Efficient spectrum sensing mechanism with interference cancellation in cognitive radio network

doi:10.11772/j.issn.1001-9081.2014.05.1243

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (5): 1243-1246.DOI: 10.11772/j.issn.1001-9081.2014.05.1243

• Network and communications • Previous Articles Next Articles

Efficient spectrum sensing mechanism with interference cancellation in cognitive radio network

XIAO Heng¹,LV Shaohe²

1. Department of Basic Computer Education, School of Sanya, Sanya Hainan 572022, China;
2. National Laboratory of Parallel and Distributed Processing, National University of Defense Technology, Changsha Hunan 410073, China

Received:2013-11-07 Revised:2013-12-21 Online:2014-05-30 Published:2014-05-01
Contact: LV Shaohe
Supported by:
;National Natural Science Foundation

认知网络融合干扰消除的高效频谱感知机制

肖衡¹,吕绍和²

1. 三亚学院计算机公共教学部，海南三亚 572022
2. 国防科学技术大学并行与分布处理重点实验室，长沙 410073

通讯作者: 吕绍和
作者简介:肖衡（1979-），女，湖南衡阳人，讲师，硕士，主要研究方向：计算机网络安全；吕绍和(1982-)，男，湖南永州人，助理研究员，博士，主要研究方向：无线网络、网络安全。
基金资助:
三亚市院地科技合作项目;国家自然科学基金资助项目

Abstract

Abstract:

To facilitate the quick detection of primary user and discover the available channel in a wireless cognitive network, a novel spectrum sensing mechanism was proposed. The mechanism conducted spectrum sensing simultaneously with data transmission to reduce the sense time, and exploited the interference cancellation technique to cancel out the self-interference. Furthermore, by combining the Fourier transform results of multiple signals with different sampling rates, it could quickly detect the behavior of the wideband channel. Finally, during the transmission, the primary user was detected in time by the well-known signature matching method. The experimental results show that, the sense time is reduced by 50%, and the throughput gain is as large as 100%. In summary, the proposed scheme can effectively reduce the overhead of spectrum sensing and improve the efficiency of cognitive communication.

摘要：

针对无线认知网次用户快速寻找可用信道与检测主用户出现的问题，提出一种新的高效频谱感知机制。该机制通过感知与传输并发以减少感知的时间开销，利用干扰消除技术消除自身传输对感知的干扰；通过结合不同采样速率下信道状态的观察，实现宽频谱的信道感知；在传输中，利用特征匹配技术检测主用户的出现。实验结果表明，新机制可减少感知时间50%，提高吞吐量100%以上。因此，新机制有效降低了频谱感知的资源消耗，提高了认知通信的效率。

CLC Number:

TP393

XIAO Heng LV Shaohe. Efficient spectrum sensing mechanism with interference cancellation in cognitive radio network[J]. Journal of Computer Applications, 2014, 34(5): 1243-1246.

肖衡吕绍和. 认知网络融合干扰消除的高效频谱感知机制[J]. 计算机应用, 2014, 34(5): 1243-1246.

References

［1］HAYKIN S, THOMSON D J, REED J H. Spectrum sensing for cognitive radio ［J］. Proceedings of the IEEE, 2009,97(5):849-877.
［2］AKYILDIZ I F, LEE W Y, VURAN M C, et al. Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey［J］. Computer Networks: The International Journal of Computer and Telecommunications Networking, 2006,50(13):2127-2159.
［3］WANG Y, TIAN Z, FENG C. Cooperative spectrum sensing based on matrix rank minimization ［C］// Proceedings of the 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway: IEEE Press, 2011:3000-3003.
［4］QUAN Z, CUI S, SAYED A H, et al. Wideband spectrum sensing in cognitive radio networks［C］// ICC '08: Proceedings of the 2008 IEEE International Conference on Communications. Piscataway: IEEE Press, 2008: 901-906.
［5］CHOI J I, JAIN M, SRINIVASAN K, et al. Achieving single channel, full duplex wireless communication［C］// Proceedings of the 16th Annual International Conference on Mobile Computing and Networking. New York: ACM, 2010:1-12.
［6］ARYAFAR E, KHOJASTEPOUR M A, SUNDARESAN K, et al. MIDU: enabling MIMO full duplex［C］// Proceedings of the 18th Annual International Conference on Mobile Computing and Networking. New York: ACM, 2012:257-268.
［7］CHENG W, ZHANG X, ZHANG H. Imperfect full duplex spectrum sensing in cognitive radio networks［C］// Proceedings of 3rd ACM Workshop on Cognitive Radio Networks. New York: ACM, 2011:1-6.
［8］CHENG W, ZHANG X, ZHANG H. Optimal dynamic power control for full-duplex bidirectional-channel based wireless networks［C］// Proceedings of the 2013 IEEE International Conference on Computer Communications. Piscataway: IEEE Press, 2013:3120-3128.
［9］LYU S, ZHUANG W, XU M, et al. Understanding the scheduling performance in wireless networks with successive interference cancellation ［J］. IEEE Transactions on Mobile Computing, 2013,12(8):1625-1639.
［10］ANDREWS J G. Interference cancellation for cellular systems: a contemporary overview［J］. IEEE Wireless Communications, 2005,12(2):19-29.
［11］HASSANIEH H, SHI L, ABARI O, et al. BigBand: GHz-wide sensing and decoding on commodity radios［R/OL］. ［2013-06-11］. http://dspace.mit.edu/bitstream/handle/1721.1/79058/MIT-CSAIL-TR-2013-009.pdf?sequence=1.
［12］LYU S, ZHUANG W, WANG X, et al. Link scheduling in wireless networks with successive interference cancellation［J］. Computer Networks, 2011,55(13):2929-2941.

[1]	ZONG Xuejun, HAN Bing, WANG Guogang, NING Bowei, HE Kan, LIAN Lian. TDRFuzzer: fuzzy testing method for industrial control protocols based on adaptive dynamic interval strategy [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	Ming ZHANG, Le FU, Haifeng WANG. Relay control model for concurrent data flow in edge computing [J]. Journal of Computer Applications, 2024, 44(12): 3876-3883.
[3]	. Survey of DNS tunneling detection technology research [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Large scale IoT binary component identification based on named entity recognition [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	Yixi LIU, Jun HE, Bo WU, Bingtong LIU, Ziyu LI. Survey of software security testing techniques in DevSecOps [J]. Journal of Computer Applications, 2024, 44(11): 3470-3478.
[6]	Chuanlin PANG, Rui TANG, Ruizhi ZHANG, Chuan LIU, Jia LIU, Shibo YUE. Distributed power allocation algorithm based on graph convolutional network for D2D communication systems [J]. Journal of Computer Applications, 2024, 44(9): 2855-2862.
[7]	Jiepo FANG, Chongben TAO. Hybrid internet of vehicles intrusion detection system for zero-day attacks [J]. Journal of Computer Applications, 2024, 44(9): 2763-2769.
[8]	ZHANG Yiming, CAO Tengfei. Federated Learning Optimization Algorithm based on Local Drift and Diversity Computing Power [J]. Journal of Computer Applications, 0, (): 0-0.
[9]	Hang XU, Zhi YANG, Xingyuan CHEN, Bing HAN, Xuehui DU. Coverage-guided fuzzing based on adaptive sensitive region mutation [J]. Journal of Computer Applications, 2024, 44(8): 2528-2535.
[10]	Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499.
[11]	GENG Haijun, DONG Yun, HU Zhiguo, CHI Haotian, YANG Jing, YIN Xia. Encrypted traffic classification method based on Attention-1DCNN-CE#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	. Summary of network intrusion detection systems based on deep learning [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	CHEN Ruilong, HU Tao, BU Youjun, YI Peng, HU Xianjun, QIAO Wei. Stacking ensemble adversarial defense method for encrypted malicious traffic detection model [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	Zhongdai WU, Dezhi HAN, Haibao JIANG, Cheng FENG, Bing HAN, Chongqing CHEN. Review of marine ship communication cybersecurity [J]. Journal of Computer Applications, 2024, 44(7): 2123-2136.
[15]	Zihao YAO, Yuanming LI, Ziqiang MA, Yang LI, Lianggen WEI. Multi-object cache side-channel attack detection model based on machine learning [J]. Journal of Computer Applications, 2024, 44(6): 1862-1871.

Efficient spectrum sensing mechanism with interference cancellation in cognitive radio network

认知网络融合干扰消除的高效频谱感知机制

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics