Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning

WANG Ying; YU Yong-hai; ZHENG Yi; LIN Bin

doi:10.12263/DZXB.20220480

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Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning

PAPERS | 更新时间：2025-12-11

- Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning
- ACTA ELECTRONICA SINICA Vol. 52, Issue 5, Pages: 1496-1505(2024)
- 作者机构：
  
  大连海事大学信息科学技术学院，辽宁大连 116026
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2019YFE0111600);National Natural Science Foundation of China(61971083;51939001)
- DOI：10.12263/DZXB.20220480
  CLC： TN91;
- Received：29 April 2022，
  
  Revised：2023-07-23，
  
  Published：25 May 2024
- 稿件说明：
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王莹,于永海,郑毅,等. 基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测[J]. 电子学报,2024,52(05):1496-1505.

WANG Ying, YU Yong-hai, ZHENG Yi, et al. Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning[J]. Acta Electronica Sinica, 2024, 52(05): 1496-1505.
王莹,于永海,郑毅,等. 基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测[J]. 电子学报,2024,52(05):1496-1505. DOI：10.12263/DZXB.20220480

WANG Ying, YU Yong-hai, ZHENG Yi, et al. Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning[J]. Acta Electronica Sinica, 2024, 52(05): 1496-1505. DOI：10.12263/DZXB.20220480

摘要

针对当前广义频分复用（Generalized Frequency Division Multiplexing，GFDM）系统时变信道估计精度低的问题，提出基于稀疏贝叶斯学习的GFDM系统联合信道估计与符号检测算法.具体地，采用无干扰导频插入的GFDM多重响应信号模型，在稀疏贝叶斯学习框架下，结合期望最大化算法（Expectation-Maximization，EM）和卡尔曼滤波与平滑算法实现块时变信道的最大似然估计；基于信道状态信息的估计值进行GFDM符号检测，并通过信道估计与符号检测的迭代处理逐步提高信道估计与符号检测的精度.仿真结果表明，所提算法能够获得接近完美信道状态信息条件下的误码率性能，且具有收敛速度快、对多普勒频移鲁棒性高等优点.

Abstract

In order to improve the accuracy of time-varying channel estimation in generalized frequency division multiplexing (GFDM) systems

a joint iterative channel estimation and symbol detection algorithm for GFDM systems using sparse Bayesian learning is proposed. Specifically

we use a GFDM multi-response signal model with non-interfering pilot insertion. Under the sparse Bayesian learning framework

we combine the expectation-maximization (EM) algorithm and the Kalman filter and smoothing algorithm to realize the maximum likelihood estimation of the block time-varying channel. Consequently

GFDM symbols are detected based on the estimated channel state information (CSI)

and the accuracy of the channel estimation and symbol detection is progressively improved through the iterative processing of the channel estimation and symbol detection. Simulation results demonstrate that the proposed algorithm can achieve better bit error rate (BER) performance close to that under perfect CSI conditions

and it has the advantages of fast convergence speed and high robustness to Doppler frequency shift.

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references

DE ALMEIDA I B F , MENDES L L , RODRIGUES J J P C , et al . 5G waveforms for IoT applications [J ] . IEEE Communications Surveys & Tutorials , 2019 , 21 ( 3 ): 2554 - 2567 .

LINSALATA F , KUMAR A , MAGARINI M . Design of DGT-based linear and non-linear equalizers for GFDM transmission [J ] . Physical Communication , 2020 , 42 : 101144 .

EHSANFAR S , MATTHE M , ZHANG D , et al . A study of pilot-aided channel estimation in MIMO-GFDM systems [C ] // WSA 2016 ; 20th International ITG Workshop on Smart Antennas . Munich : VDE , 2016 : 1 - 8 .

EHSANFAR S , MATTHE M , ZHANG D , et al . Theoretical analysis and CRLB evaluation for pilot-aided channel estimation in GFDM [C ] // 2016 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE , 2016 : 1 - 7 .

PERMANA A K , HAMID E Y . Performance evaluation of GFDM channel estimation using DFT for tactile Internet application [J ] . Electronics , 2021 , 10 ( 5 ): 595 .

ZENG Y H , SUN S M , WANG Y H , et al . Fast channel estimation for massive machine type Communications1 [C ] // 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) . Piscataway : IEEE , 2022 : 1 - 5 .

VILAIPORNSAWAI U , JIA M . Scattered-pilot channel estimation for GFDM [C ] // 2014 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway : IEEE , 2014 : 1053 - 1058 .

EHSANFAR S , MATTHE M , ZHANG D , et al . Interference-free pilots insertion for MIMO-GFDM channel estimation [C ] // 2017 IEEE Wireless Communications and Networking Conference (WCNC) . Piscataway : IEEE , 2017 : 1 - 6 .

CHEN P C , SU B , HUANG Y . Matrix characterization for GFDM: Low complexity MMSE receivers and optimal filters [J ] . IEEE Transactions on Signal Processing , 2017 , 65 ( 18 ): 4940 - 4955 .

NA Z Y , PAN Z , XIONG M D , et al . Turbo receiver channel estimation for GFDM-based cognitive radio networks [J ] . IEEE Access , 2018 , 6 : 9926 - 9935 .

MOHAMMADIAN A , TELLAMBURA C . Joint channel and phase noise estimation and data detection for GFDM [J ] . IEEE Open Journal of the Communications Society , 2021 , 2 : 915 - 933 .

JEONG J , PARK Y , WEON S , et al . Eigendecomposition-based GFDM for interference-free data transmission and pilot insertion for channel estimation [J ] . IEEE Transactions on Wireless Communications , 2018 , 17 ( 10 ): 6931 - 6943 .

EHSANFAR S , MATTHÉ M , CHAFII M , et al . Pilot- and CP-aided channel estimation in MIMO non-orthogonal multi-carriers [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 1 ): 650 - 664 .

LIU Y J , ZHU X , LIM E G , et al . A semi-blind multiuser SIMO GFDM system in the presence of CFOs and IQ imbalances [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 1 ): 48 - 63 .

ZHANG M J , WEN C K , JIN S , et al . A model-driven deep learning network for quantized GFDM receiver [J ] . Journal of Communications and Information Networks , 2019 , 4 ( 3 ): 53 - 59 .

CUI P F , ZHANG J A , LU W J , et al . Statistical sparse channel modeling for measured and simulated wireless temporal channels [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 12 ): 5868 - 5881 .

张凯 , 于宏毅 , 胡赟鹏 , 等 . 基于EM-SBL迭代的稀疏SIMO信道频域盲均衡算法 [J ] . 电子学报 , 2018 , 46 ( 2 ): 372 - 380 .

ZHANG K , YU H Y , HU B /Y)P , et al . Blind frequency-domain equalization for sparse SIMO channels based on iterative EM-SBL algorithm [J ] . Acta Electronica Sinica , 2018 , 46 ( 2 ): 372 - 380 . (in Chinese)

DING W B , YANG F , PAN C Y , et al . Compressive sensing based channel estimation for OFDM systems under long delay channels [J ] . IEEE Transactions on Broadcasting , 2014 , 60 ( 2 ): 313 - 321 .

LEE H C , GONG C S A , CHEN P Y . A compressed sensing estimation technique for doubly selective channel in OFDM systems [J ] . IEEE Access , 2019 , 7 : 115192 - 115199 .

樊甫华 , 阮怀林 . 低信噪比下非凸化压缩感知超宽带信道估计方法 [J ] . 电子学报 , 2014 , 42 ( 2 ): 353 - 359 .

FAN F H , RUAN H L . Non-convex compressive sensing ultra-wide band channel estimation method in low SNR conditions [J ] . Acta Electronica Sinica , 2014 , 42 ( 2 ): 353 - 359 . (in Chinese)

PRASAD R , MURTHY C R . Bayesian learning for joint sparse OFDM channel estimation and data detection [C ] // 2010 IEEE Global Telecommunications Conference GLOBECOM . Piscataway : IEEE , 2010 : 1 - 6 .

PRASAD R , MURTHY C R , RAO B D . Joint approximately sparse channel estimation and data detection in OFDM systems using sparse Bayesian learning [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 14 ): 3591 - 3603 .

SRIVASTAVA S , KUMAR M S , MISHRA A , et al . Sparse doubly-selective channel estimation techniques for OSTBC MIMO-OFDM systems: A hierarchical Bayesian Kalman filter based approach [J ] . IEEE Transactions on Communications , 2020 , 68 ( 8 ): 4844 - 4858 .

ZHAO L , GAO W J , GUO W B . Sparse Bayesian learning of delay-doppler channel for OTFS system [J ] . IEEE Communications Letters , 2020 , 24 ( 12 ): 2766 - 2769 .

WEI Z Q , YUAN W J , LI S Y , et al . Off-grid channel estimation with sparse Bayesian learning for OTFS systems [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 9 ): 7407 - 7426 .

ZHANG J N , LI Y , NIU K . Iterative channel estimation algorithm based on compressive sensing for GFDM [C ] // 2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) . Piscataway : IEEE , 2016 : 244 - 248 .

SIMEONE O , BAR-NESS Y , SPAGNOLINI U . Pilot-based channel estimation for OFDM systems by tracking the delay-subspace [J ] . IEEE Transactions on Wireless Communications , 2004 , 3 ( 1 ): 315 - 325 .

WIPF D P , RAO B D . An empirical Bayesian strategy for solving the simultaneous sparse approximation problem [J ] . IEEE Transactions on Signal Processing , 2007 , 55 ( 7 ): 3704 - 3716 .

KAY S M . Fundamentals of Statistical Signal Processing [M ] . Englewood Cliffs : Prentice-Hall PTR , 1993 .

GHAHRAMANI Z , HINTON G E . Parameter Estimation for Linear Dynamical Systems [R ] . Toronto : University of Toronto , 1996 .

GASPAR I , MICHAILOW N , NAVARRO A , et al . Low complexity GFDM receiver based on sparse frequency domain processing [C ] // 2013 IEEE 77th Vehicular Technology Conference (VTC Spring) . Piscataway : IEEE , 2013 : 1 - 6 .

International Telecommunication Union (ITU) . Guidelines for evaluation of radio transmission technologies (RTT) for IMT-2000 [EB/OL ] . ( 1997-02-28 )[ 2022-04-29 ] . https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.1225-0-199702-I https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.1225-0-199702-I !!PDF-E .pdf.

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