本文選題：深空通信　切入點：Spinal碼　出處：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：隨著航天技術(shù)的發(fā)展,世界各國越來越重視深空探測。而深空通信在深空探測任務(wù)中起著關(guān)鍵作用。但深空環(huán)境的大尺度距離跨度、高動態(tài)、傳播環(huán)境特性復(fù)雜等特征,對深空數(shù)據(jù)信息的高質(zhì)、高效、高可靠實時傳輸提出了極大挑戰(zhàn)。除了傳統(tǒng)的應(yīng)對方式(增加接收/發(fā)射天線尺寸、提高載波、增加發(fā)射功率等)之外,高效的信道編譯碼技術(shù)和合理的傳輸機制也起著關(guān)鍵的作用。新近提出的Spinal碼是一種全新的、在BSC(二進(jìn)制對稱信道)、AWGN(加性高斯白噪聲信道)上均能實現(xiàn)近容量限傳輸?shù)臒o速率編碼方式,相比傳統(tǒng)的高增益固定速率編碼(如LDPC碼),Spinal碼在極寬的信噪比范圍內(nèi)尤其是低信噪比情況下均獲得了更好的性能,同時編譯碼復(fù)雜度遠(yuǎn)低于傳統(tǒng)的高增益固定速率編碼。鑒于此,本文將Spinal碼引入深空通信中,并重點結(jié)合應(yīng)用層數(shù)據(jù)壓縮、傳輸層數(shù)據(jù)糾刪和數(shù)據(jù)鏈路層/物理層Spinal編碼以及LTP文件傳輸協(xié)議,設(shè)計了一種面向DTN(Delay/Disruption Tolerant Network)協(xié)議�？蚣艿目鐚勇�(lián)合傳輸機制,以實現(xiàn)深空數(shù)據(jù)信息的可靠、高效傳輸。本文首先利用Markov預(yù)測和反饋信息實現(xiàn)了發(fā)送策略的動態(tài)調(diào)整,從而不等待反饋,持續(xù)發(fā)送數(shù)據(jù)。然后,本文建立了一種跨層聯(lián)合優(yōu)化模型。以探測圖像為例,對應(yīng)用層的圖像壓縮、傳輸層的數(shù)據(jù)糾刪以及數(shù)據(jù)鏈路層/物理層的Spinal編碼進(jìn)行聯(lián)合優(yōu)化,使得傳輸每一幅圖像所需的符號數(shù)最小。在應(yīng)用層和傳輸層,利用了信息與應(yīng)用數(shù)學(xué)領(lǐng)域近年來新提出的壓縮感知(Compressed Sensing,CS)技術(shù)進(jìn)行圖像壓縮。與傳統(tǒng)壓縮相比,CS壓縮編碼復(fù)雜度低,可以實現(xiàn)高效率壓縮,同時,CS壓縮還具有潛在的糾刪功能,因此將CS應(yīng)用到應(yīng)用層的圖像壓縮和傳輸層的數(shù)據(jù)糾刪中。最后,在基于Spinal碼的Markov預(yù)測和跨層聯(lián)合優(yōu)化模型的基礎(chǔ)上設(shè)計了一種跨層聯(lián)合優(yōu)化傳輸機制,使每次傳輸所發(fā)送的編碼符號數(shù)最少,在傳輸過程中不等待反饋,收到反饋時處理反饋,根據(jù)反饋信息和Markov預(yù)測決定下一時刻的發(fā)送策略,從而持續(xù)發(fā)送圖像數(shù)據(jù)。通過對吞吐量的仿真分析,將理想狀態(tài)的收發(fā)無延時交互傳輸機制、本文所用的跨層聯(lián)合優(yōu)化傳輸機制和基于反饋重傳的傳輸機制等機制作為對比,結(jié)果表明在延時巨大、誤碼率非常高的深空通信中,跨層聯(lián)合傳輸機制接近于收發(fā)無延時交互傳輸機制,高于預(yù)測重傳機制6.5%,比無預(yù)測追加機制高13.9%,比無預(yù)測重傳機制高20%。
[Abstract]:With the development of space technology, countries in the world pay more and more attention to deep space exploration. Deep space communication plays a key role in deep space exploration missions. It poses a great challenge to the high quality, high efficiency and high reliability of real-time transmission of deep space data information. In addition to the traditional response methods (increasing the size of receiving / transmitting antennas, increasing carrier, increasing transmission power, etc.), Efficient channel coding and decoding technology and reasonable transmission mechanism also play a key role. Recently proposed Spinal code is a new one. In BSC (binary symmetric channel) AWGN (additive Gao Si white noise channel) can realize near-capacity limited transmission rate free coding. Compared with the traditional high gain fixed rate code (such as LDPC code / Spinal code), it has better performance in a wide range of signal-to-noise ratio (SNR), especially in the case of low signal-to-noise ratio (SNR). At the same time, the complexity of encoding and decoding is much lower than that of traditional high gain fixed-rate coding. In view of this, Spinal code is introduced into deep space communication, and combined with application layer data compression. Data erasure in transport layer, Spinal coding in data link layer / physical layer and LTP file transfer protocol, a cross-layer joint transmission mechanism for DTN(Delay/Disruption Tolerant Network) protocol stack framework is designed to realize the reliability of deep space data information. In this paper, the Markov prediction and feedback information are used to realize the dynamic adjustment of the transmission strategy, so that the data can be continuously transmitted without waiting for feedback. Then, a cross-layer joint optimization model is established, and the detection image is taken as an example. Image compression in application layer, data erasure in transmission layer and Spinal coding in data link layer / physical layer are jointly optimized to minimize the number of symbols needed to transmit each image. In this paper, the compressing sensing CS (compressed sensing) technique proposed in recent years in the field of information and applied mathematics is used for image compression. Compared with traditional compression, CS compression has lower complexity and can achieve high efficiency compression. At the same time, CS compression also has potential erasure function. Therefore, CS is applied to the application layer image compression and data erasure in the transmission layer. Finally, based on the Markov prediction and cross-layer joint optimization model based on Spinal code, a cross-layer joint optimization transmission mechanism is designed. The number of coded symbols transmitted in each transmission is minimized, and the feedback is processed when the feedback is received, and the transmission strategy at the next moment is determined according to the feedback information and the Markov prediction. Through the simulation and analysis of the throughput, we compare the ideal state transmission mechanism, the cross-layer joint optimization transmission mechanism and the feedback retransmission mechanism. The results show that in the deep space communication with huge delay and high bit error rate, the cross-layer joint transmission mechanism is close to the transceiver and non-delay interactive transmission mechanism, which is higher than the predictive retransmission mechanism 6.5, 13.9 higher than the non-predictive supplementary mechanism, and 20 times higher than the non-predictive retransmission mechanism.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN927.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 劉嘉興;;向技術(shù)極限挑戰(zhàn)——深空測控通信的目標(biāo)[J];電訊技術(shù);2008年04期

2 肖甫;王汝傳;葉曉國;孫力娟;;衛(wèi)星網(wǎng)絡(luò)傳輸控制協(xié)議研究[J];南京郵電大學(xué)學(xué)報(自然科學(xué)版);2009年03期

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本文編號：1680123