【摘要】：在無線通信中,由于通信信道復(fù)雜多變而導(dǎo)致的失真和有限帶寬所引起的碼間干擾(Inter-symbol Interference, ISI)嚴(yán)重影響通信質(zhì)量,因此在通信系統(tǒng)的接收端需要適當(dāng)?shù)难a(bǔ)償這些因素所帶來的影響。由于盲均衡技術(shù)不需要發(fā)送訓(xùn)練序列,節(jié)省了帶寬,因此可以有效地克服碼間干擾和提高通信速度。在信號(hào)處理領(lǐng)域中,盲均衡技術(shù)已經(jīng)成為一個(gè)研究熱點(diǎn)。本文主要針對(duì)傳統(tǒng)盲均衡算法收斂速度慢、穩(wěn)態(tài)誤差大的缺陷,利用DNA遺傳算法和正交小波變換等手段,對(duì)盲均衡算法的均衡性能進(jìn)行優(yōu)化,研究內(nèi)容主要包括以下幾個(gè)方面：(1)提出了基于DNA遺傳優(yōu)化的常模盲均衡算法。傳統(tǒng)盲均衡算法采用最速下降法對(duì)均衡器權(quán)向量進(jìn)行更新,然而最速下降法要求代價(jià)函數(shù)必須滿足連續(xù)、可導(dǎo)的條件,并且容易陷入局部極值,從而導(dǎo)致盲均衡算法收斂速度慢,穩(wěn)態(tài)誤差大。因此,針對(duì)這些缺點(diǎn),利用DNA遺傳算法的全局搜索能力對(duì)均衡器的權(quán)向量進(jìn)行優(yōu)化,避免常模盲均衡算法出現(xiàn)局部收斂,改善了盲均衡算法的均衡性能。仿真結(jié)果表明了該算法的有效性。(2)提出了基于禁忌搜索的自適應(yīng)雙鏈DNA遺傳優(yōu)化常模盲均衡算法。由于在一般的DNA遺傳算法中,種群的交叉操作的概率都是定值,不能隨種群變化而做適當(dāng)?shù)恼{(diào)整,因此傳統(tǒng)的DNA遺傳算法搜索效率不高。另外,DNA遺傳算法在對(duì)問題最優(yōu)解進(jìn)行搜索時(shí),有時(shí)會(huì)出現(xiàn)重復(fù)搜索,同樣影響了算法的搜索效率。因此針對(duì)這些缺陷,結(jié)合禁忌搜索算法,同時(shí)采用DNA遺傳算法中的交叉操作概率隨種群進(jìn)化代數(shù)變化的方法,形成了基于禁忌搜索的自適應(yīng)雙鏈DNA遺傳優(yōu)化常模盲均衡算法。(3)提出了基于禁忌搜索的自適應(yīng)雙鏈DNA遺傳優(yōu)化多模盲均衡算法。針對(duì)傳統(tǒng)的多模盲均衡算法對(duì)高階多模調(diào)制信號(hào)均衡效果不好的問題,采用正交小波變換,將基于禁忌搜索的自適應(yīng)雙鏈DNA遺傳算法應(yīng)用到多模盲均衡算法中,從而形成了基于禁忌搜索的自適應(yīng)雙鏈DNA遺傳優(yōu)化多模盲均衡算法。由于基于禁忌搜索的自適應(yīng)雙鏈DNA遺傳算法具有更快的搜索效率和更好局部搜索特性,因此,將DNA遺傳算法應(yīng)用到多模盲均衡算法中,能夠顯著地提高多模盲均衡算法的性能。(4)提出了基于多種群進(jìn)化的禁忌搜索DNA遺傳優(yōu)化小波分?jǐn)?shù)間隔多模盲均衡算法。為了進(jìn)一步提高DNA遺傳算法的搜索效率和小波分?jǐn)?shù)間隔多模盲均衡算法的均衡效果,利用禁忌搜索算法和多種群進(jìn)化策略,對(duì)DNA遺傳算法進(jìn)行改進(jìn),并且將改進(jìn)后的算法應(yīng)用到小波分?jǐn)?shù)間隔多模盲均衡算法中,從而形成了基于多種群進(jìn)化的禁忌搜索DNA遺傳優(yōu)化小波分?jǐn)?shù)間隔多模盲均衡算法。該算法將DNA種群分為多個(gè)子種群,每個(gè)子種群側(cè)重于不同的搜索目的,即主種群側(cè)重于局部搜索,輔助種群側(cè)重于全局搜索,并且在三個(gè)種群中分別采用禁忌交叉操作和不同的變異操作,提高了DNA遺傳算法的搜索能力,從而改善了小波分?jǐn)?shù)間隔多模盲均衡算法的性能。
[Abstract]:In wireless communication, inter-symbol interference (ISI) due to the complexity of communication channel and the inter-symbol interference (ISI) caused by the limited bandwidth seriously affect the communication quality, and therefore, it is necessary to compensate the influence of these factors at the receiving end of the communication system. Since the blind equalization technique does not need to send a training sequence, the bandwidth is saved, so that inter-code interference and communication speed can be effectively overcome. Blind equalization has become a hot topic in the field of signal processing. Aiming at the defects of slow convergence speed and large steady-state error of traditional blind equalization algorithm, the equalization performance of blind equalization algorithm is optimized by means of DNA genetic algorithm and orthogonal wavelet transform. (1) A norm blind equalization algorithm based on DNA genetic optimization is proposed. The traditional blind equalization algorithm is used to update the equalizer weight vector by steepest descent method. However, the steepest descent method requires that the cost function must satisfy continuous and guide conditions, and can easily fall into local extreme value, leading to the slow convergence speed and large steady-state error of the blind equalization algorithm. Therefore, aiming at these disadvantages, the weight vector of the equalizer is optimized by using the global search capability of the DNA genetic algorithm so as to avoid local convergence of the norm blind equalization algorithm and improve the equalization performance of the blind equalization algorithm. The simulation results show the validity of the algorithm. (2) An adaptive double-stranded DNA genetic optimization norm blind equalization algorithm based on tabu search is proposed. Because of the common DNA genetic algorithm, the probability of cross operation of the population is fixed value, and can not be properly adjusted with the population change, so the traditional DNA genetic algorithm search efficiency is not high. In addition, when searching the optimal solution of the problem, the DNA genetic algorithm sometimes repeats the search, and also influences the search efficiency of the algorithm. Therefore, the adaptive double-stranded DNA genetic optimization norm blind equalization algorithm based on tabu search is formed by combining the tabu search algorithm and the method of crossover operation probability in DNA genetic algorithm with the evolution of population evolution. (3) An adaptive double-stranded DNA genetic optimization multi-mode blind equalization algorithm based on tabu search is proposed. Aiming at the problem that the traditional multi-mode blind equalization algorithm has poor equalization effect on the high-order multi-mode modulation signal, the self-adaptive double-stranded DNA genetic algorithm based on the tabu search is applied to the multi-mode blind equalization algorithm by adopting orthogonal small wave transformation, thereby forming a self-adaptive double-stranded DNA genetic optimization multi-mode blind equalization algorithm based on tabu search. Because the self-adaptive double-stranded DNA genetic algorithm based on tabu search has faster search efficiency and better local search characteristics, the DNA genetic algorithm is applied to the multi-mode blind equalization algorithm, and the performance of the multi-mode blind equalization algorithm can be significantly improved. (4) A multi-mode blind equalization algorithm based on multi-group evolution is proposed to search DNA genetic optimization small-wave fractional interval multi-mode blind equalization. In order to further improve the search efficiency of DNA genetic algorithm and the equalization effect of small-wave fractional-interval multi-mode blind equalization algorithm, using tabu search algorithm and multi-group evolution strategy, the DNA genetic algorithm is improved. and applying the improved algorithm to the small-wave fractional-interval multi-mode blind equalization algorithm, thereby forming a tabu search DNA genetic optimization small wave fractional interval multi-mode blind equalization algorithm based on a plurality of group evolution. The algorithm divides the DNA population into a plurality of sub-populations, each sub-population focuses on different search purposes, namely, the main population is focused on local search, the auxiliary population is focused on global search, and taboos cross operation and different mutation operations are respectively adopted in the three populations, improves the search capability of the DNA genetic algorithm, thereby improving the performance of the small-wave fractional-interval multi-mode blind equalization algorithm.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN911.5

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本文編號(hào)：2263828