基于图信号处理的OFDM系统导频设计和信道估计方法

doi:10.11959/j.issn.2096-3750.2022.00288

Abstract

Abstract:

Orthogonal frequency division multiplexing (OFDM) is one of the key technologies in the physical layer of the internet of things (IoT).Pilot design and channel estimation are key issues in OFDM systems.In view of the problem of performance loss by fixed pilot pattern due to the complexity and variety of IoT communication scenarios, a pilot design and channel estimation scheme based on graph signal processing (GSP) was proposed.Firstly, the time-frequency resource block was modeled as a graph signal, and the channel estimation problem was reformulated into a sampling and reconstruction problem of the graph signal.Then, considering the influence of time-frequency fading, a weighted graph adjacency matrix was designed to construct a graph topology structure based on the time-frequency position.On this basis, the pilot position is selected based on the graph signal sampling theory, a greedy pilot pattern design algorithm based on weighted graph topology was proposed.At the same time, signal reconstruction was performed based on the graph signal reconstruction method, and a channel estimation method based on the graph smoothness constraint was proposed.Compared with the conventional scheme, simulation results show that the proposed method achieves higher channel estimation accuracy in high-speed scenarios of double selective channels, and effectively reduces pilot overhead in low-speed scenarios.

Key words: orthogonal frequency division multiplexing, graph signal processing, channel estimation, pilot pattern design, graph sampling

CLC Number:

TN929.5

Bin HE, Guobing LI, Yuan CHEN, Guomei ZHANG. Graph signal processing based pilot pattern design and channel estimation for OFDM system[J]. Chinese Journal on Internet of Things, 2022, 6(3): 91-102.

Figures/Tables 6

References 34

[1]	孙其博, 刘杰, 黎羴 ,等. 物联网,概念、架构与关键技术研究综述[J]. 北京邮电大学学报, 2010,33(3): 1-9.
	SUN Q B , LIU J , LI S ,et al. Internet of things,summarize on concepts,architecture and key technology problem[J]. Journal of Beijing University of Posts and Telecommunications, 2010,33(3): 1-9.
[2]	NGUYEN D C , DING M , PATHIRANA P N ,et al. 6G internet of things,a comprehensive survey[J]. IEEE Internet of Things Journal, 2022,9(1): 359-383.
[3]	FAN Y J , LI H , SONG S S ,et al. Structured compressed sensing-based time-frequency joint channel estimation for MIMO-OFDM systems[C]// Proceedings of 2018 13th IEEE Conference on Industrial Electronics and Applications. Piscataway:IEEE Press, 2018: 2006-2010.
[4]	YANG Y W , GAO FF , MA X L ,et al. Deep learning-based channel estimation for doubly selective fading channels[J]. IEEE Access, 2019,7: 36579-36589.
[5]	OH J H , KIM J G , LIM J T . On the design of pilot symbols for OFDM systems over doubly-selective channels[J]. IEEE Communications Letters, 2011,15(12): 1335-1337.
[6]	FENGM M , HONG J . An improved channel estimation algorithm based on DFT in OFDM system[C]// Proceedings of 2020 International Conference on Computer Information and Big Data Applications (CIBDA). Piscataway,IEEE Press, 2020: 321-325.
[7]	ZHAO Y R , ZHAO W J , WANG G P ,et al. AoA-based channel estimation for massive MIMO OFDM communication systems on high speed rails[J]. China Communications, 2020,17(3): 90-100.
[8]	NISSEL R , ADEMAJ F , RUPP M . Doubly-selective channel estimation in FBMC-OQAM and OFDM systems[C]// Proceedings of 2018 IEEE 88th Vehicular Technology Conference. Piscataway,IEEE Press, 2018: 1-5.
[9]	CHEN J C , WEN C K , TING P G . An efficient pilot design scheme for sparse channel estimation in OFDM systems[J]. IEEE Communications Letters, 2013,17(7): 1352-1355.
[10]	REN X , SHAO X F , TAO M X ,et al. Compressed channel estimation for high-mobility OFDM systems,pilot symbol and pilot pattern design[C]// Proceedings of 2015 IEEE International Conference on Communications. Piscataway,IEEE Press, 2015: 4553-4557.
[11]	REN X , CHEN W , TAO M X . Position-based compressed channel estimation and pilot design for high-mobility OFDM systems[J]. IEEE Transactions on Vehicular Technology, 2015,64(5): 1918-1929.
[12]	ABU HASSAN M S , AHMAD R B . Variable pilot channels estimation based on block type and comb-type pilot arrangement in OFDM system[C]// Proceedings of 2016 3rd International Conference on Electronic Design (ICED). Piscataway,IEEE Press, 2016: 99-102.
[13]	NASSER A , MUTA O , ELSABROUTY M . Pilot-assisted sparse channel estimation based on mutual incoherence property[C]// Proceedings of 2019 IEEE 90th Vehicular Technology Conference. Piscataway:IEEE Press, 2019: 1-5.
[14]	XIAO S Y , JIN Z G , CHEN Y ,et al. Joint mutual coherence and total coherence pilot design for OFDM channel estimation[J]. IEEE Access, 7: 144536-144547.
[15]	HE S J , ZHANG Q , QIN J Y . Pilot pattern design for two-dimensional OFDM modulations in time-varying frequency-selective fading channels[J]. IEEE Transactions on Wireless Communications, 2022,21(2): 1335-1346.
[16]	CHEN J Q , ZHANG X , ZHANG P . Bayesian learning for BPSO-based pilot pattern design over sparse OFDM channels[C]// Proceedings of ICC 2020-2020 IEEE International Conference on Communications. Piscataway,IEEE Press, 2020: 1-6.
[17]	SOLTANI M , POURAHMADI V , SHEIKHZADEH H . Pilot pattern design for deep learning-based channel estimation in OFDM systems[J]. IEEE Wireless Communications Letters, 2020,9(12): 2173-2176.
[18]	SANDRYHAILA A , MOURA J M F . Discrete signal processing on graphs[J]. IEEE Transactions on Signal Processing, 2013,61(7): 1644-1656.
[19]	WANG X H , LIU P F , GU Y T . Local-set-based graph signal reconstruction[J]. IEEE Transactions on Signal Processing, 2015,63(9): 2432-2444.
[20]	YANG Y D , ZHANG S , GAO F F ,et al. Graph neural network-based channel tracking for massive MIMO networks[J]. IEEE Communications Letters, 2020,24(8): 1747-1751.
[21]	DAO H T , KIM S . Vertex graph-coloring-based pilot assignment with location-based channel estimation for massive MIMO systems[J]. IEEE Access, 2018,6: 4599-4607.
[22]	HMIDA W H , MEGHDADI V , BOUALLEGUE A ,et al. Graph coloring based pilot reuse among interfering users in cell-free massive MIMO[C]// Proceedings of 2020 IEEE International Conference on Communications Workshops. Piscataway,IEEE Press, 2020: 1-6.
[23]	3GPP. Technical specification group radio access network; evolved universal terrestrial radio access (E-UTRA); physical channels and modulation,TS 36.211[S]. 2019.
[24]	OZDEMIR M K , ARSLAN H . Channel estimation for wireless of dm systems[J]. IEEE Communications Surveys ＆ Tutorials, 2007,9(2): 18-48.
[25]	SHIN C , ANDREWS J G , POWERS E J . An efficient design of doubly selective channel estimation for OFDM systems[J]. IEEE Transactions on Wireless Communications, 2007,6(10): 3790-3802.
[26]	PETER M DJURIC . Cooperative and craph signal processing[M]. Amsterdam,Elsevier, 2018.
[27]	SAKIYAMA A , TANAKA Y , TANAKA T ,et al. Eigen decomposition-free sampling set selection for graph signals[J]. IEEE Transactions on Signal Processing, 2019,67(10): 2679-2692.
[28]	ANIS A , GADDE A , ORTEGA A . Efficient sampling set selection for band limited graph signals using graph spectral proxies[J]. IEEE Transactions on Signal Processing, 2016,64(14): 3775-3789.
[29]	ANIS A , GADDE A , ORTEGA A . Towards a sampling theorem for signals on arbitrary graphs[C]// Proceedings of 2014 IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2014: 3864-3868.
[30]	MAO X H , QIU K , LI T J ,et al. Spatio-temporal signal recovery based on low rank and differential smoothness[J]. IEEE Transactions on Signal Processing, 2018,66(23): 6281-6296.
[31]	STANKOVIC L , MANDIC D , DAKOVIC M ,et al. Graph signal processing,part III,machine learning on graphs,from graph topology to applications[EB]. 2020.
[32]	GOU J , YI Z , DU L ,et al. A local mean-based k-nearest centroid neighbor classifier[J]. The Computer Journal, 2012,55(9): 1058-1071.
[33]	CHENG P , CHEN Z , RUI Y ,et al. Channel estimation for OFDM systems over doubly selective channels,a distributed compressive sensing based approach[J]. IEEE Transactions on Communications, 2013,61(10): 4173-4185.
[34]	REN X , SHAO X F , TAO M X ,et al. Compressed channel estimation for high-mobility OFDM systems,pilot symbol and pilot pattern design[C]// Proceedings of 2015 IEEE International Conference on Communications. Piscataway,IEEE Press, 2015: 4553-4557.

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