動態(tài)頻譜認知無線通信關(guān)鍵技術(shù)研究
發(fā)布時間：2018-11-18 17:20
【摘要】：認知無線電的關(guān)鍵技術(shù)可以概括為頻譜感知、頻譜共享和頻譜管理三個方面。本文重點研究頻譜共享方面的星地協(xié)同頻率選擇技術(shù),頻譜管理方面的頻譜數(shù)據(jù)壓縮、頻譜數(shù)據(jù)挖掘和混沌序列預(yù)測技術(shù),最后設(shè)計了一種衛(wèi)星移動通信場景中的動態(tài)頻譜協(xié)同認知無線通信系統(tǒng)架構(gòu)。本文工作主要有以下四個方面： 1、針對認知無線通信中感知頻譜信息交互存在瓶頸的問題,本文提出了一種適用于感知頻譜的數(shù)據(jù)壓縮技術(shù),大幅縮減了回傳的數(shù)據(jù)量。通過分析頻譜數(shù)據(jù)的特性和應(yīng)用傳統(tǒng)數(shù)據(jù)壓縮技術(shù)存在的缺陷,本文在DCT變換的基礎(chǔ)上,通過能量檢測結(jié)果將頻譜數(shù)據(jù)劃分為噪聲和信號兩部分,并分別采用不同的壓縮方案,提高了壓縮效能。在此基礎(chǔ)上,本文繼續(xù)深入分析頻譜特性,根據(jù)頻帶的鄰域相似性提出一種基于信號識別的分段頻譜數(shù)據(jù)壓縮算法,改善了DCT變換的低頻能量聚焦性,提高了壓縮比。仿真實驗結(jié)果表明分段壓縮在絕大部分場景下能同時帶來壓縮比和失真率兩方面的增益。 2、本文在分析頻譜數(shù)據(jù)特性的基礎(chǔ)上,針對海量頻譜數(shù)據(jù)挖掘復(fù)雜度大的問題提出了一種基于增量運算的頻譜數(shù)據(jù)挖掘方法。運用增量運算的思想,將新回傳數(shù)據(jù)的挖掘信息與已有信息庫進行融合,不需要每次都進行全局挖掘,有效降低了運算量。針對頻譜數(shù)據(jù)具有多維特性、稀疏性、非連續(xù)性和多變性的特點,本文所提挖掘方法對多項信道質(zhì)量指標進行了統(tǒng)計分析,提取頻譜變化的規(guī)律性信息。通過利用預(yù)測技術(shù),從挖掘所得信息中生成可靠的頻率圖譜輔助衛(wèi)星進行頻率選擇決策。 3、深入研究了混沌時間序列預(yù)測技術(shù),在傳統(tǒng)支持向量機預(yù)測技術(shù)的基礎(chǔ)上,設(shè)計了3種場景下利用數(shù)據(jù)特性優(yōu)化預(yù)測模型的技術(shù)方案。首先研究了理論混沌系統(tǒng)時間序列預(yù)測技術(shù),提出了一種基于迭代誤差補償?shù)腖SSVM混沌時間序列預(yù)測算法,算法的預(yù)測精度相對現(xiàn)有算法提高一個數(shù)量級以上。其次對隨機性較強的小尺度網(wǎng)絡(luò)流量預(yù)測技術(shù)進行了研究,提出了一種基于相關(guān)分析的局域LSSVM小尺度網(wǎng)絡(luò)流量預(yù)測算法。算法通過相關(guān)分析優(yōu)化預(yù)測模型訓練集,有效提高了預(yù)測模型的預(yù)測精度,并減少了運算量。最后研究了規(guī)律性較強的電力負荷預(yù)測,提出了一種基于K-means分類的電力負荷LSSVM預(yù)測算法,取得了較好的多步預(yù)測效果。 4、在衛(wèi)星移動通信的場景下,提出一種星地協(xié)同的認知無線通信系統(tǒng)架構(gòu)設(shè)計方案。衛(wèi)星認知終端與控制中心協(xié)同認知,系統(tǒng)利用控制中心豐富的軟硬件資源對終端回傳的海量頻譜數(shù)據(jù)進行數(shù)據(jù)挖掘得到頻譜變化的規(guī)律性信息,結(jié)合認知終端即時感知的頻譜環(huán)境數(shù)據(jù),實現(xiàn)了認知功能的智能化。
[Abstract]:The key technologies of cognitive radio can be summarized as spectrum sensing, spectrum sharing and spectrum management. This paper focuses on the space-ground cooperative frequency selection technology in spectrum sharing, spectrum data compression, spectrum data mining and chaotic sequence prediction in spectrum management. Finally, a dynamic spectrum cooperative cognitive wireless communication system architecture in mobile satellite communication scene is designed. The main work of this paper is as follows: 1. Aiming at the bottleneck of spectrum information interaction in cognitive wireless communication, this paper proposes a data compression technology suitable for sensing spectrum, which greatly reduces the amount of data returned. By analyzing the characteristics of spectrum data and the shortcomings of traditional data compression technology, this paper divides the spectrum data into two parts, noise and signal, on the basis of DCT transform, and adopts different compression schemes. The compression efficiency is improved. On this basis, this paper further analyzes the spectrum characteristics, and proposes a segmented spectrum data compression algorithm based on signal recognition according to the neighborhood similarity of the frequency band, which improves the low frequency energy focusing of the DCT transform and increases the compression ratio. Simulation results show that segmented compression can bring both compression ratio and distortion gain in most scenarios. 2. On the basis of analyzing the characteristics of spectrum data, a method of spectrum data mining based on incremental operation is proposed in this paper. By using the idea of incremental operation, the mining information of the newly transmitted data is fused with the existing information base, and the global mining is not needed every time, which effectively reduces the computation cost. Aiming at the characteristics of multi-dimension, sparsity, discontinuity and variability of spectrum data, the mining methods proposed in this paper are used to analyze the channel quality indexes and extract the regular information of spectrum variation. By using the prediction technique, a reliable frequency map aided satellite is generated from the information obtained from the mining to make the frequency selection decision. 3. The chaotic time series prediction technology is deeply studied. Based on the traditional support vector machine (SVM) prediction technology, the technical scheme of optimizing the prediction model using the data characteristics under three scenarios is designed. Firstly, the time series prediction technology of theoretical chaotic system is studied, and a LSSVM chaotic time series prediction algorithm based on iterative error compensation is proposed. The prediction accuracy of the algorithm is more than one order of magnitude higher than that of the existing algorithms. Secondly, the small scale network traffic prediction technology with strong randomness is studied, and a local LSSVM small scale network traffic prediction algorithm based on correlation analysis is proposed. The algorithm optimizes the training set of prediction model by correlation analysis, which can effectively improve the prediction accuracy and reduce the computation cost. In the end, a new power load LSSVM forecasting algorithm based on K-means classification is proposed, which has a good multi-step forecasting effect. 4. In the scenario of satellite mobile communication, a design scheme of satellite-ground cooperative cognitive wireless communication system is proposed. The satellite cognitive terminal and the control center cooperate in cognition. The system uses the abundant software and hardware resources of the control center to mine the massive spectrum data returned by the terminal to obtain the regular information of the spectrum change. The intelligence of cognitive function is realized by combining the spectrum environment data of cognitive terminal instant perception.
【學位授予單位】：北京郵電大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN925
本文編號：2340643
[Abstract]:The key technologies of cognitive radio can be summarized as spectrum sensing, spectrum sharing and spectrum management. This paper focuses on the space-ground cooperative frequency selection technology in spectrum sharing, spectrum data compression, spectrum data mining and chaotic sequence prediction in spectrum management. Finally, a dynamic spectrum cooperative cognitive wireless communication system architecture in mobile satellite communication scene is designed. The main work of this paper is as follows: 1. Aiming at the bottleneck of spectrum information interaction in cognitive wireless communication, this paper proposes a data compression technology suitable for sensing spectrum, which greatly reduces the amount of data returned. By analyzing the characteristics of spectrum data and the shortcomings of traditional data compression technology, this paper divides the spectrum data into two parts, noise and signal, on the basis of DCT transform, and adopts different compression schemes. The compression efficiency is improved. On this basis, this paper further analyzes the spectrum characteristics, and proposes a segmented spectrum data compression algorithm based on signal recognition according to the neighborhood similarity of the frequency band, which improves the low frequency energy focusing of the DCT transform and increases the compression ratio. Simulation results show that segmented compression can bring both compression ratio and distortion gain in most scenarios. 2. On the basis of analyzing the characteristics of spectrum data, a method of spectrum data mining based on incremental operation is proposed in this paper. By using the idea of incremental operation, the mining information of the newly transmitted data is fused with the existing information base, and the global mining is not needed every time, which effectively reduces the computation cost. Aiming at the characteristics of multi-dimension, sparsity, discontinuity and variability of spectrum data, the mining methods proposed in this paper are used to analyze the channel quality indexes and extract the regular information of spectrum variation. By using the prediction technique, a reliable frequency map aided satellite is generated from the information obtained from the mining to make the frequency selection decision. 3. The chaotic time series prediction technology is deeply studied. Based on the traditional support vector machine (SVM) prediction technology, the technical scheme of optimizing the prediction model using the data characteristics under three scenarios is designed. Firstly, the time series prediction technology of theoretical chaotic system is studied, and a LSSVM chaotic time series prediction algorithm based on iterative error compensation is proposed. The prediction accuracy of the algorithm is more than one order of magnitude higher than that of the existing algorithms. Secondly, the small scale network traffic prediction technology with strong randomness is studied, and a local LSSVM small scale network traffic prediction algorithm based on correlation analysis is proposed. The algorithm optimizes the training set of prediction model by correlation analysis, which can effectively improve the prediction accuracy and reduce the computation cost. In the end, a new power load LSSVM forecasting algorithm based on K-means classification is proposed, which has a good multi-step forecasting effect. 4. In the scenario of satellite mobile communication, a design scheme of satellite-ground cooperative cognitive wireless communication system is proposed. The satellite cognitive terminal and the control center cooperate in cognition. The system uses the abundant software and hardware resources of the control center to mine the massive spectrum data returned by the terminal to obtain the regular information of the spectrum change. The intelligence of cognitive function is realized by combining the spectrum environment data of cognitive terminal instant perception.
【學位授予單位】：北京郵電大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN925
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