基于平行因子分析的SIMO-OFDM系統(tǒng)盲信道與符號聯(lián)合估計算法

楊若男; 張偉濤; 樓順天

doi:10.11999/JEIT180432

基于平行因子分析的SIMO-OFDM系統(tǒng)盲信道與符號聯(lián)合估計算法

doi: 10.11999/JEIT180432

西安電子科技大學電子工程學院 ??西安 ??710071

基金項目: 國家自然科學基金(61571339)，陜西省創(chuàng)新人才推進計劃-青年科技新星項目(2018KJXX-019)

詳細信息

作者簡介:
楊若男：女，1993年生，博士生，研究方向為MIMO-OFDM系統(tǒng)盲信道估計

張偉濤：男，1983年生，副教授，碩士生導師，研究方向為盲信號處理、語音信號處理等

樓順天：男，1962年生，教授，博士生導師，研究方向為神經(jīng)網(wǎng)絡信息處理與應用、模糊信息處理與應用、盲信號處理、現(xiàn)代信號智能處理、智能控制技術等

通訊作者:
楊若男　rn_yang@163.com

中圖分類號: TN911.7
計量
- 文章訪問數(shù): 2336
- HTML全文瀏覽量: 1066
- PDF下載量: 81
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-05-07
- 修回日期: 2018-10-25
- 網(wǎng)絡出版日期: 2018-11-19
- 刊出日期: 2019-03-01

Joint Blind Channel Estimation and Symbols Detection for SIMO-OFDM Systems Based on PARAFAC

School of Electronic Engineering, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61571339), The Natural Science Foundation of Shaanxi Province (2018KJXX-019)

摘要

摘要:
針對SIMO-OFDM系統(tǒng)下的信道估計和符號檢測問題，該文建立了接收數(shù)據(jù)矩陣的平行因子分析(PARAFAC)模型，利用PARAFAC模型中離散傅里葉變換矩陣的行滿秩特性，結合數(shù)據(jù)矩陣的奇異值分解，提出了一種信道與符號聯(lián)合盲估計的閉式求解方法。由于提出的求解方法無須進行迭代便可以完成信道估計和符號檢測，因此其計算復雜度低，此外，利用PARAFAC模型實現(xiàn)信道和符號的同時計算，避免了因信道估計誤差導致的符號誤碼率性能下降問題。仿真結果表明，與傳統(tǒng)方法相比提出的方法計算復雜度更低，估計性能更好。
- 盲信道估計 /
- 符號檢測 /
- SIMO-OFDM /
- 平行因子分析
Abstract:
To solve the problem of the joint blind channel estimation and symbol detection for SIMO-OFDM systems, a PARAllel FACtor (PARAFAC) analysis model of the receive data matrix is established. Then, with the full row rank characteristic of the discrete Fourier transform matrix and the singular value decomposition of the receiving data matrix, a closed method is proposed for joint blind channel estimation and symbol detection. The proposed method has low computational complexity because it has no iteration. Furthermore, by the simultaneously calculated of channel and signals, the proposed method can avoid the performance reduction of signal estimation caused by channel estimation error. Simulation results show that the proposed method has lower computational complexity and better estimation performance compared with traditional methods.
- Blind channel estimation /
- Symbols detection /
- SIMO-OFDM /
- PARAllel FACtor (PARAFAC) analysis

HTML全文

圖 1 ALS方法與本文方法的CPU占用時間比較

下載: 全尺寸圖片幻燈片

圖 2 4種方法的信道估計均方誤差與信噪比的關系

下載: 全尺寸圖片幻燈片

圖 3 4種方法的誤碼率與信噪比的關系

下載: 全尺寸圖片幻燈片

表 1 不同參數(shù)下4種方法的計算復雜度

本文方法文獻[8] 文獻[9] 文獻[11]

${L_m} = 3, M = 3, N = 32$ 33056 65536 35840 98304
${L_m} = 3, M = 5, N = 64$ 270144 524288 274432 786432

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參考文獻(15)

ANAS S, ARAFAT A D, ABDALLAH S, et al. One-shot blind channel estimation for OFDM systems over frequency-selective fading channels[J]. IEEE Transactions on Communications, 2017, 65(12): 5445–5458. doi: 10.1109/tcomm.2017.2740925

CHEN Weichang and CHUNG C. Spectrally efficient OFDM pilot waveform for channel estimation[J]. IEEE Transactions on Communications, 2017, 65(1): 387–402. doi: 10.1109/TCOMM.2016.2616859

HAMED H, OMIDI M J, HAMID S S, et al. Joint CFO and channel estimation in OFDM-based massive MIMO systems[C]. IEEE International Symposium on Telecommunications, Tehran, Iran, 2017: 343–348. doi: 10.1109/ISTEL.2016.7881837.

YUAN Zhengdao, ZHANG Chuanzong, WANG Zhongyong, et al. An auxiliary variable-aided hybrid message passing approach to joint channel estimation and decoding for MIMO-OFDM[J]. IEEE Signal Processing Letters, 2017, 24(1): 12–16. doi: 10.1109/LSP.2016.2632180

KIM J G, OH J H, and LIM J T. Subspace-based channel estimation for MIMO-OFDM systems with few received blocks[J]. IEEE Signal Processing Letters, 2012, 19(7): 435–438. doi: 10.1109/LSP.2012.2197201

CHANG Annchen. Using ICA to improve blind subspace-based channel estimation for OFDM system under unknown noise fields[J]. International Journal of Electronics and Communications, 2015, 69(1): 449–454. doi: 10.1016/j.aeue.2014.08.013

FANG Shihhao, CHEN Juya, LIN Jingshiun, et al. Blind channel estimation for CP/CP-free OFDM systems using subspace approach[C]. Proceeding of IEEE Vehicular Technology Conference, Glasgow, UK, 2015: 1–5. doi: 10.1109/VTCSpring.2015.7145717.

GAO Feifei and NALLANATHAN A. Subspace-based blind channel estimation for SISO, MISO and MIMO OFDM systems[C]. IEEE International Conference on Communications, Istanbul, Turkey, 2006: 3025–3030. doi: 10.1109/icc.2006.255268.

GAO Feifei and NALLANATHAN A. Blind channel estimation for OFDM systems via a generalized precoding[J]. IEEE Transactions on Vehicular Technology, 2007, 56(3): 1155–1164. doi: 10.1109/icc.2006.255367

NOH S and SUNG Y. A new precoder design for blind channel estimation in MIMO-OFDM systems[J]. IEEE Transactions on Wireless Communications, 2014, 13(12): 7011–7024. doi: 10.1109/glocom.2013.6831582

ZHANG Xiaofei, WANG Fei, and XU Dazhuan. Blind signal detection algorithm for MIMO-OFDM systems over multipath channel using PARALIND model[J]. IET Communications, 2011, 5(5): 606–611. doi: 10.1049/iet-com.2010.0246

林和昀, 袁超偉, 杜建和. OFDM系統(tǒng)中基于降維PARAFAC模型的信道估計方法[J]. 北京郵電大學學報, 2017, 40(3): 62–66. doi: 10.13190/j.jbupt.2017.03.008

LIN Heyun, YUAN Chaowei, and DU Jianhe. Channel estimation for OFDM systems via reduced-dimensional PARAFAC method[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(3): 62–66. doi: 10.13190/j.jbupt.2017.03.008

黃敏, 李兵兵. 基于整體最小二乘的聯(lián)合信道估計及OFDM信號檢測算法[J]. 電子與信息學報, 2014, 36(6): 1448–1453. doi: 10.3724/SP.J.1146.2013.01327

HUANG Min and LI Bingbing. Joint channel estimation and OFDM signals detection based on total least square[J]. Journal of Electronics &Information Technology, 2014, 36(6): 1448–1453. doi: 10.3724/SP.J.1146.2013.01327

LEE T J and KO Y C. Channel estimation and data detection in the presence of phase noise in MIMO-OFDM systems with independent oscillators[J]. IEEE Access, 2017, 5(99): 9647–9662. doi: 10.1109/ACCESS.2017.2709325

JOMON K C and PRASANTH S. Joint channel estimation and data detection in MIMO-OFDM using distributed compressive sensing[J]. Radioelectronics & Communications Systems, 2017, 60(2): 80–87. doi: 10.3103/s0735272717020029

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