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A Survey on Long Term Evolution Scheduling in Data Mining

  • Divya Mohan
  • Geetha Mary Amalanathan
Article
  • 26 Downloads

Abstract

Scheduling procedures implemented in wireless networks consists of varied workflows such as resource allocation, channel gain improvement, and reduction in packet arrival delay. Among these techniques, Long term evolution (LTE) scheduling is most preferable due to its high speed communication and low bandwidth consumption. LTE allocates resources to the workflow based on time and frequency domains. Normally, the information gathered prior to scheduling increases the processing time since each attributes of the users have to be verified. In order to solve this issue, parallel processing via data mining is analyzed in recent research studies. The label that is assigned to the user attributes contributes primarily on scheduling time slots effectively. The label assignment and parallel processing via data mining reduces the delay and increases the throughput respectively. Additionally, the matched data extraction from the library and the prediction of available channels with fewer dimensions posed major challenges in the LTE scheduling. This paper surveys about various LTE scheduling algorithms, dimensionality reduction techniques, optimal feature selection techniques, multi-level classification techniques, and data mining combined with LTE techniques. A brief survey illustrates the impact of each technique on 3G/4G networks, channel availability prediction, scheduling of time slots in detail. A brief comparison of the techniques involved in the respective LTE processes via tabular form reveals that the verification of channel and user availability are the primary functions of the LTE scheduling. The survey of this paper identifies the limitations such as computational complexity and poor scheduling performance in the existing systems and encourages researchers to develop novel algorithms for LTE scheduling.

Keywords

Long term evolution (LTE) Data mining Quality of service LTE scheduling Dimensionality reduction Optimal feature selection Classification LTE in data mining Self-organizing networks 

Notes

REFERENCES

  1. 1.
    Adeniyi, D., Wei, Z., & Yongquan, Y. (2016). Automated web usage data mining and recommendation system using k-nearest neighbor (KNN) classification method. Applied Computing and Informatics, 12(1), 90–108.CrossRefGoogle Scholar
  2. 2.
    Albataineh, Z., & Salem, F. M. (2017). Adaptive blind CDMA receivers based on ICA filtered structures. Circuits, Systems, and Signal Processing, 36(8), 3320–3348.CrossRefGoogle Scholar
  3. 3.
    Alfayly, A., Mkwawa, I.-H., Sun, L., & Ifeachor, E. (2012). QoE-based performance evaluation of scheduling algorithms over LTE. In IEEE globecom workshops, IEEE (pp. 1362–1366).Google Scholar
  4. 4.
    Alguliev, R. M., Aliguliyev, R. M., & Nazirova, S. A. (2011). Classification of textual e-mail spam using data mining techniques. Applied Computational Intelligence and Soft Computing, 2011, 10.Google Scholar
  5. 5.
    AlQahtani, S. A., & Alhassany, M. (2013 ). Comparing different LTE scheduling schemes. In 2013 9th international wireless communications and mobile computing conference (IWCMC), IEEE (pp. 264–269).Google Scholar
  6. 6.
    Ameigeiras, P., Navarro-Ortiz, J., Andres-Maldonado, P., Lopez-Soler, J. M., Lorca, J., Perez-Tarrero, Q., et al. (2016). 3GPP QoS-based scheduling framework for LTE. EURASIP Journal on Wireless Communications and Networking, 2016(1), 1–14.CrossRefGoogle Scholar
  7. 7.
    Bao, Y., Wu, H., & Liu, X. (2017). From prediction to action: Improving user experience with data-driven resource allocation. IEEE Journal on Selected Areas in Communications, 35(5), 1062–1075.CrossRefGoogle Scholar
  8. 8.
    Batina, L., Hogenboom, J., & van Woudenberg, J. G. (2012). Getting more from PCA: First results of using principal component analysis for extensive power analysis. In Cryptographers’ track at the RSA conference (pp. 383–397). Springer.Google Scholar
  9. 9.
    Beckmann, C. F. (2012). Modelling with independent components. Neuroimage, 62(2), 891–901.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Bertrand, B. A. P., Moustapha, D., Etienne, S., Souleymane, O., & Matthieu, A. (2016). A genetic algorithm for overall designing and planning of a long term evolution advanced network. American Journal of Operations Research, 6(04), 355.CrossRefGoogle Scholar
  11. 11.
    Bilbao Marón, M. N. (2014). Advanced meta-heuristic approaches and their application to operational optimization in forest wildfire management, Ph.D Thesis. Madrid: University of Alcala.Google Scholar
  12. 12.
    Biral, A., Centenaro, M., Zanella, A., Vangelista, L., & Zorzi, M. (2015). The challenges of M2M massive access in wireless cellular networks. Digital Communications and Networks, 1(1), 1–19.CrossRefGoogle Scholar
  13. 13.
    Challita, U., Dong, L., & Saad, W. (2017). Deep learning for proactive resource allocation in LTE-U networks. In European wireless technology conference. Google Scholar
  14. 14.
    Chen, Y.-C. (2015). Robust mobile data transport: Modeling, measurements, and implementation, Ph.D. Thesis. Amherst: University of Massachusetts.Google Scholar
  15. 15.
    Chen, Y., Miao, D., Wang, R., & Wu, K. (2011). A rough set approach to feature selection based on power set tree. Knowledge-Based Systems, 24(2), 275–281.CrossRefGoogle Scholar
  16. 16.
    Chernov, S., Chernogorov, F., Petrov, D., & Ristaniemi, T. (2014). Data mining framework for random access failure detection in LTE networks. In IEEE 25th annual international symposium on personal, indoor, and mobile radio communication (PIMRC), IEEE (pp. 1321–1326).Google Scholar
  17. 17.
    Chidhambararajan, K S Ba B. (2016). Fast and secure intelligence re-authentication m echanism for next generation subscribers. International Journal of Computer Technology and Applications, 9(9), 4175–4189.Google Scholar
  18. 18.
    Chisab, R. F., & Shukla, C. (2014). Performance evaluation Of 4G-LTE-SCFDMA scheme under SUI And ITU channel models. International Journal of Engineering & Technology IJET-IJENS , 14(1), 58–69.Google Scholar
  19. 19.
    Dechene, D. J., & Shami, A. (2014). Energy-aware resource allocation strategies for LTE uplink with synchronous HARQ constraints. IEEE Transactions on Mobile Computing, 13(2), 422–433.CrossRefGoogle Scholar
  20. 20.
    Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016). Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172, 371–381.CrossRefGoogle Scholar
  21. 21.
    Esser, E., Moller, M., Osher, S., Sapiro, G., & Xin, J. (2012). A convex model for nonnegative matrix factorization and dimensionality reduction on physical space. IEEE Transactions on Image Processing, 21(7), 3239–3252.MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., et al. (2017). State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Communications Surveys & Tutorials, 19(4), 2432–2455.CrossRefGoogle Scholar
  23. 23.
    Guangming, W., & Xiaoliang, W. (2009). Notice of retraction application of classification algorithm based on association rules in the labor market. In International conference on e-Learning, e-Business, enterprise information systems, and e-Government, EEEE’0, IEEE (pp. 255–258).Google Scholar
  24. 24.
    Gupta, A., Verma, T., Bali, S., & Kaul, S. (2013). Detecting MS initiated signaling DDoS attacks in 3G/4G wireless networks. In Fifth international conference on communication systems and networks (COMSNETS), IEEE (pp. 1–60).Google Scholar
  25. 25.
    Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513–529.CrossRefGoogle Scholar
  26. 26.
    Ingram, S., & Munzner, T. (2015). Dimensionality reduction for documents with nearest neighbor queries. Neurocomputing, 150, 557–569.CrossRefGoogle Scholar
  27. 27.
    Juvonen, A., & Sipola, T. (2012). Adaptive framework for network traffic classification using dimensionality reduction and clustering. In 2012 4th international congress on ultra modern telecommunications and control systems and workshops (ICUMT), IEEE (pp. 274–279).Google Scholar
  28. 28.
    Kabir, M. M., Shahjahan, M., & Murase, K. (2012). A new hybrid ant colony optimization algorithm for feature selection. Expert Systems with Applications, 39(3), 3747–3763.CrossRefGoogle Scholar
  29. 29.
    Kashef, S., & Nezamabadi-pour, H. (2015). An advanced ACO algorithm for feature subset selection. Neurocomputing, 147, 271–279.CrossRefGoogle Scholar
  30. 30.
    Khan, A. H., Qadeer, M. A., Ansari, J. A., & Waheed, S. (2009). 4G as a next generation wireless network. In International conference on future computer and communication, ICFCC 2009, IEEE (pp. 334–338).Google Scholar
  31. 31.
    Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017). A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Communications Surveys & Tutorials, 19(4), 2392–2431.CrossRefGoogle Scholar
  32. 32.
    Lai, W. K., & Tang, C.-L. (2013). QoS-aware downlink packet scheduling for LTE networks. Computer Networks, 57(7), 1689–1698.CrossRefGoogle Scholar
  33. 33.
    Lausch, A., Schmidt, A., & Tischendorf, L. (2015). Data mining and linked open data—New perspectives for data analysis in environmental research. Ecological Modelling, 295, 5–17.CrossRefGoogle Scholar
  34. 34.
    Lehtimäki, P., & Raivio, K. A (2005). SOM based approach for visualization of GSM network performance data. In International conference on industrial, engineering and other applications of applied intelligent systems (pp. 588–598). Springer.Google Scholar
  35. 35.
    Lei, L., You, L., Dai, G., Vu, T. X., Yuan, D., & Chatzinotas, S. (2017). A deep learning approach for optimizing content delivering in cache-enabled HetNet. In 2017 international symposium on wireless communication systems (ISWCS), IEEE (pp. 449–453).Google Scholar
  36. 36.
    Li, G. Y., Niu, J., Lee, D., Fan, J., & Fu, Y. (2014). Multi-cell coordinated scheduling and MIMO in LTE. IEEE Communications Surveys & Tutorials, 16(2), 761–775.CrossRefGoogle Scholar
  37. 37.
    Li, L., & Kianmehr, K. (2012). Internet traffic classification based on associative classifiers. In 2012 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER), IEEE (pp. 263–268).Google Scholar
  38. 38.
    Li, W., Prasad, S., Fowler, J. E., & Bruce, L. M. (2012). Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 50(4), 1185–1198.CrossRefGoogle Scholar
  39. 39.
    Lin, S.-W., Ying, K.-C., Lee, C.-Y., & Lee, Z.-J. (2012). An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Applied Soft Computing, 12(10), 3285–3290.CrossRefGoogle Scholar
  40. 40.
    Liu, L., Cheng, Y., Cai, L., Zhou, S., & Niu, Z. (2017). Deep learning based optimization in wireless network. In 2017 IEEE international conference on communications (ICC), IEEE (pp. 1–6).Google Scholar
  41. 41.
    Ma, Z., Teschendorff, A. E., Leijon, A., Qiao, Y., Zhang, H., & Guo, J. (2015). Variational bayesian matrix factorization for bounded support data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(4), 876–889.CrossRefGoogle Scholar
  42. 42.
    Ma, Z., Xie, J., Li, H., Sun, Q., Si, Z., Zhang, J., et al. (2017). The role of data analysis in the development of intelligent energy networks. IEEE Network, 31(5), 88–95.CrossRefGoogle Scholar
  43. 43.
    Ma, Z., Xue, J.-H., Leijon, A., Tan, Z.-H., Yang, Z., & Guo, J. (2016). Decorrelation of neutral vector variables: Theory and applications. IEEE Transactions on Neural Networks and Learning Systems, 29(1), 129–143.MathSciNetCrossRefGoogle Scholar
  44. 44.
    Mao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, O., Inoue, T., et al. (2017). Routing or computing? The paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Transactions on Computers, 66(11), 1946–1960.MathSciNetMATHCrossRefGoogle Scholar
  45. 45.
    Mercaldo, F., Visaggio, C. A., Canfora, G., & Cimitile, A. (2016). Mobile malware detection in the real world. In IEEE/ACM international conference on software engineering companion (ICSE-C), IEEE (pp. 744–746).Google Scholar
  46. 46.
    Moysen, J., Giupponi, L., & Mangues-Bafalluy, J. (2016). A machine learning enabled network planning tool. In 2016 IEEE 27th annual international symposium on personal, indoor, and mobile radio communications (PIMRC) (pp. 1–7).  https://doi.org/10.1109/pimrc.2016.7794909.
  47. 47.
    Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Coello, C. A. C. (2014). A survey of multiobjective evolutionary algorithms for data mining: Part I. IEEE Transactions on Evolutionary Computation, 18(1), 4–19.CrossRefGoogle Scholar
  48. 48.
    Nagpal, G., & Rajpoot, A. S. (2015). Scheduling exploiting frequency and multi-user diversity in LTE downlink systems with heterogeneous mobilities. MR International Journal of Engineering & Technology, 7(1), 29–40.Google Scholar
  49. 49.
    Nguyen, D., Memik, G., Memik, S. O., & Choudhary, A. (2015). Real-time feature extraction for high speed networks. In International conference on field programmable logic and applications, 2005, IEEE (pp. 438–443).Google Scholar
  50. 50.
    Niu, J., Lee, D., Ren, X., Li, G. Y., & Su, T. (2013). Scheduling exploiting frequency and multi-user diversity in LTE downlink systems. IEEE Transactions on Wireless Communications, 12(4), 1843–1849.CrossRefGoogle Scholar
  51. 51.
    Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2011). Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2), 199–210.CrossRefGoogle Scholar
  52. 52.
    Perez Rodriguez, J. S. (2016). Distributed cognitive rat selection in 5G heterogeneous networks: A machine learning approach, Ph.D. Thesis. Albuquerque, New Mexico: University of New Mexico.Google Scholar
  53. 53.
    Piro, G., Grieco, L. A., Boggia, G., Fortuna, R., & Camarda, P. (2011). Two-level downlink scheduling for real-time multimedia services in LTE networks. IEEE Transactions on Multimedia, 13(5), 1052–1065.CrossRefGoogle Scholar
  54. 54.
    Prabhu, V., & Nagaraja, G. (2014). Survey, classification and future direction for packet scheduling in 4G networks to provide quality of service. IJRCCT, 3(10), 1334–1339.Google Scholar
  55. 55.
    Prasad, N., Zhang, H., Zhu, H., & Rangarajan, S. (2014). Multiuser scheduling in the 3GPP LTE cellular uplink. IEEE Transactions on Mobile Computing, 13(1), 130–145.CrossRefGoogle Scholar
  56. 56.
    Rossi, M., Benatti, S., Farella, E., & Benini, L. (2015). Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics. In IEEE international conference on industrial technology (ICIT), IEEE (pp. 1700–1705).Google Scholar
  57. 57.
    Secci, S., Pujolle, G., Nguyen, T. M. T., & Nguyen, S. C. (2014). Performance-cost trade-off strategic evaluation of multipath TCP communications. IEEE Transactions on Network and Service Management, 11(2), 250–263.CrossRefGoogle Scholar
  58. 58.
    Sedlmair, M., Munzner, T., & Tory, M. (2013). Empirical guidance on scatterplot and dimension reduction technique choices. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2634–2643.CrossRefGoogle Scholar
  59. 59.
    Tabakhi, S., & Moradi, P. (2015). Relevance–redundancy feature selection based on ant colony optimization. Pattern Recognition, 48(9), 2798–2811.CrossRefGoogle Scholar
  60. 60.
    Tennant, M., Stahl, F., Rana, O., & Gomes, J. B. (2017). Scalable real-time classification of data streams with concept drift. Future Generation Computer Systems, 75, 187–199.CrossRefGoogle Scholar
  61. 61.
    Tiwana, M. I., & Tiwana, M. I. (2014). A novel framework of automated RRM for LTE son using data mining: Application to LTE mobility. Journal of Network and Systems Management, 22(2), 235–258.CrossRefGoogle Scholar
  62. 62.
    Unler, A., Murat, A., & Chinnam, R. B. (2011). mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Information Sciences, 181(20), 4625–4641.CrossRefGoogle Scholar
  63. 63.
    Xu, Y., Yue, G., & Mao, S. (2014). User grouping for massive MIMO in FDD systems: New design methods and analysis. IEEE Access, 2, 947–959.CrossRefGoogle Scholar
  64. 64.
    Xue, B., Zhang, M., & Browne, W. N. (2013). Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE transactions on cybernetics, 43(6), 1656–1671.CrossRefGoogle Scholar
  65. 65.
    Yu, J., Lee, H., Im, Y., Kim, M.-S., & Park, D. (2010). Real-time classification of internet application traffic using a hierarchical multi-class SVM. KSII Transactions on Internet & Information Systems, 4(5), 859–876.Google Scholar
  66. 66.
    Zhang, Y., Gong, D., Hu, Y., & Zhang, W. (2015). Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing, 148, 150–157.CrossRefGoogle Scholar
  67. 67.
    Zhao, M., Fu, C., Ji, L., Tang, K., & Zhou, M. (2011). Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes. Expert Systems with Applications, 38(5), 5197–5204.CrossRefGoogle Scholar
  68. 68.
    Zhenqi, S., Haifeng, Y., Xuefen, C., & Hongxia, L. (2013). Research on uplink scheduling algorithm of massive M2M and H2H services in LTE. In IET international conference on Information and communications technologies (IETICT 2013), IET (pp. 365–369).Google Scholar
  69. 69.
    Zolotukhin, M. (2014). On data mining applications in mobile networking and network security. Jyväskylä studies in computing, 189.Google Scholar

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Authors and Affiliations

  1. 1.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia

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