基于混合遗传算法的导频优化

doi:10.11959/j.issn.1000-0801.2016248

摘要/Abstract

摘要：

OFDM系统中，基于压缩感知的稀疏信道估计能够充分利用无线信道的固有稀疏性，进而降低导频开销，提高频谱利用率。针对压缩感知信道估计的导频设计，通过最小化压缩感知理论中测量矩阵的互相关性，提出一种基于混合遗传算法的导频优化方法。该方案首先采用遗传算法获得次优初始导频序列，然后结合导频位置以及导频功率对导频序列逐位进行替换、优化，以使测量矩阵的互相关性最小。MATLAB 仿真结果表明，相比于伪随机导频设计和等间距导频设计，该算法能够保证较低的均方误差和误码率。

Abstract:

In OFDM system,sparse channel estimation based on compressed sensing(CS)can make full use of the inherent sparse degree of the wireless channel,which can reduce the pilot overhead and improve the spectrum efficiency.Therefore,a new method based on hybrid genetic algorithm was investigated for the pilot design of CS channel estimation,which was based on the minimization of the matrix cross correlation in the CS theory.In this method,genetic algorithm was used to obtain the initial sub-optimal pilot sequence,and then combined with the pilot position and pilot power,each entry of pilot pattern could be sequentially updated and optimized to make the minimum correlation of measurement matrix.Simulation results show that the proposed method can ensure a better mean square error and bit error rate compared to the pseudo-random pilot design and the equal distance pilot design.

Key words: channelestimation, crosscorrelation, hybridgeneticalgorithm, measurementmatrix, compressedsensing,pilotpattern

郑寒冰,余翔,王维维. 基于混合遗传算法的导频优化[J]. 电信科学, 2016, 32(9): 75-81.

Hanbing ZHENG,Xiang YU,Weiwei WANG. Hybrid genetic algorithm based optimization of pilotpattern[J]. Telecommunications Science, 2016, 32(9): 75-81.

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参考文献 16

[1]	IEEE 802.11a-1999 part 11.Wireless LAN medium access control(MAC)and physical layer(PHY)specifications[S]. 1999.
[2]	LIU Y , TAN Z , HU H ,et al. Channel estimation for OFDM[J]. IEEE Communications Surveys ＆Tutorials, 2014,16(4): 1891-1908.
[3]	GHOSH A , ZHANG J , ANDREWS J G ,et al. Fundamentals of LTE[M]. Upper Saddle River: Prentice Hall PressPress, 2010: 28-41.
[4]	NGUYEN Q D , LUU T T , PHAM L H ,et al. Inter-carrier interference suppression combined with channel estimation for mobile OFDM system[C]// 2016 Second Asian Conference on Defence Technology(ACDT),Jan 21-23,2016,Chiang Mai,Thailand. New Jersey:IEEE Press, 2016: 61-66.
[5]	QI C , WU L , HUANG Y ,et al. Joint design of pilot powerand pilot pattern for sparse cognitive radio systems[J]. IEEE Transactions on Vehicular Technology, 2015,11(64): 5384-5390.
[6]	HU D , WANG X , HE L . A new sparse channel estimation and trackingmethod for time-varying OFDM systems[J]. IEEE Transactions on Vehicular Technology, 2013,9(62): 4648-4653.
[7]	BARBOTIN Y , VETTERLI M . Fast and robust parametric estimation ofjointly sparse channels[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2012,3(2): 402-412.
[8]	BAJWA W U , HAUPT J , SAYEED A M . Compressed channel sensing:anew approach to estimating sparse multipath channels[J]. Proceedings of the IEEE, 2010,98(6): 1058-1076.
[9]	HU D , HE L H , WANG X D . An efficient pilot design methodfor OFDM-based cognitive radio systems[J]. IEEE Transactions on Wireless Communications, 2011,10(4): 1252-1259.
[10]	HE X Y , SONG R F , ZHU W P . Pilot allocation for sparse channel estimation in MIMO-OFDM systems[J]. IEEE Transactions on Circuits Systems II:Express Briefs, 2013,9(60): 612-616.
[11]	SILVEIRA L R , TANSCHEIT R , VELLASCO M . Quantum-inspired genetic algorithms applied to ordering combinatorial optimization problems[C]// 2012 IEEE Congress on Evolutionary Computation (CEC),June 10-15,2012,Brisbane,Australia. New Jersey:IEEE Press, 2012: 1-7.
[12]	GUO P , WANG X , HAN Y S . The enhanced genetic algorithms for the optimization design[C]// 3rd International Conference on Biomedical Engineering and Informatics,Oct 16-18,2010,Yantai,China. New Jersey:IEEE Press, 2010: 2990-2994.
[13]	QI C , WU L . A study deterministic pilot allocation for spare channel estimation in OFDM systems[J]. IEEE Communications Letters, 2012,16(5): 742-744.
[14]	TROPP J A . Greed is good:algorithmicresults for sparse approximation[J]. IEEE Transactions on Information Theory, 2004,50(10): 2231-2242.
[15]	QI C , YUE G , WU L . Pilot design schemes for sparse channel estimation in OFDM systems[J]. IEEE Transactions on Vehicular Technology, 2012,4(64): 1493-1505.
[16]	余翔, 徐宇明, 吕世起 ,等. 一种多点协作压缩感知信道估计伪随机导频图案生成算法[J]. 重庆邮电大学学报(自然科学版), 2015,4(27): 499-505. YU X , XU Y M , LV S Q ,et al. Generation algorithm of pseudorandom pilot pattern formultipoint cooperative compressive sensing channel estimation[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2015,4(27): 499-505.

参数	取值
子载波数N	512
循环长度CP	100
信道长度L	50
信道稀疏度K	6
信噪比E_b/N₀/dB	0～30
导频数Np	32、48
调制方式	QPSK