基于二次差分概率的无线信道估计方法

doi:10.11959/j.issn.1000-0801.2017015

摘要/Abstract

摘要：

5G具有高速率和大带宽的特性，使得无线信号在时域和频域的衰落更加明显。随着差分值统计步长和空间采样间隔的增大，采用差分概率信道估计方法已无法跟踪深衰落区域，且峰值区域跟踪过度。针对以上问题提出一种二次差分概率的信道估计方法。该方法首先找到衰落曲线的谷值点和峰值点，然后通过二次差分概率对深衰落区域和峰值区域的估计差分值进行改善，最后利用差分运算得到估计增益。仿真实验结果表明，此方法有效地提高了整体的跟踪性能，并降低了跟踪误差。

关键词: 5G, 移动无线信道, 信道估计, 差分概率, 二次差分

Abstract:

The fifth generation (5G) mobile communication technology has the characteristics of high speed and large bandwidth,which makes the fading of radio signal in time domain and frequency domain more obviously.With the increase of differential values statistical step and the spatial sampling interval,the channel estimation method based on the differential probability can't track the deep fading area and the peak area was over-tacked.In view of the above problems,a channel estimation method based on quadratic differentials probability was proposed.Firstly,the valley point and peak point of the fading curve were found.Then,the estimated differential values of deep fading area and peak area were improved by quadratic differential probability.Finally,the estimated gains were obtained by the difference operation.Simulation results show that,this method can improve the whole tracking performance effectively and reduce the tracking error.

Key words: 5G, mobile radio channel, channel estimation, differentials probability, quadratic differentials

中图分类号:

TN929.5

王立姣,杜薇薇,李凡,王智森. 基于二次差分概率的无线信道估计方法[J]. 电信科学, 2017, 33(1): 34-44.

Lijiao WANG,Weiwei DU,Fan LI,Zhisen WANG. Radio channel estimation method based on quadratic differentials probability[J]. Telecommunications Science, 2017, 33(1): 34-44.

图/表 19

图1

图2

图3

图4

图5

图6

表1

表2

表3

表4

表5

表6

表7

表8

表9

图7

图8

图9

图10

参考文献 25

[1]	陆琦 . 无线通信衰落信道的实现[D]. 大连：大连海事大学， 2007.
	LU Q . Therealizationoffadingchannelsforwirelesscommunication[D]. Dalian: Dalian Maritime University, 2007.
[2]	SINGH Y . Comparison of Okumura,Hata and COST-231 modelson the basis of path loss and signal strength[J]. International Journal of Computer Applications, 2012,59(11):37-41.
[3]	张鑫，黄进文 . 移动通信中 Okumura 和 Walfisch-Bertoni 传播预测模型对比浅析[J]. 科技信息：学术研究， 2007(35): 86-90.
	ZHANG X , HUANG J W . Comparison of Okumura and Walfisch-Bertoni propagation prediction models in mobile nommunication[J]. Scientific Information:Academic Research, 2007(35): 86-90.
[4]	NADIR Z , AHMAD M I . Characterization of pathloss using Okumura-Hata model and missing data prediction for Oman[J]. 2010,1285(1):509-518.
[5]	COTA N , SERRADOR A , VIEIRA P , et al . On the use of Okumura-Hata propagation model on railway communications[J]. Wireless Personal Communications, 2013,6983(6):1-5.
[6]	MARDENI R , PRIYA T S . Optimised COST-231 Hata models for WiMAX path loss prediction in suburban and open urban environments[J]. Modern Applied Science, 2010,4(9):75-89.
[7]	SHABBIR N , SADIQ M T , KASHIF H , et al . Comparison of radio propagation models for long term evolution(LTE)network[J]. International Journal of Next-Generation Networks, 2011,3(3):27-41.
[8]	JOSEPH I . CDMA2000 radio measurements at 1.9GHz and comparison of propagation models in three built-up cities of south-south,Nigeria[J]. American Journal of Engineering Research, 2000,2(5):96-106.
[9]	DALKILIC T E , HANCI B Y , APAYDIN A . Fuzzy adaptive neural network approach to path loss prediction in urban areas at GSM-900 band[J]. Turkish Journal of Electrical Engineering＆ Computer Sciences, 2010,18(6):1077-1094.
[10]	JAIN R , SHRIVASTAVA L . Performance evaluation of AODV,SR,DYMO ＆ ZRP in Cost 231 Walfisch-Ikegami path loss propagation model[J]. Oeconomics of Knowledge, 2011,3(3):2-15.
[11]	JOSHI S , GUPTA V . A review on empirical data collection and analysis of Bertoni's model at 1.8 GHz[J]. International Journal of Computer Applications, 2012,56(6):17-23.
[12]	楚高峰 . 认知无线电中信道估计技术的研究[D]. 重庆：重庆邮电大学， 2011.
	CHU G F . Research on channel estimation technology in cognitive radio[D]. Chongqing: Chongqing University of Posts and Telecommunications, 2011.
[13]	GE X , TU S , HAN T , et al. Energy efficiency of small cell backhaul networks based on Gauss-Markov mobile models[J]. Networks IET, 2014,4(2):158-167.
[14]	王智森，王洪海，房媛，等. 移动无线信道的数学仿真计算[J]. 大连工业大学学报， 2009,28(5):370-374.
	WANG Z S , WANG H H , FANG Y , et al. Mathematical simulated calculation of mobile wireless channel[J]. Journal of Dalian Polytechnic University, 2009,28(5):370-374.
[15]	王智森，浦良，潘登，等. 一种窄带信道增益估计方法：201010536771.4[P]. 哈尔滨：哈尔滨工程大学， 2010-11-09.
	WANG Z S , PU L , PAN D , et al. A method of narrowband channel gain estimation:201010536771.1[P]. Harbin: Harbin Engineering University, 2010-11-09.
[16]	BOGDANOVIC M . Frequency domain based LS channel estimation in OFDM based power line communications[J]. Automatika, 2014,55(4):487-494.
[17]	JO J , SOHN I . On the optimality of training signals for MMSEchannel estimation in MIMO-OFDM systems[J]. EURASIP Journal on Wireless Communications and Networking, 2015(1):1-9.
[18]	张建康，穆晓敏，陈恩庆，等. OFDM系统基于导频的信道估计算法分析[J]. 通信技术， 2009,42(8):91-94.
	ZHANG J K , MU X M , CHEN E Q , et al. Pilot-based channel estimation algorithms for OFDM system[J]. Communications Technology, 2009,42(8):91-94.
[19]	AGYAPONG P K , IWAMURA M , STAEHLE D , et al. Design considerations for a 5G network architecture[J]. IEEE Communications Magazine, 2014,52(11):65-75.
[20]	WUNDER G , JUNG P , KASPARICK M , et al. 5GNOW:non-orthogonal,asynchronous waveforms for future mobile applications[J]. IEEE Communications Magazine, 2014,52(2):97-105.
[21]	GE X , ZI R , WANG H , et al. Multi-user massive MIMO communication systems based on irregular antenna arrays[J]. IEEE Transactions on Wireless Communications, 2016,15(8):1-15.
[22]	CHEN S , ZHAO J . The requirements,challenges,and technologies for 5G of terrestrial mobile telecommunication[J]. IEEE Communications Magazine, 2014,52(5):36-43.
[23]	张建敏，谢伟良，杨峰义 . 5G超密集组网网络架构及实现[J]. 电信科学， 2016,32(6):36-43.
	ZHANG J M , XIE W L , YANG F Y . Architecture and solutions of 5G ultra dense network[J]. Telecommunications Science, 2016,32(6):36-43.
[24]	陈山枝 . 发展5G的分析与建议[J]. 电信科学， 2016,32(7):1-10.
	CHEN S Z . Analysis and suggestion of future 5G directions[J]. Telecommunications Science, 2016,32(7):1-10.
[25]	RAPPAPORTTS . Wirelesscommunicationprinciplesandpractice[M]. New York: Prentice Hall, 1996:139-140.

m	期望	均方差
1万	0.002 13	0.002 92
10万	0.001 69	0.002 69
100万	0.001 26	0.002 38
1 000万	0.001 25	0.002 33

m	期望	均方差
1万	0.001 58	0.003 26
10万	0.001 15	0.002 84
100万	0.000 79	0.002 37
1 000万	0.000 78	0.002 32

m	期望	均方差
1万	0.001 88	0.002 78
10万	0.001 44	0.002 55
100万	0.001 26	0.002 33
1 000万	0.001 20	0.002 25

m	期望	均方差
1万	0.001 92	0.005 08
10万	0.001 17	0.004 11
100万	0.001 06	0.003 52
1 000万	0.000 98	0.003 40

m	期望	均方差
1万	0.002 14	0.002 88
10万	0.001 70	0.002 63
100万	0.001 41	0.002 49
1 000万	0.001 30	0.002 44