On the use of Hidden Markov Processes and auto-regressive filters to incorporate indoor bursty wireless channels into network simulation platforms

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• 作者：David Gómez ; Ramón Agüero ; Marta García-Arranz ; Luis Mu?oz
• 关键词：Simulation ; Wireless channel models ; Hidden Markov Processes ; Bursty behavior ; ns ; 3
• 刊名：Wireless Networks
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：21
• 期：7
• 页码：2137-2154
• 全文大小：1,555 KB
• 参考文献：1.The ns-3 network simulator, http://?www.?nsnam.?org/-/span> r>2.Wang, W., Liew, S. C., & Li, V. (2005). Solutions to performance problems in VoIP over a 802.11 wireless LAN. IEEE Transactions on Vehicular Technology, 54(1), 366-84.CrossRef r>3.Agüero, R., García-Arranz, M., & Mu?oz, L. (2010). Accurate simulation of 802.11 indoor links: A bursty channel model based on real measurements. EURASIP Journal on Wireless Communications and Networking, 2010, 16:1-6:12.CrossRef r>4.Baum, L. E. (1972). An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. In O. Shisha (Ed.), Inequalities III: Proceedings of the Third Symposium on Inequalities (pp. 1-). University of California, Los Angeles: Academic Press.r>5.Cardoso, K., & De Rezende, J. (2009). Accurate hidden markov modeling of packet losses in indoor 802.11 networks. IEEE Communications Letters, 13(6), 417-19.CrossRef r>6.Gómez, D., Agüero, R., García-Arranz, M., & noz, L.M. (2013). Replication of the bursty behavior of indoor WLAN channels. In Workshop on NS3 (WNS3), Cannes, France, [Online]. http://?hal.?inria.?fr/?hal-00781591 r>7.Lacage, M., & Henderson, T. R. (2006). Yet another network simulator. In Proceeding from the 2006 Workshop on ns-2: The IP Network Simulator, ser. WNS2 -6. New York, NY, USA: ACM, 2006. [Online]. doi:10.-145/-190455.-190467 r>8.Pei, G., & Henderson, T. (2009). Validation of ns-3 802.11b PHY model, May 2009, http://?www.?nsnam.?org/?~pei/-0211b.?pdf r>9.Vlavianos, A., Law, L., Broustis, I., Krishnamurthy, S., & Faloutsos, M. (2008). Assessing link quality in IEEE 802.11 wireless networks: Which is the right metric? In personal, indoor and mobile radio communications, 2008. PIMRC 2008. IEEE 19th International Symposium on, Sept. 2008, pp. 1-.r>10.Lertpratchya, D., Riley, G.F., & Blough, D.M. (2014). Simulating frame-level bursty links in wireless networks. In Proceedings of the 7th International Conference on Simulation Tools and Techniques, SIMUTOOLS-4, March.r>11.Eckhardt, D., & Steenkiste, P. (1996). Measurement and analysis of the error characteristics of an in-building wireless network. SIGCOMM Computer communication review, 26(4), 243-54. doi:10.-145/-48157.-48178 .CrossRef r>12.Nguyen, G., Noble, B., Katz, R., & Satyanarayanan, M. (1996). A trace-based approach for modeling wireless channel behavior. In Simulation Conference, Proceedings Winter, 597-04.r>13.Ikkurthy, P., & Labrador, M. (2002). Characterization of MPEG-4 traffic over IEEE 802.11b wireless LANs, in local computer networks, 2002. Proceedings. LCN 2002. 27th Annual IEEE Conference on, Nov. pp. 421-27.r>14.Convertino, G., Oliva, S., Sigona, F., & Anchora, L. (2006). An adaptive FEC scheme to reduce bursty Losses in a 802.11 network. In IEEE Global Telecommunications Conference, 2006. GLOBECOM -6, 27 2006-Dec. 1 pp. 1-.r>15.Gandikota, V. R., Tamma, B. R., & Murthy, C. S. R. (2008). Adaptive FEC-based packet loss resilience scheme for supporting voice communication over Ad hoc wireless networks. IEEE Transactions on Mobile Computing, 7(10), 1184-199. doi:10.-109/?TMC.-008.-2 .CrossRef r>16.Barsocchi, P., Oligeri, G., & Potorti, F. (2009). Measurement-based frame error model for simulating outdoor Wi-Fi networks. IEEE Transactions on Wireless Communications, 8(3), 1154-158.CrossRef r>17.Turin, W., & Van Nobelen, R. (1998). Hidden markov modeling of flat fading channels. Selected Areas in Communications, IEEE Journal on, 16(9), 1809-817.CrossRef r>18.Zhu, W., & García-Frías, J. (2004). Stochastic context-free grammars and hidden markov models for modeling of bursty channels. Vehicular Technology, IEEE Transactions on, 53(3), 666-76.CrossRef r>19.Papanastasiou, S., Mittag, J., Strom, E., & Hartenstein, H. (2010). Bridging the gap between physical layer emulation and network simulation. In IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-.r>20.Al-Bado, M., Sengul, C., & Merz, R. (2012). What details are needed for wireless simulations?—A study of a site-specific indoor wireless model. In INFOCOM. IEEE, pp. 289-97.r>21.802.11b interference modeling in NS-3 simulator, http://?www.?ee.?washington.?edu/?research/?funlab/-02_-1_?b_?intf_?model/?index.?html r>22.Gómez, D., & Agüero, R. GitHub’s repository for HMP and BEAR IEEE 802.11b indoor channel models (source code and documentation), https://?github.?com/?dgomezunican/?ns3-wifi-memory-channel r>
• 作者单位：David Gómez (1) r> Ramón Agüero (1) r> Marta García-Arranz (1) r> Luis Mu?oz (1) r>r>1. Communications Engineering Department, Edificio José Luis García García, Plaza de la Ciencia, Avda. de Los Castros S/N, Santander, Cantabria, Spain r>
• 刊物类别：Computer Science
• 刊物主题：Computer Communication Networksr>Electronic and Computer Engineeringr>Business Information Systemsr>
• 出版者：Springer Netherlands
• ISSN：1572-8196

文摘

In this paper we thoroughly analyze two alternatives to replicate the bursty behavior that characterizes real indoor wireless channels within Network Simulation platforms. First, we study the performance of an improved Hidden Markov Process model, based on a time-wise configuration so as to decouple its operation from any particular traffic pattern. We compare it with the behavior of Bursty Error Model Based on an Auto-Regressive Filter, a previous proposal of ours that emulates the received Signal to Noise Ratio by means of an auto-regressive filter that captures the “memory-assessed in real measurements. We also study the performance of one of the legacy approaches intrinsically offered by most network simulation frameworks. By means of a thorough simulation campaign, we demonstrate that our two models are able to offer a much more realistic behavior, yet maintaining an affordable response in terms of computational complexity. Keywords Simulation Wireless channel models Hidden Markov Processes Bursty behavior ns-3