期刊文献+

Achievement of Interference Alignment in General Underlay Cognitive Radio Networks: Scenario Classification and Adaptive Spectrum Sharing 被引量:1

Achievement of Interference Alignment in General Underlay Cognitive Radio Networks: Scenario Classification and Adaptive Spectrum Sharing
下载PDF
导出
摘要 Interference alignment(IA) is suitable for cognitive radio networks(CRNs).However, in IA spectrum sharing(SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user(PU), which may lead to low signalto-noise-ratio at cognitive radio receivers(CRRs). Consequently, sum rate of cognitive users(CUs) may fall short of the theoretical maximum through IA. To solve this problem,we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU. Interference alignment(IA) is suitable for cognitive radio networks(CRNs).However, in IA spectrum sharing(SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user(PU), which may lead to low signalto-noise-ratio at cognitive radio receivers(CRRs). Consequently, sum rate of cognitive users(CUs) may fall short of the theoretical maximum through IA. To solve this problem,we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU.
作者 Mei Rong
出处 《China Communications》 SCIE CSCD 2018年第6期98-108,共11页 中国通信(英文版)
基金 supported by National Natuvertexesral Science Foundation of China under Grant 61201233 61271262 and 61701043
关键词 收音机 干扰 光谱 分类 网络 排列 空间设计 CRN cognitive radio networks spectrum sharing interference alignment scenario classification
  • 相关文献

参考文献3

二级参考文献13

  • 1S. A. Jafar, Interference Alignment -- A New Look at Signal Dimensions in a Communication Network[J], Foundations and Trends in Communications and Information Theory, 201 I, 7( l ), pp. 1 - 134.
  • 2K. Gomadam, V. R. Cadambe and S. A. Jafar, Approaching the capacity of wireless networks through distributed interference alignment[C], IEEE Global Telecommunications Conference (GLOBECOM), Dec. 2008.
  • 3B. Niu and A. M. Haimovich, Interference subspace tracking for network interference alignment in cellular systems[C], IEEE Global Telecommunications Conference(GLOBECOM), Dec. 2009.
  • 4S.M. Perlaza, N. Fawaz, S. Lasaulce and M. Debbah, From Spectrum Pooling to Space Pooling: Opportunistic Interference Alignment in MIMO Cognitive Networks[J], IEEE Transactions on Signal Processing, 2010, 58(7), pp. 3728 - 3741.
  • 5Bin Zhu, Jianhua Ge, Jing Li, Xiaoye Shi and Yunxia Huang, Interference alignment for MIMO cognitive networks: a complex FDPM- based subspace tracking approach[J], Int. J. of Embedded Systems, 2013, 5(3), pp. 166 - 174.
  • 6Mohamed Amir, Amr EI-Keyi and Mohammed Nafie, Constrained Interference Alignment and the Spatial Degrees of Freedom of MIMO Cognitive Networks[J], IEEE Transactions on Information Theory, 2011, 57(5), pp. 2994 - 3004.
  • 7C. M. Yetis, Tiangao Gou, S. A. Jafar and A. H. Kayran, On Feasibility of Interference Alignment in MIMO Interference Networks[J], IEEE Transactions on Signal Processing, 2010, 58(9), pp. 4771 - 4782.
  • 8X. G. Doukopoulos and G. V. Moustakides, The fast data projection method for stable subspace tracking[C], 13th Europe Signal Processing. Conference (ESPC), Sep. 2005.
  • 9PANG Xingdong HONG Wei YANG Tianyang LI Linsheng.Design and Implementation of An Active Multibeam Antenna System with 64 RF Channels and 256 Antenna Elements for Massive MIMO Application in 5G Wireless Communications[J].China Communications,2014,11(11):16-23. 被引量:4
  • 10JIA Haikun,CHI Baoyong,KUANG Lixue,YU Xiaobao,CHEN Lei,ZHU Wei,WEI Meng,SONG Zheng,WANG Zhihua.Research on CMOS Mm-Wave Circuits and Systems for Wireless Communications[J].China Communications,2015,12(5):1-13. 被引量:2

共引文献3

同被引文献1

引证文献1

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部