脈沖噪聲下基于Renyi熵的分?jǐn)?shù)低階雙模盲均衡算法

馬濟(jì)通; 邱天爽; 李蓉; 夏楠; 李景春

doi:10.11999/JEIT170366

脈沖噪聲下基于Renyi熵的分?jǐn)?shù)低階雙模盲均衡算法

doi: 10.11999/JEIT170366

1.
(大連理工大學(xué)電子信息與電氣工程學(xué)部大連 116024) ②(國家無線電監(jiān)測中心北京 100037)

基金項(xiàng)目:

國家自然科學(xué)基金(61671105, 61139001, 61172108, 81241059)

計(jì)量
- 文章訪問數(shù): 1482
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-04-24
- 修回日期: 2017-07-27
- 刊出日期: 2018-02-19

Dual-mode Blind Equalization Algorithm Based on Renyi Entropy and Fractional Lower Order Statistics Under Impulsive Noise

1.
(Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China)
2.
(State Radio Monitoring Center, Beijing 100037, China)

Funds:

The National Natural Science Foundation of China (61671105, 61139001, 61172108, 81241059)

摘要

摘要: 針對脈沖噪聲下盲均衡器難以快速收斂并有效抑制噪聲的問題，該文提出一種基于Renyi熵的分?jǐn)?shù)低階雙模盲均衡算法。該算法將Renyi熵與分?jǐn)?shù)低階統(tǒng)計(jì)量相結(jié)合并用作代價(jià)函數(shù)來更新盲均衡器權(quán)向量，利用Renyi熵提高算法的收斂速度，利用分?jǐn)?shù)低階統(tǒng)計(jì)量增強(qiáng)算法對脈沖噪聲的抑制能力。為了提升系統(tǒng)穩(wěn)健性，該文進(jìn)一步提出雙閾值加權(quán)判決法，通過設(shè)置雙閾值并引入非線性加權(quán)函數(shù)，使得兩種代價(jià)函數(shù)之間的切換更為平滑。在不同脈沖性噪聲、不同信道環(huán)境下進(jìn)行仿真實(shí)驗(yàn)，結(jié)果表明，該文算法既能有效抑制脈沖噪聲，又具有較快的收斂速度。
- 脈沖噪聲 /
- 盲均衡 /
- Renyi熵 /
- 分?jǐn)?shù)低階統(tǒng)計(jì)量
Abstract: To improve the convergence speed and noise suppression effects of blind equalizer under impulsive noise environment, a new dual-mode blind equalization algorithm based on Renyi entropy and fractional lower order statistics is presented. Renyi entropy and fractional lower order statistics are combined as cost functions to update the weight coefficients of the equalizer in this method, which can improve the convergence speed and enhance the ability of suppressing impulse noise. In addition, considering the robustness of system, a double-threshold based weighting decision method is proposed. By setting double thresholds and a nonlinear weighting function, the switching between two cost functions become smooth. Simulation experiments are carried out under different impulse noise and different channel conditions. The results show that the algorithm converges faster and suppresses impulse noise effectively at the same time.
- Impulsive noise /
- Blind equalization /
- Renyi entropy /
- Fractional lower order statistics

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