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
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上海海事大學(xué)信息工程學(xué)院 上海 201306
Channel Estimation Algorithm of Maritime Sparse Channel Based on Fast Bayesian Matching Pursuit Optimization
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College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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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。
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關(guān)鍵詞:
- 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.
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Key words:
- Channel estimation /
- Bayesian criterion /
- Sparse channel /
- Matching pursuit
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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) 下載: 導(dǎo)出CSV
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} 下載: 導(dǎo)出CSV
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 下載: 導(dǎo)出CSV
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