期刊文献+

基于统计特性的多天线能效优化 被引量:1

Energy Efficiency Optimization for Multi-antenna Systems Based on Statistical Characteristics
下载PDF
导出
摘要 针对多天线链路,提出基于信道统计特性的传输与能效优化方案.接收端采用基于训练序列的最小均方误差估计获得信道估计值,并基于码本进行有限反馈,从而使得发送端获得信道状态信息并进行波束赋形的数据传输.针对这一传输过程进行能效优化,根据其信道统计特性对最小均方误差估计值和估计误差及码本反馈量化值和反馈误差进行统计分析,进而形成针对能效度量的统计分析和能效优化模型,并给出能效最大化的训练功率及数据功率分配方案.仿真结果表明,与已有静态功率分配方案相比,提出方案可以有效地提高系统能效性,且在快速时变信道环境下,以极低复杂度获得趋近于已有动态功率分配方案的性能. An energy efficient transmission and optimization scheme was proposed for the link with multiple antennas. The channels were estimated by the receiver by using MMSE estimation with the trainings, and then were fed back to the estimated channels based on the codebook. Therefore, the transmitter transmitted data with beam forming by exploiting this feedback channel state information. To optimize the energy efficiency of the transmission, the statistics of the MMSE estimate, the estimation error, the quantized feedback value and feedback error were analyzed based on the statistical characteristic of channels. Subsequently, the statistics of energy efficiency was obtained and the optimization model of energy efficiency was also proposed, based on which the energy efficient training power and data power were given. Simulation results show that the proposed scheme can effectively improve the system energy efficiency in compared with the existing static power allocation scheme, and also can obtain the similar performance with the existing dynamic power allocation scheme in fast time varying channels but with very low complexity.
作者 杨睿哲 毕瑞琪 司鹏搏 孙艳华 张延华 YANG Ruizhe BI Ruiqi SI Pengbo SUN Yanhua ZHANG Yanhua(Beijing Laboratory of Advanced Information Networks, Beijing 100124, China College of Information and Communications, Beijing University of Technology, Beijing 100124, China)
出处 《北京工业大学学报》 CAS CSCD 北大核心 2017年第6期853-858,共6页 Journal of Beijing University of Technology
基金 北京市优秀人才资助项目(2014000020124G040) 国家自然科学基金资助项目(61571021 61372089)
关键词 多天线 功率分配 能效优化 信道估计 信道反馈 multiple input multiple output (MIMO) power allocation energy efficiency channel estimation feedback
  • 相关文献

参考文献1

二级参考文献17

  • 1Sun JG, Liu J, Zhao LY. Clustering algorithms research. Ruan Jian Xue Bao/Joumal of Software, 2008,19(1):48-61 (in Chinese with English abstract), http://www.jos.org.cn/1000-9825/19/48.htm [doi: 10.3724/SP.J.1001.2008.00048].
  • 2Zhu M. Introduction to Data Mining. Hefei: Press of University of Science and Technology of China, 2002. 138-139 (in Chinese).
  • 3Jain AK, Dubes RC. Algorithms for Clustering Data. Prentice-Hall, Inc., 1988. 1-334.
  • 4Gelbard R, Goldman O, Spiegler I. Investigating diversity of clustering methods: An empirical comparison. Data & Knowledge Engineering, 2007,63(1): 155-166* [doi: 10.1016/j.datak.2007.01.002].
  • 5MacQueen J. Some methods for classification and analysis of multivariate observations. In: Proc. of the 5th Berkeley Symp. on Mathematical Statistics and Probability. 1967. 281-297.
  • 6Lloyd S. Least squares quantization in PCM. IEEE Trans, on Information Theory, 1982,28(2):129-137. [10.1109/TIT.1982.10564 89].
  • 7Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y. Robust face recognition via sparse representation. IEEE Trans, on Pattern Analysis and Machine Intelligence, 2009,31(2):210-227. [doi: 10.1109/TPAMI.2008.79].
  • 8Wu JX. Balance support vector machines locally using the structural similarity kernel. In: Proc. of the Pacific-Asia Conf. on Knowledge Discovery and Data Mining. 2011. 112-123. [doi: 10.1007/978-3-642-20841-6 10].
  • 9Muja M, Lowe DG. Fast approximate nearest neighbors with automatic algorithm configuration. In: Proc. of the Int’l Conf. on Vision Theory and Applications. 2009. 331-340.
  • 10Nene SA, Nayar SK, Murase H. Columbia object image library (COIL-20). Technical Report, CUCS-005-96, New York: Department of Computer Science, Columbia University, 1996.

共引文献10

同被引文献1

引证文献1

二级引证文献2

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