for cellular networks. Wirel. Commun. IEEE 2015, 22, 150–159. [Google Scholar] [CrossRef]
- Kozal, A.S.; Merabti, M.; Bouhafs, F. An improved energy detection scheme for cognitive radio networks in low SNR region. In Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Cappadocia, Turkey, 1–4 July 2012; pp. 684–689. [Google Scholar]
- Kim, K.; Xin, Y.; Rangarajan, S. Energy detection based spectrum sensing for cognitive radio: An experimental study. In Proceedings of the Global Telecommunications Conference (GLOBECOM 2010), Miami, FL, USA, 6–10 December 2010; pp. 1–5. [Google Scholar]
- Chin, W.L.; Li, J.M.; Chen, H.H. Low-complexity energy detection for spectrum sensing with random arrivals of primary users. IEEE Trans. Veh. Technol. 2016, 65, 947–952. [Google Scholar] [CrossRef]
- Bhowmick, A.; Chandra, A.; Roy, S.D.; Kundu, S. Double threshold-based cooperative spectrum sensing for a cognitive radio network with improved energy detectors. IET Commun. 2015, 9, 2216–2226. [Google Scholar] [CrossRef]
- Zhang, H.; Chu, X.; Guo, W.; Wang, S. Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum. Commun. Mag. IEEE 2015, 53, 158–164. [Google Scholar] [CrossRef]
- Mansouri, W.; Mnif, K.; Zarai, F.; Obaidat, M.S.; Kamoun, L. A new multi-rat scheduling algorithm for heterogeneous wireless networks. J. Syst. Softw. 2016, 115, 174–184. [Google Scholar] [CrossRef]
- Galinina, O.; Pyattaev, A.; Andreev, S.; Dohler, M.; Koucheryavy, Y. 5G multi-RAT LTE-WiFi ultra-dense small cells: Performance dynamics, architecture, and trends. IEEE J. Sel. Areas Commun. 2015, 33, 1224–1240. [Google Scholar] [CrossRef]
- Bormann, C.; Ersue, M.; Keranen, A. Terminology for Constrained-Node Networks; Internet Engineering Task Force (IETF): Fremont, CA, USA, 2014. [Google Scholar]
- Akyildiz, I.F.; Lee, W.Y.; Vuran, M.C.; Mohanty, S. A survey on spectrum management in cognitive radio networks. Commun. Mag. IEEE 2008, 46, 40–48. [Google Scholar] [CrossRef]
- Arslan, H. Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems; Springer: New York, NY, USA, 2007; Volume 10. [Google Scholar]
- Gardner, W.A. Signal interception: A unifying theoretical framework for feature detection. IEEE Trans. Commun. 1988, 36, 897–906. [Google Scholar] [CrossRef]
- Zheleva, M.; Chandra, R.; Chowdhery, A.; Kapoor, A.; Garnett, P. TxMiner: Identifying transmitters in real-world spectrum measurements. In Proceedings of the IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Stockholm, Sweden, 29 Sertember–2 October 2015; pp. 94–105. [Google Scholar]
- Mazuelas, S.; Bahillo, A.; Lorenzo, R.M.; Fernandez, P.; Lago, F.A.; Garcia, E.; Blas, J.; Abril, E.J. Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks. IEEE J. Sel. Top. Signal Process. 2009, 3, 821–831. [Google Scholar] [CrossRef]
- Zhuang, Y.; Syed, Z.; Li, Y.; El-Sheimy, N. Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation. IEEE Trans. Mob. Comput. 2016, 15, 1982–1995. [Google Scholar] [CrossRef]
- Zhuang, Y.; Syed, Z.; Georgy, J.; El-Sheimy, N. Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing. Pervasive Mob. Comput. 2015, 18, 118–136. [Google Scholar] [CrossRef]
- Zhuang, Y.; Yang, J.; Li, Y.; Qi, L.; El-Sheimy, N. Smartphone-based indoor localization with bluetooth low energy beacons. Sensors 2016, 16, 596. [Google Scholar] [CrossRef] [PubMed]
- Haeberlen, A.; Flannery, E.; Ladd, A.M.; Rudys, A.; Wallach, D.S.; Kavraki, L.E. Practical robust localization over large-scale 802.11 wireless networks. In Proceedings of the 10th Annual International Conference on Mobile Computing and Networking, Philadelohia, PA, USA, 26 September–1 October 2004; pp. 70–84. [Google Scholar]
- Kaemarungsi, K. Distribution of WLAN received signal strength indication for indoor location determination. In Proceedings of the 2006 1st International Symposium on Wireless Pervasive Computing, Phuket, Thailand, 16–18 January 2006; p. 6. [Google Scholar]
- Luo, J.; Zhan, X. Characterization of smart phone received signal strength indication for WLAN indoor positioning accuracy improvement. J. Netw. 2014, 9, 739–746. [Google Scholar] [CrossRef]
- Chapre, Y.; Mohapatra, P.; Jha, S.; Seneviratne, A. Received signal strength indicator and its analysis in a typical WLAN system (short paper). In Proceedings of the IEEE 38th Conference on Local Computer Networks (LCN), Sydney, Australia, 21–24 October 2013; pp. 304–307. [Google Scholar]
- Rajab, S.A.; Balid, W.; Al Kalaa, M.O.; Refai, H.H. Energy detection and machine learning for the identification of wireless MAC technologies. In Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC), Dubrovnik, Croatia, 24–28 August 2015; pp. 1440–1446. [Google Scholar]
- Hu, S.; Yao, Y.D.; Yang, Z. MAC protocol identification using support vector machines for cognitive radio networks. IEEE Wirel. Commun. 2014, 21, 52–60. [Google Scholar] [CrossRef]
- Zheng, X.; Cao, Z.; Wang, J.; He, Y.; Liu, Y. ZiSense: Towards interference resilient duty cycling in wireless sensor networks. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, Memphis, TN, USA, 3–6 November 2014; pp. 119–133. [Google Scholar]
- Hou, M.; Ren, F.; Lin, C.; Miao, M. HEIR: Heterogeneous interference recognition for wireless sensor networks. In Proceedings of the IEEE 15th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Boston, MA, USA, 14–17 June 2014; pp. 1–9. [Google Scholar]
- Rayanchu, S.; Patro, A.; Banerjee, S. Airshark: Detecting non-WiFi RF devices using commodity WiFi hardware. In Proceedings of the ACM SIGCOMM Conference on Internet Measurement Conference, Berlin, Germany, 2–4 November 2011; pp. 137–154. [Google Scholar]
- Ettus Research. USRP. Available online: https://www.ettus.com/ (accessed on 11 September 2017).
- GNURadio. Available online: https://www.gnuradio.org/ (accessed on 11 September 2017).
- Kim, J.H.; Lee, J.K. Performance of carrier sense multiple access with collision avoidance protocols in wireless LANs. Wirel. Pers. Commun. 1999, 11, 161–183. [Google Scholar] [CrossRef]
- Myung, H.G. Technical Overview of 3GPP LTE; Polytechnic University of New York: Brooklyn, NY, USA, 2008. [Google Scholar]
- Anritsu MS2690A Analyzer. Available online: https://www.anritsu.com/en-us/test-measurement/products/ms2690a (accessed on 11 September 2017).
- Liu, W.; De Poorter, E.; Hoebeke, J.; Tanghe, E.; Joseph, W.; Willemen, P.; Mehari, M.; Jiao, X.; Moerman, I. Assessing the Coexistence of Heterogeneous Wireless Technologies With an SDR-Based Signal Emulator: A Case Study of Wi-Fi and Bluetooth. IEEE Trans. Wirel. Commun. 2017, 16, 1755–1766. [Google Scholar] [CrossRef]
- Bluetooth, S. Specification of the Bluetooth System Version 3.0+ HS—Core System Package (Vol. 2)—Radio Specification (Part A); Bluetooth: Kirkland, WA, USA, 2009. [Google Scholar]
- 3rd Generation Partnership Project (3GPP). Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation; European Telecommunications Standards Institute: Sophia Antipolis, France, 2012; p. 315. [Google Scholar]
Tech | Wi-Fi | LTE | DVB-T | |
---|---|---|---|---|
Features | ||||
Variable | High | Low | ||
Variable | High | Low | ||
High | Not visible | Not present |
Prediction | Wi-Fi | LTE | DVB-T | Noise | Unknown | |
---|---|---|---|---|---|---|
Actual | ||||||
Wi-Fi | 92.6% | 1.85 % | 0% | 3.70% | 1.85% | |
LTE | 0% | 100% | 0% | 0% | 0% | |
DVB-T | 0% | 1.85% | 98.15% | 0% | 0% | |
Noise | 0% | 0% | 0% | 0% | 0% | |
Unknown | 0% | 0% | 0% | 0% | 0% |
Predicted | Wi-Fi | LTE | DVB-T | Mixed LTE Wi-Fi | |
---|---|---|---|---|---|
Actual | |||||
Wi-Fi | 94.4% | 0 % | 0% | 5.60% | |
LTE | 0% | 100% | 0% | 0% | |
DVB-T | 0% | 1.85% | 98.15% | 0% | |
Mixed LTE Wi-Fi | 0% | 1.85% | 0% | 98.15% |
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Liu, W.; Kulin, M.; Kazaz, T.; Shahid, A.; Moerman, I.; De Poorter, E. Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices. Sensors 2017, 17, 2081. https://doi.org/10.3390/s17092081
Liu W, Kulin M, Kazaz T, Shahid A, Moerman I, De Poorter E. Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices. Sensors. 2017; 17(9):2081. https://doi.org/10.3390/s17092081
Chicago/Turabian StyleLiu, Wei, Merima Kulin, Tarik Kazaz, Adnan Shahid, Ingrid Moerman, and Eli De Poorter. 2017. "Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices" Sensors 17, no. 9: 2081. https://doi.org/10.3390/s17092081
APA StyleLiu, W., Kulin, M., Kazaz, T., Shahid, A., Moerman, I., & De Poorter, E. (2017). Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices. Sensors, 17(9), 2081. https://doi.org/10.3390/s17092081