الانتشار اللاسلكي متعدد المسيرات باستخدام التجميع الطيفي و ثلاثة مصفوفة القيد الطيفية العنقودية
محتوى المقالة الرئيسي
الملخص
ركزت هذه الدراسة على التجميع الطيفي (SC) والتجمع الطيفي لمصفوفة التقارب ثلاثي القيود (3CAM-SC) لتحديد عدد المجموعات وعضوية مجموعات نموذج قناة COST 2100 (C2CM) متعدد المسارات في وقت واحد. تعمل مناهج التجميع متعدد المسارات المختلفة على حل عدد المجموعات فقط دون مراعاة عضوية المجموعات. تكمن مشكلة إعطاء عدد المجموعات فقط في عدم وجود ضمان بأن عضوية المجموعات متعددة المسارات دقيقة على الرغم من أن عدد المجموعات صحيح. تهدف SC و 3CAM-SC إلى حل هذه المشكلة من خلال تحديد عضوية المجموعات. ثم تم حساب الكتلة والتعداد العنقودي من خلال مؤشر Jaccard العنقودي لعضوية المسارات المتعددة في مجموعاتهم. تم تحويل المسارات المتعددة الناتجة عن C2CM باستخدام تحويل جيب التمام الاتجاهي (DCT) ومحول التبييض (WT). تم تجميع مجموعة البيانات المحولة باستخدام SC و 3CAM-SC. تم التحقق من صحة أداء التجميع باستخدام مؤشر Jaccard من خلال مقارنة مجموعة البيانات المرجعية متعددة المسيرات مع المجموعات متعددة المسارات المحسوبة. تظهر النتائج أن فعالية SC مماثلة لنهج التجميع الحديث. ومع ذلك ، يتفوق 3CAM-SC على SC في جميع سيناريوهات القناة. يمكن استخدام SC في السيناريوهات الداخلية بناءً على الدقة ، بينما 3CAM-SC قابلة للتطبيق في السيناريوهات الداخلية وشبه الحضرية. وبالتالي ، يمكن تطبيق نهج التجميع كتقنيات تجميع بديلة في مجال نمذجة القناة.
تفاصيل المقالة
هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.
كيفية الاقتباس
المراجع
Suresh G, Balasubramanian R. An ensemble feature selection model using fast convergence ant colony optimization algorithm. International Journal of Emerging Trends in Engineering Research. 2020;8(4),1417-1423. https://doi.org/10.30534/ijeter/2020/77842020
Pavithra G, Abirami P, Bhuvaneshwari S, Dharani S, Haridharani B. A survey on intrusion detection mechanism using machine learning algorithms. International Journal of Emerging Trends in Engineering Research. 2020;8(4), 945-949. https://doi.org/10.30534/ijeter/2020/01842020
Ibrahim S, Rozan M, Sabri N. Comparative analysis of support vector machine (SVM) and convolutional neural network (CNN) for white blood cells’ classification. International Journal of Advanced Trends in Computer Science and Engineering. 2019;8(1.3),394-399. https://doi.org/10.30534/ijatcse/2019/6981.32019
Chandirika B, Sakthivel NK, Subasree S. An energy efficient k-means clustering based trust model for wireless sensor networks. International Journal of Advanced Trends in Computer Science and Engineering. 2019;8(2),144-153. https://doi.org/10.30534/ijatcse/2019/08822019
Blanza, J, Materum L, Hirano T. Deep divergence-based clustering of wireless multipaths for simultaneously addressing the grouping and cardinality. International Journal of Emerging Trends in Engineering Research. 2020;8(7),3104-3110. https://doi.org/10.30534/ijeter/2020/37872020
Blanza, J, Materum L. Joint identification of the clustering and cardinality of wireless propagation multipaths. International Journal of Emerging Trends in Engineering Research. 2019;7(12),762-767. https://doi.org/10.30534/ijeter/2019/057122019
Foschini G, Gans M. On limits of wireless communications in a fading
environment when using multiple antennas. Wireless Personal Communications. 1998;6,311-
https://doi.org/10.1023/A:1008889222784
Verdone R, Zanella A. Pervasive Mobile and Ambient Wireless Communications: COST Action 2100, Signals and Communication Technology. Springer. 2012
Czink N, Cera P, Salo J, Bonek E, Nuutinen JP, Ylitalo J. A framework for automatic clustering of parametric MIMO channel data including path powers. IEEE 64th Vehicular Technology Conference. 2006;1-5. https://doi.org/10.1109/VTCF.2006.35
Gentile C. Using the kurtosis measure to identify clusters in wireless channel impulse responses. IEEE Transactions on Antennas and Propagation. 2013;61(6),3392-3395. https://doi.org/10.1109/TAP.2013.2253299
He R, Li Q, Ai B, Geng Y, Molisch A, Kristem V, Zhong Z, Yu J. A kernel-power-density-based algorithm for channel multipath components clustering using the kurtosis measure to identify clusters in wireless channel impulse responses. IEEE Transactions on Wireless Communications. 2017;16(11),7138-7151. https://doi.org/10.1109/TWC.2017.2740206
Li Y, Zhang J, Ma Z, Zhang Y. Clustering analysis in the wireless propagation channel with a variational Gaussian mixture model. IEEE Transactions on Big Data. 2020;6(2),223-232. https://doi.org/10.1109/TBDATA.2018.2840696
Blanza J, Teologo, A, Materum L. Datasets for multipath clustering at 285 MHz and 5.3 GHz bands based on COST 2100 MIMO channel model. 2019 International Symposium on Multimedia and Communication Technology. 2019;1-5. https://doi.org/10.1109/ISMAC.2019.8836143
Li Z, Cheong L, Yang S, Toh K. Simultaneous clustering and model selection: algorithm, theory and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(8),1964-1978. https://doi.org/10.1109/TPAMI.2017.2739147
Kampffmeyer M, Løkse S, Bianchi F, Livi L, Salberg A, Jenssen R. Deep divergence-based approach to clustering. Neural Network. 2019;113,91-101. https://doi.org/10.1016/j.neunet.2019.01.015
Chowdhury M, Biswas A. Wireless Communication: Theory and Applications. Cambridge University Press. 2017.
Molisch A. Wireless Communications. Wiley-IEEE. 2012.
3GPP. Study on channel model for frequencies from 0.5 to 100 GHz. 3GPP 2059 TR 38.901 V16.0.0. Technical report. 2019.
Saleh A, Valenzuela R. A statistical model for indoor multipath propagation. IEEE Journal on Selected Areas in Communications. 1987;5(2),128-137. https://doi.org/10.1109/JSAC.1987.1146527
Molisch A, Cassioli D, Chong C-C, Emami S, Fort A, Kannan B, Karedal J, Kunisch J, Schantz H G, Siwiak K, et al. A comprehensive standardized model for ultrawideband propagation channels. IEEE Transactions on Antennas and Propagation. 2006;54(11),3151-3166. https://doi.org/10.1109/TAP.2006.883983
Meinila J, Kyosti P, Jamsa T, Hentila L. WINNER II channel models. Radio Technologies and Concepts for IMT-Advanced. 2009;39-92. https://doi.org/10.1002/9780470748077.ch3
Molisch A. Modeling the MIMO propagation channel. REVUE HF. 2003;2209(4),5-14.
Chong C-C, Tan C-M, Laurenson DI, McLaughlin S, Beach MA, Nix AR. A new statistical wideband spatio-temporal channel model for 5-GHz band WLAN systems. IEEE Journal on Selected Areas in Communications. 2003;21(2),139-150. https://doi.org/10.1109/JSAC.2002.807347
Ghassemzadeh SS, Greenstein LJ, Sveinsso T, Kavcic A, Tarokh V. UWB delay profile models for residential and commercial indoor environments. IEEE Transactions on Vehicular Technology. 2005;54(4),1235-1244. https://doi.org/10.1109/TVT.2005.851379
Karedal J, Wyne S, Almers P, Tufvesson F, Molisch A. Statistical analysis of the UWB channel in an industrial environment. IEEE 60th Vehicular Technology Conference. 2004;1,81–85. https://doi.org/10.1109/VETECF.2004.1399930
Corrigan M, Walton A, Niu W, Li J, Talty T. Automatic UWB clusters identification. IEEE Radio and Wireless Symposium. 2009;376-379. https://doi.org/10.1109/RWS.2009.4957359
Mlinarsky F. Throughput test methods for MIMO radios. Octoscope. 2014.
Li B, Zhao C, Zhang H, Zhou Z, Nallanathan A. Efficient and robust cluster identification for ultra-wideband propagations inspired by biological ant colony clustering. IEEE Transactions on Communications. 2015;63(1),286-300. https://doi.org/10.1109/TCOMM.2014.2377120
Cheng S, Martinez-Ingles M-T, Gaillot DP, Molina-Garcia-Pardo J-M, Lienard M, Degauque P. Performance of a novel automatic identification algorithm for the clustering of radio channel parameters. IEEE Access. 2015,3,2252-2259. https://doi.org/10.1109/ACCESS.2015.2497970
He R, Chen W, Ai B, Molisch A, Wang W, Zhong Z, Yu J, Sangodoyin A. On the clustering of radio channel impulse responses using sparsity based methods. IEEE Transactions on Antennas and Propagation. 2016;64(6),2465-2474. https://doi.org/10.1109/TAP.2016.2546953
Hanpinitsak P, Saito K, Takada J-i, Kim M, Materum L. Multipath clustering and cluster tracking for geometry-based stochastic channel modeling. IEEE Transactions on Antennas and Propagation. 2017;65(11),6015-6028. https://doi.org/10.1109/TAP.2017.2754417
COST 2100 channel model. 2018. Retrieved from http://github.com/cost2100/cost2100/tree/master/matlab
Poutanen J, Haneda K, Liu L, Oestges C, Tufvesson F, Vainikainen P. Parameterization of the COST 2100 MIMO channel model in indoor scenarios. Proceedings of the 5th European Conference on Antennas and Propagation. 2011; 3606-3610.
Zhu M, Eriksson G, Tufvesson F. The COST 2100 channel model: parameterization and validation based on outdoor MIMO measurements at 300 MHz. IEEE Transactions on Wireless Communications. 2013;12(2),888-897. https://doi.org/10.1109/TWC.2013.010413.120620
Xu R, Wunsch D. Survey of clustering algorithms. IEEE Transactions on Neural Networks. 2005;16(3),645-678. https://doi.org/10.1109/TNN.2005.845141
Steinbauer M, Molisch A, Bonek E. The double directional radio channel. IEEE Antennas and Propagation Magazine. 2001;43(4),51-63. https://doi.org/10.1109/74.951559
RCore. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2015.
Maechler M. Diptest: Hartigan’s dip test statistic for unimodality corrected. R package version 0.75-7. 2015.
Ng A, Weiss Y, Jordan M. On spectral clustering analysis and an algorithm. Proceedings of Neural Information Processing Systems Conference. 2001;849-856.
Elhamifar E, Vidal R Sparse subspace clustering: algorithm, theory, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001;35(11),2765-
https://doi.org/10.1109/TPAMI.2013.57
Blanza J. Identification of wireless propagation multipath lusters and their cardinality using simultaneous clustering and model selection matrix affinity and deep divergence-based clustering approaches.[Doctoral Dissertation]. De La Salle University; 2002.