基于快速貝葉斯匹配追蹤優(yōu)化的海上稀疏信道估計(jì)方法

張穎; 姚雨豐

doi:10.11999/JEIT190102

基于快速貝葉斯匹配追蹤優(yōu)化的海上稀疏信道估計(jì)方法

doi: 10.11999/JEIT190102

張穎^,,
姚雨豐

上海海事大學(xué)信息工程學(xué)院上海 201306

基金項(xiàng)目: 國家自然科學(xué)基金(61673259)

詳細(xì)信息

作者簡介:
張穎：男，1968年生，博士，教授，博士生導(dǎo)師，研究方向?yàn)槲锫?lián)網(wǎng)、海事無線通信、無線自組織網(wǎng)絡(luò)

姚雨豐：男，1995年生，碩士生，研究方向?yàn)楹Ｊ聼o線通信信道估計(jì)、無線信號傳輸技術(shù)

通訊作者:
張穎　yingzhang@shmtu.edu.cn

中圖分類號: TN911
計(jì)量
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-02-21
- 修回日期: 2019-09-01
- 網(wǎng)絡(luò)出版日期: 2019-09-06
- 刊出日期: 2020-02-19

Channel Estimation Algorithm of Maritime Sparse Channel Based on Fast Bayesian Matching Pursuit Optimization

Ying ZHANG^,,
Yufeng YAO

College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Funds: The National Natural Science Foundation of China (61673259)

摘要

摘要:
正交頻分復(fù)用(OFDM)系統(tǒng)中，由于頻率發(fā)生選擇性衰落會導(dǎo)致信道在數(shù)據(jù)傳輸中產(chǎn)生符號間干擾，因此接收機(jī)往往需要知道信道狀態(tài)信息。而在海上通信的情況下，信道傳輸會受到多種外界因素的干擾，往往需要預(yù)先進(jìn)行信道探測估計(jì)。為了提高估計(jì)性能，該文提出一種基于奇異值分解優(yōu)化觀測矩陣的快速貝葉斯匹配追蹤稀疏信道估計(jì)優(yōu)化算法(FBMPO)，該算法不僅能夠充分考慮海上通信的信道稀疏性，也能夠降低信道的不確定性帶來的影響。計(jì)算機(jī)仿真實(shí)驗(yàn)表明，與傳統(tǒng)的信道估計(jì)算法相比，該算法能夠提高信道估計(jì)的精確度。
- 信道估計(jì) /
- 貝葉斯準(zhǔn)則 /
- 稀疏信道 /
- 匹配追蹤
Abstract:
In the Orthogonal Frequency Division Multiplexing (OFDM) system, the receiver often needs to know the channel state information, because the frequency selective fading channel will generate inter-symbol interference in the data transmission. In the case of maritime communication, the method of channel estimation is often needed to detect the channel subjected to the interference of various external factors. In order to improve the estimation performance, the Fast Bayesian Matching Pursuit based on singular-value-decomposition for Optimizing observation matrix (FBMPO) is proposed, which fully considers not only the sparse channel of maritime communication, but also reduces the influence of uncertainty of the unpredictable channel. Computer simulation shows, compared with traditional channel estimation algorithms, the proposed algorithm can effectively improve the accuracy of channel estimation.
- Channel estimation /
- Bayesian criterion /
- Sparse channel /
- Matching pursuit

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圖 1 海上通信損耗模型

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圖 2 N為32時，p₁為0.04時，3種算法的AMSE對比

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圖 4 N為64時，p₁為0.04時，3種算法的AMSE對比

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圖 5 N為32時，p₁為0.01時，3種算法的AMSE對比

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圖 3 N為48時，p₁為0.04時，3種算法的AMSE對比

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圖 6 N為32時，p₁為0.04時，3種算法的BER對比

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圖 8 N為64時，p₁為0.04時，3種算法的BER對比

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圖 9 N為32時，p₁為0.01時，3種算法的BER對比

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圖 7 N為48時，p₁為0.04時，3種算法的BER對比

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表 1 FBMPO算法的偽代碼

FBMPO算法
輸入：參數(shù)向量s, 觀測矩陣${{\varphi } }_i$，迭代閾值K, R and L；
輸出：${\tilde h_{ {\rm{MMSE} } } }$;
(1) Initialize ${\mu _{0,1}}$ by式(20)
(2) for i ← 1 to L:
(3) 　　${{}_i} \leftarrow {{{\varphi}} ^{ - 1}}{{{\phi}} _i};\;{{{\beta }}_i} \leftarrow {\left( {1 + {\sigma _1}^2{{\phi}} _i^{\rm{T}}{{}_i}} \right)^{ - 1}}$;
(4) 　　${\mu _{1,i} }^* \leftarrow {\mu _{0,1} } + \dfrac{1}{2}\lg \left( {\frac{ { { {{\beta} } _i} } }{ { {\sigma _1}^2} } } \right) + \dfrac{1}{2}{ {{\beta} } _i}{\left\| { { {{y} }^{\rm{T} } }{ { }_i} } \right\|^2}$ 　　　　　　　 $ + {\rm{lg} }\dfrac{ { {p_1} } }{ {1 - {p_1} } }$;
(5) end for
(6) for q ← 1 to K:
(7) 　　${\mu _{1,q}} \leftarrow {\mu _{1,i}}^$; ${\rm{}}{b_{1,q}}^{\left( 1 \right)} \leftarrow {\mu _{1,i}}^$; ${\rm{}}{c_{1,q}}^{\left( 1 \right)} \leftarrow {c_{1,i}}^$; 　　　　　　${\beta _{1,q}}^{\left( 1 \right)} \leftarrow {\beta _{1,i}}^$;
(8) end for
(9) ${{{\phi}}_i} \leftarrow {{{U}}_1} {{W}_2} {{{V}}_1}^{\rm T}$; ${{{\phi}} _i}' \leftarrow {{{U}}_1}{{{W}}_2}'{{{V}}_1}^{\rm{T}}$;
(10) for l ← 1 to R:
(11) 　　${{{\beta}} _i} \leftarrow {\left( {1 + {\sigma _1}^2{{{\phi}} _i}{{'}^{\rm{T}}}{{}_i}} \right)^{ - 1}}$;
(12) 　　${{{\mu}} _i} \leftarrow {\mu ^{\left( {l - 1} \right)}} + \dfrac{1}{2}{\rm{lg}}{{{\beta}} _i} + \dfrac{1}{2}{{{\beta}} _i}{\left( {{{{s}}^{\rm{T}}}c_i^{\left( l \right)}} \right)^2} $ 　　　　　　 $ + {\rm{lg}}\frac{{{p_1}}}{{1 - {p_1}}}$;
(13) 　　$i_*^{\left( l \right)} \leftarrow {\rm{argma}}{{\rm{x}}_i}{\mu _i}$;
(14)　　 ${G^{\left( l \right)}} \leftarrow {G^{\left( {l - 1} \right)}} \cup ^{\{i_{}^{(l)}\}} $; 　　　　　　 $c_i^{\left( {l + 1} \right)} \leftarrow c_i^{\left( l \right)} - {{i}}_{i_^{\left( l \right)}}^{\left( l \right)}{{{\beta }}_{i_^{\left( l \right)}}}{{i}}_{i_^{\left( l \right)}}^{{{\left( l \right)}^{\rm{T}}}}{{{\phi}} _i}$;
(15) end for
(16) 計(jì)算${\tilde h_{ {\rm{MMSE} } } }$ by式(30)

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表 2 系統(tǒng)仿真參數(shù)設(shè)置

參數(shù)仿真	參數(shù)值
信道抽頭數(shù)系統(tǒng)信道帶寬	6410 MHz
采樣頻率循環(huán)前綴長度	10 MHz16
調(diào)制方式	BPSK
非零抽頭概率 p₁	{0.04,0.01}
FFT/IFFT點(diǎn)數(shù)	1024
訓(xùn)練序列長度	{32,48,64}

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表 3 不同算法在不同訓(xùn)練序列時的運(yùn)算時間(s)

	N=32	N=48	N=64
OMP	6.4284	8.0413	11.4591
BCS	18.2541	20.8931	24.5212
FBMPO	11.4618	13.7194	15.0951

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