[1] Li Xi, Song Tiecheng, Zhang Yueyue, Chen Guojun, et al. A weighted selection combining schemefor cooperative spectrum prediction in cognitive radio networks [J]. Journal of Southeast University (English Edition), 2018, 34 (3): 281-287. [doi:10.3969/j.issn.1003-7985.2018.03.001]

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A weighted selection combining schemefor cooperative spectrum prediction in cognitive radio networks()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

34

Issue:

2018 3

Page:

281-287

Research Field:

Information and Communication Engineering

Publishing date:

2018-09-20

Info

Title:

A weighted selection combining schemefor cooperative spectrum prediction in cognitive radio networks

Author(s):

Li Xi;  Song Tiecheng;  Zhang Yueyue;  Chen Guojun;  Hu Jing

National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Keywords:

cognitive radio network;  cooperative spectrum prediction;  genetic algorithm-based neural network;  iterative self-organizing data analysis algorithm;  weighted selection combining

PACS:

TN915

DOI:

10.3969/j.issn.1003-7985.2018.03.001

Abstract:

A weighted selection combining(WSC)scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network(GANN)is designed to perform spectrum prediction in consideration of both the characteristics of the primary users(PU)and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis(ISODATA)algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.

References:

[1] Liang Y C, Zeng Y H, Phe E C Y, et al. Sensing-throughput tradeoff for cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2008, 7(4): 1326-1337.
[2] Tumuluru V K, Wang P, Niyato D. Channel status prediction for cognitive radio networks [J]. Wireless Communications & Mobile Computing, 2012, 12(10): 862-874. DOI:10.1002/wcm.1017.
[3] Eltholth A A. Spectrum prediction in cognitive radio systems using a wavelet neural network [C]// 2016 24th International Conference on Software Telecommunications & Computer Networks. Split, Croatia, 2016: 1-6. DOI:10.1109/softcom.2016.7772181.
[4] Shamsi N, Mousavinia A, Amirpour H. A channel state prediction for multi-secondary users in a cognitive radio based on neural network [C]// 2013 International Conference on Electronics, Computer and Computation. Ankara, Turkey, 2013: 1-4. DOI:10.1109/icecco.2013.6718263.
[5] Bai S Y, Zhou X, Xu F J. “Soft decision” spectrum prediction based on back-propagation neural networks[C]// International Conference on Computing, Management and Telecommunications. Da Nang, Vietnam, 2014: 128-133.
[6] Yang J, Zhao H, Chen X. Genetic algorithm optimized training for neural network spectrum prediction[C]// IEEE International Conference on Computer & Communications. Chengdu, China, 2017: 2949-2954.
[7] Eltom H, Kandeepan S, Liang Y C, et al. HMM based cooperative spectrum occupancy prediction using hard fusion [C]// IEEE International Conference on Communications Workshops. Kuala Lumpur, Malaysia, 2016: 669-675.
[8] Jing T, Xing X S, Cheng W, et al. Cooperative spectrum Prediction in multi-PU multi-SU cognitive radio networks[C]// Proceedings of the 8th International Conference on Cognitive Radio Oriented Wireless Networks.Washington, DC, USA, 2013:502-511. DOI:10.4108/icst.crowncom.2013.252029.
[9] Barnes S D, Maharaj B T, Alfa A S. Cooperative prediction for cognitive radio networks[J]. Wireless Personal Communications, 2016, 89(4): 1177-1202. DOI:10.1007/s11277-016-3311-z.
[10] Sun D F, Song T C, Gu B, et al. Spectrum sensing and the utilization of spectrum opportunity tradeoff in cognitive radio network [J]. IEEE Communications Letters, 2016, 20(12): 2442-2445. DOI:10.1109/lcomm.2016.2605674.
[11] Chen J, Guo X, Zhang S. A spectrum access strategy for cloud-based cognitive radio networks[C]//International Conference on Wireless Communications & Signal Processing. Yangzhou, China, 2016:1-5.
[12] Chen Z, Guo N, Hu Z, et al. Channel state prediction in cognitive radio, Part Ⅱ: Single-user prediction [J]. Proceedings of IEEE Southeastcon, 2011, 670(8): 50-54. DOI:10.1109/secon.2011.5752904.
[13] Saroha S, Aggarwal S K. Multi step ahead forecasting of wind power by genetic algorithm based neuralnetworks[C]// 2014 6th IEEE Power India International Conference(PIICON).Delhi, India, 2014: 1-6.DOI:10.1109/poweri.2014.7117664.
[14] Zhang G Q, Patuwo B E, Hu M Y. Forecasting with artificial neural networks: The state of the art [J]. International Journal of Forecasting, 1998, 14(1): 35-62. DOI:10.1016/s0169-2070(97)00044-7.
[15] Ball G H, Hall J. Isodata, a novel method of data analysis and pattern classification [R]. Menlo Park, CA, USA: Stanford Research Institute, 1965.
[16] Zhou J, Shen Y, Shao S H, et al. Cooperative spectrum sensing scheme with hard decision based on location information in cognitive radio networks [J]. Wireless Personal Communications, 2013, 71(4): 2637-2656. DOI:10.1007/s11277-012-0961-3.
[17] Chair Z, Varshney P K. Optimal data fusion in multiple sensor detection systems [J]. IEEE Transactions on Aerospace & Electronic Systems, 2007, 22(1): 98-101. DOI:10.1109/taes.1986.310699.
[18] Eghbali Y, Hassani H, Attari M A. Cooperative spectrum sensing by improved energy detector for heterogeneous environments in cognitive radio networks [C]// 6th International Symposium on Telecommunications. Tehran, Iran, 2013: 383-386. DOI:10.1109/istel.2012.6483017.

Memo

Memo:

Biographies: Li Xi(1992—), female, graduate; Song Tiecheng(corresponding author), male, doctor, professor, songtc@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61771126, 61372104), the Science and Technology Project of State Grid Corporation of China(No.SGRIXTKJ[2015]349).
Citation: Li Xi, Song Tiecheng, Zhang Yueyue, et al. A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks[J].Journal of Southeast University(English Edition), 2018, 34(3):281-287.DOI:10.3969/j.issn.1003-7985.2018.03.001.

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