基于構(gòu)造代價(jià)函數(shù)求解的自同步擾碼盲識(shí)別方法

韓樹楠; 張旻; 李歆昊

doi:10.11999/JEIT171026

基于構(gòu)造代價(jià)函數(shù)求解的自同步擾碼盲識(shí)別方法

doi: 10.11999/JEIT171026

韓樹楠^{*張旻李歆昊},
張旻,
李歆昊

基金項(xiàng)目:

國家自然科學(xué)基金(61602491)

計(jì)量
- 文章訪問數(shù): 1322
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-11-02
- 修回日期: 2018-03-23
- 刊出日期: 2018-08-19

A Blind Identification Method of Self-synchronous Scramblers Based on Optimization of Established Cost Function

HAN Shunan^{*張旻李歆昊},
ZHANG Min,
LI Xinhao

Funds:

The National Natural Science Foundation of China (61602491)

摘要

摘要: 由于卷積碼序列的0,1 bit的概率幾乎均衡，對(duì)于卷積碼自同步加擾的擾碼盲識(shí)別，現(xiàn)有的基于輸入序列0, 1 bit概率不均衡性的識(shí)別方法均已失效，為此該文提出一種新的自同步擾碼盲識(shí)別方法。首先將卷積碼自同步加擾序列進(jìn)行分塊處理，通過加擾數(shù)據(jù)塊與卷積碼校驗(yàn)向量相乘產(chǎn)生新的序列；然后以最大化新生成序列間線性約束關(guān)系成立概率為準(zhǔn)則，利用解調(diào)輸出的軟判決序列建立自同步擾碼反饋多項(xiàng)式系數(shù)的代價(jià)函數(shù)；最后根據(jù)自同步擾碼反饋多項(xiàng)式的項(xiàng)數(shù)特點(diǎn)，在求解代價(jià)函數(shù)時(shí)改進(jìn)了動(dòng)態(tài)搜索煙花算法，增加了對(duì)煙花個(gè)體元素值的約束操作，由求解出的參量值識(shí)別出自同步擾碼反饋多項(xiàng)式。仿真實(shí)驗(yàn)驗(yàn)證了所提方法的有效性，該方法無需遍歷搜索反饋多項(xiàng)式，且具有較好的魯棒性，所需數(shù)據(jù)量小，隨著數(shù)據(jù)量的增大和擾碼階數(shù)的降低，其識(shí)別正確率逐漸提高。
- 自同步擾碼 /
- 卷積碼 /
- 反饋多項(xiàng)式 /
- 校驗(yàn)向量 /
- 煙花算法
Abstract: Since the probability bias between 0 and 1 bit in a convolutional code sequence is very small, the existing method based on the probability bias in the input sequence is ineffective for the identification of a self-synchronous scrambler placed after a convolutional encoder. To solve this problem, a novel method for the blind identification of a self-synchronous scrambler is proposed. First, the scrambled convolutional code sequence is divided into blocks, and a new bit sequence is generated, in which each bit is the dot product of a scrambled bit block with a parity check vector of the convolutional code. Second, based on the criteria of maximizing the probability that the linear equations in the generated bits hold, the cost function of the feedback polynomial coefficients of the self-synchronous scrambler is established using the soft decision sequence, which is the output of the demodulator. Third, according to the characteristic of the number of terms in the feedback polynomial, the dynamic fireworks algorithm is modified by constraining the values of elements in fireworks, and the cost function is optimized using the modified dynamic fireworks algorithm. Simulation experiments show the effectiveness of the proposed algorithm. There is no need to search for the feedback polynomial exhaustively in the proposed algorithm. It is robust to the noise and the number of data required is small. Moreover, along with the increase of the number of received data or the decrease of the order of the feedback polynomial, the correct identification ratio of the proposed method increases.
- Self-synchronous scrambler /
- Convolutional code /
- Feedback polynomial /
- Parity check vector /
- Fireworks algorithm

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