期刊文献+

基于交叉熵的NC-OFDM系统最优导频序列设计算法 被引量:1

Optimal pilot sequence design algorithm for NC-OFDM system based on cross entropy
下载PDF
导出
摘要 针对NC-OFDM(non-contiguous orthogonal frequency)系统子载波非连续且动态变化导致传统等间隔分布的导频序列设计算法计算复杂度高、频谱利用率低和处理时延大等问题,提出了基于交叉熵的最优导频序列位置优化设计算法。算法以最小化系统均方误差为准则,采用交叉熵算法在所有可用子载波中求解目标函数的最优解,有效解决了传统穷举搜索算法计算复杂度高的问题。与传统算法相比,所提算法保证了信道估计精度,降低了系统均方误差和误码率,从整体上提高了系统性能。 The subcarriers of NC-OFDM(non-contiguous orthogonal frequency)system have non-continuous and dynamic characteristics,resulting in high computational complexity,low spectrum utilization and long time delay for equally spaced pilot sequence.A new and optimal pilot design algorithm based on cross entropy is proposed in this paper.The optimal pilot placement is derived by minimizing the mean square error and obtained by cross entropy.The proposed method decreases the computational complexity of conventional algorithms.Simulation results indicate that the proposed pilot design algorithm provides better channel estimation precision as well as MSE and BER performance and improves the system performance,compared with current methods.
出处 《中国科技论文》 CAS 北大核心 2015年第2期134-138,共5页 China Sciencepaper
基金 国家自然科学基金资助项目(61104005) 高等学校博士学科点专项科研基金资助项目(20110042120015)
关键词 信号处理 正交频分复用 导频序列 交叉熵 signal processing OFDM pilot sequence cross entropy
  • 相关文献

参考文献3

二级参考文献32

  • 1卢新明,吴方.一个求解线性不等式组的新算法[J].应用数学学报,1995,18(3):340-343. 被引量:3
  • 2VAN DE BEEK J J, EDFORS O. On channel estimation in OFDM, systems[A]. Proc of IEEE VTC 1995.Piscataway: IEEE[C]. 1995, 2: 815-819. 被引量:1
  • 3DONOHO D L, ELAD M, TEMLYAKOV V. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Trans on Information Theory, 2006 52(1): 6-18. 被引量:1
  • 4BUDIARJO I, RASHAD I, NIKOOKAR H. Efficient pilot pattern for OFDM-based cognitive radio channel estimation-part 1 [A]. 14th IEEE Symposium on Communications and Vehicular Technology in the Benelux[C]. 2007.1-5. 被引量:1
  • 5LIU J N, FENG S L, WANG H G. Comb-type pilot aided channel estimation in non-contiguous OFDM systems for cognitive radio[A]. Proceedings of the 5th International Conference on Wireless commu- nications, networking and mobile computing[C]. Beijing, china, 2009.1463-1466. 被引量:1
  • 6DONOHO D L. Compressed sensing[J].IEEE Trans on Info Theory, 2006,52(4): 1289-1306. 被引量:1
  • 7BARANIUK R G. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-120,124. 被引量:1
  • 8PAREDES J L, ARCE G R, WANG Z M. Ultra-wideband compressed sensing: channel estimation[J]. IEEE Journal of Selected Topics in Signal processing, 2007,1(3):383-395. 被引量:1
  • 9TAUBOCK (2 HLAWATSCH F, EIWEN D, et al. Compressive esti- marion of doubly selective channels in multicarrier systems: leakage effects and sparsity-enhancing processing[J] .IEEE Journal of se- lected topics in signal processing, 2010, 4(2): 255-271. 被引量:1
  • 10CANDES E, TAO T. Near optimal signal recovery from random pro- jections: universal encoding strategies?[J]. IEEE Trans on Information Theory, 2006, 52(12):5406-5425. 被引量:1

共引文献22

同被引文献9

引证文献1

二级引证文献5

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