|Table of Contents|

[1] Song Xiaoqin, Jin Hui, Tan Yazhu, Hu Jing, et al. A cooperative spectrum sensing results transmission schemewith LT code based on energy efficiency priority [J]. Journal of Southeast University (English Edition), 2018, (4): 444-450. [doi:10.3969/j.issn.1003-7985.2018.04.005]

A cooperative spectrum sensing results transmission schemewith LT code based on energy efficiency priority()

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

2018 4
Research Field:
Information and Communication Engineering
Publishing date:


A cooperative spectrum sensing results transmission schemewith LT code based on energy efficiency priority
Song Xiaoqin1 Jin Hui1 Tan Yazhu1 Hu Jing2 Song Tiecheng2
1College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2 School of Information Science and Engineering, Southeast University, Nanjing 210096, China
cooperative spectrum sensing energy-efficient cluster-based Luby transform(LT)code degree distribution
To deal with a sharp increase in transmission energy consumption due to the presence of a large number of secondary users(SUs), an energy-efficient cooperative spectrum sensing results transmission scheme is proposed for cognitive radio systems. First, a cluster-based structure is introduced into the sensing results transmission scheme to reduce the transmission power. Then, the centralized sensing results transmission model is employed, and the non-fixed code rate Luby transform(LT)code is selected as the channel coding since its code rate can dynamically adapt to channel conditions and therefore avoid unnecessary redundancy in the transmission power. Furthermore, an improved optimal degree distribution(ODD)is designed for the LT code. The simulation results show that the choice of the appropriate parameters in degree distribution is very helpful for the LT code to achieve a promising performance. The ODD with optimized parameters can achieve more than 2 dB performance gain than other typical degree distributions when the bit error rate(BER)is 10-3. The energy consumption of the proposed scheme is not only at least 71.4% lower than that of the non-coding system, but also lower than that of the convolutional coding system with different code rates. Meanwhile, the energy consumption can be further reduced in the case that a suitable clustering method is selected.


[1] Ramani V, Sharma S K. Cognitive radios: A survey on spectrum sensing, security and spectrum handoff[J]. China Communications, 2017, 14(11): 185-208. DOI:10.1109/cc.2017.8233660.
[2] Luby M. LT codes[C]//Proceedings of the 43rd Annual IEEE Symposium on Foundations of Computer Science. Vancouver, Canada, 2002: 271-282. DOI: 10.1109/SFCS.2002.1181950.
[3] Sharma S, Bohara V A, Peer M. Opportunistic and cooperative hybrid spectrum access protocol for cognitive radio network [C]//IEEE International Conference on Advanced Networks and Telecommunications Systems(ANTS). Bangalore, India, 2016:1-6. DOI: 10.1109/ANT S.2016.7947852.
[4] Bogale T E, Vandendorpe L, Le L B. Wide-band sensing and optimization for cognitive radio networks with noise variance uncertainty[J]. IEEE Transactions on Communications, 2015, 63(4): 1091-1105. DOI:10.1109/tcomm.2015.2394390.
[5] Alabbasi A, Rezki Z, Shihada B. Energy efficient resource allocation for cognitive radios: A generalized sensing analysis[J]. IEEE Transactions on Wireless Communications, 2015, 14(5): 2455-2469. DOI:10.1109/twc.2014.2387161.
[6] Mitzenmacher M. Digital fountains: A survey and look forward[C]//Information Theory Workshop. San Antonio, USA, 2004:271-276. DOI: 10.1109/ITW. 2004.1405313.
[7] Shao H Q, Xu D Z, Zhang X F. Distributed Luby transform coding for three-source single-relay networks based on the deconvolution of robust soliton distribution[J]. IET Communications, 2015, 9(2): 167-176. DOI:10.1049/iet-com.2014.0586.
[8] Zhang S, Lee J, Tepedelenlioglu C, et al. Distributed estimation of the degree distribution in wireless sensor networks[C]//2016 IEEE Global Communications Conference(GLOBECOM). Washington DC, USA, 2016:1-6. DOI: 10.1109/GLOCOM.2016.7841740.
[9] Hussain I, Xiao M, Rasmussen L K. Erasure floor analysis of distributed LT codes[J].IEEE Transactions on Communications, 2015, 63(8): 2788-2796. DOI:10.1109/tcomm.2015.2451640.
[10] Yang H T, Jiang M, Shen H, et al. A distributed LT code design for multiple-access relay networks subject to erasures[J].IEEE Communications Letters, 2015, 19(4): 509-512. DOI:10.1109/lcomm.2015.2398412.
[11] Yao W Q, Yi B S, Huang T Q, et al. Poisson robust soliton distribution for LT codes[J].IEEE Communications Letters, 2016, 20(8): 1499-1502. DOI:10.1109/lcomm.2016.2578920.
[12] Lazaro F, Liva G, Bauch G. Inactivation decoding of LT and raptor codes: Analysis and code design[J]. IEEE Transactions on Communications, 2017: 1-1. DOI:10.1109/tcomm.2017.2715805.
[13] Tsai P C, Chen M, Chen Y P. Sparse degrees analysis for LT codes optimization[C]//IEEE Congress on Evolutionary Computation. Brisbane, Australia, 2012: 1-6. DOI: 10.1109/CEC.2012. 6252861.
[14] Shokrollahi A, Luby M. Raptor codes [M].Hanver, USA: Now Publishers Inc, 2011: 1-136.
[15] Nguyen T D, Yang L L, Ng S X, et al. An optimal degree distribution design and a conditional random integer generator for the systematic Luby transform coded wireless Internet[C]//IEEE Wireless Communications and Networking Conference in America. Las Vegas, USA, 2008:243-248. DOI: 10.1109/WCNC.2008.48.


Biography: Song Xiaoqin(1973—), female, doctor, associate professor, xiaoqin.song@163.com.
Foundation items: The National Natural Science Foundation of China(No.61771126), the Foundation of Graduate Innovation Center in NUAA(No.kfjj20170402).
Citation: Song Xiaoqin, Jin Hui, Tan Yazhu, et al.A cooperative spectrum sensing results transmission scheme with LT code based on energy efficiency priority[J].Journal of Southeast University(English Edition), 2018, 34(4):444-450.DOI:10.3969/j.issn.1003-7985.2018.04.005.
Last Update: 2018-12-20