基于貝葉斯自動(dòng)相關(guān)性確定的稀疏重構(gòu)正交頻分復(fù)用信號時(shí)延估計(jì)算法
doi: 10.11999/JEIT181181
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信息工程大學(xué)信息系統(tǒng)工程學(xué)院 ??鄭州 ??450001
基金項(xiàng)目: 國家自然科學(xué)基金(61401513)
Sparse Reconstruction OFDM Delay Estimation Algorithm Based on Bayesian Automatic Relevance Determination
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Institute of Information System Engineering, The Information Engineering University, Zhengzhou 450001, China
Funds: The National Natural Science Foundation of China (61401513)
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摘要: 針對復(fù)雜環(huán)境下，單測量矢量(SMV)條件下的正交頻分復(fù)用(OFDM)時(shí)延估計(jì)問題，該文提出了一種基于貝葉斯自動(dòng)相關(guān)性確定(BARD)的稀疏重構(gòu)時(shí)延估計(jì)算法。該算法運(yùn)用貝葉斯框架，從進(jìn)一步挖掘有用信息的角度入手，引入不對稱的自動(dòng)相關(guān)性確定(ARD)先驗(yàn)，融入?yún)?shù)估計(jì)過程中，有效提升了低信噪比(SNR)和SMV條件下的時(shí)延估計(jì)精度。該算法首先基于OFDM信號物理層協(xié)議數(shù)據(jù)單元估計(jì)出的信道頻域響應(yīng)構(gòu)造稀疏化實(shí)數(shù)域表示模型，然后對模型中的噪聲和稀疏系數(shù)矢量進(jìn)行概率假設(shè)，同時(shí)引入自動(dòng)相關(guān)性確定先驗(yàn)；最后根據(jù)貝葉斯框架，通過期望最大化(EM)算法求解超參數(shù)，實(shí)現(xiàn)對時(shí)延的估計(jì)。仿真實(shí)驗(yàn)表明，該算法具有更好的估計(jì)性能，在信噪比較高時(shí)更加貼近克拉美羅界(CRB)。同時(shí)基于通用軟件無線電外設(shè)(USRP)，利用實(shí)際信號對所提算法進(jìn)行了有效性地驗(yàn)證。
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關(guān)鍵詞:
- 時(shí)延估計(jì) /
- 神經(jīng)網(wǎng)絡(luò) /
- 自動(dòng)相關(guān)性確定(ARD) /
- 通用軟件無線電外設(shè)(USRP)
Abstract: Considering the problem of Orthogonal Frequency Division Multiplexing (OFDM) signal delay estimation with only a Single Measurement Vector (SMV) in a complex environment, a sparse reconstruction time delay estimation algorithm based on Bayesian Automatic Relevance Determination (BARD) is proposed. The Bayesian framework is used to start from the perspective of further mining useful information, and asymmetric Automatic Relevance Determination(ARD) priori is introduced to integrate into the parameter estimation process, which improves the accuracy of time delay estimation under SMV and low Signal-to-Noise Ratio (SNR) conditions. Firstly, a sparse real-domain representation model is constructed based on the estimated frequency domain response of the OFDM signal physical layer protocol data unit. Then, probability hypothesis for the noise and sparse coefficient vectors are made in the model, and Automatic Relevance Determination (ARD) prior is introduced. Finally, according to the Bayesian framework, the Expectation Maximization (EM) algorithm is used to solve the hyperparameters to estimate the delay. The simulation experiments show that the proposed algorithm has better estimation performance and is closer to the Cramér–Rao Bound (CRB). At the same time, based on the Universal Software Radio Peripheral (USRP), the effectiveness of the proposed algorithm is verified by the actual signal. -
表 1 OFDM系統(tǒng)參數(shù)設(shè)置
參數(shù) 數(shù)值 FFT周期${T_{{\rm{FFT}}}}(\mu s)$ 3.2 系統(tǒng)帶寬$B({\rm{MHz}})$ 20 子載波數(shù)(個(gè)) 64 載波頻率${f_{\rm{c}}}{\rm{(GHz}})$ 2.4 
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表 2 各種算法時(shí)延估計(jì)結(jié)果比較(ns)
算法 多徑序號 1 2 3 4 均值 RMSE 均值 均值 均值 PM 218.40 10.81 270.93 302.16 308.16 CoSaMP 211.00 10.57 262.33 287.50 298.06 MFOCUSS 204.16 4.50 258.16 287.66 307.20 BARD 201.53 1.17 253.00 283.00 314.32
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