本文選題：物聯(lián)網(wǎng)稀疏信號　切入點：壓縮感知　出處：《浙江大學(xué)》2017年博士論文

【摘要】：物聯(lián)網(wǎng)被視為繼計算機、互聯(lián)網(wǎng)之后世界信息產(chǎn)業(yè)發(fā)展的第三次浪潮,應(yīng)用前景巨大。其中信息感知與傳輸在物聯(lián)網(wǎng)應(yīng)用中起著至關(guān)重要的作用。但由于噪音及干擾等因素的影響,物聯(lián)網(wǎng)中低功耗節(jié)點之間的數(shù)據(jù)傳輸并不可靠,而傳統(tǒng)的信源、信道編碼通常為節(jié)點端引入額外的能量開銷及復(fù)雜的計算。如何在不可靠傳輸及資源受限的情況下,快速準(zhǔn)確地傳輸原始信息是一個亟待解決的問題�？紤]到物聯(lián)網(wǎng)中眾多的原始信息具有稀疏性,本文將壓縮感知理論應(yīng)用于資源受限物聯(lián)網(wǎng)的稀疏信號傳輸體系中以提高網(wǎng)絡(luò)性能。其主要研究內(nèi)容包括以下部分:1.針對有損鏈路過渡區(qū)范圍廣而數(shù)據(jù)傳輸并不可靠的問題,本文利用信號固有的稀疏特性,將有損傳輸過程中的數(shù)據(jù)丟失模擬為隨機壓縮采樣。接收端通過接收到的部分?jǐn)?shù)據(jù)即可通過重構(gòu)算法恢復(fù)原始稀疏信號。進(jìn)一步考慮到長數(shù)據(jù)包傳輸導(dǎo)致的塊狀丟包不利于信號重構(gòu),我們在節(jié)點端進(jìn)行交織以隨機化數(shù)據(jù)丟包,保證了信號的傳輸性能。實驗結(jié)果表明,該方法大大拓寬了有損鏈路空間可用范圍,且相對于傳統(tǒng)的數(shù)據(jù)丟失重傳-插值方法能夠有效減少能耗并提高重構(gòu)精度。2.針對若干傳感器節(jié)點信道接入問題,本文利用感知信息的結(jié)構(gòu)化稀疏特性,提出了結(jié)構(gòu)化稀疏信號隨機信道接入�？紤]到傳統(tǒng)多量測向量模型中投影矩陣構(gòu)造不適用于該場景下,我們將多量測向量問題轉(zhuǎn)化為單量測向量問題。并在節(jié)點端引入感知概率的概念以控制通信量,通過聯(lián)合考慮隨機信道接入及信號重構(gòu)需求,求得最優(yōu)感知概率以最小化系統(tǒng)傳輸能耗。3.針對現(xiàn)有的壓縮數(shù)據(jù)收集耗能較大的問題,本文提出了基于分簇結(jié)構(gòu)的最稀疏壓縮數(shù)據(jù)收集。相對于現(xiàn)有的數(shù)據(jù)收集方法,該機制中每個量測值只需一個節(jié)點的數(shù)據(jù),大大減少了傳輸量。注意到數(shù)據(jù)傳輸可通過調(diào)節(jié)功率直接傳輸或中間節(jié)點轉(zhuǎn)發(fā)兩種方式,本文分別對其能耗建模以進(jìn)行比較。并在上述能耗模型下獲取最優(yōu)簇大小。該方法能夠以較少的能耗進(jìn)行壓縮感知下的數(shù)據(jù)收集,且節(jié)點故障時魯棒性較好。4.針對壓縮感知應(yīng)用中稠密投影矩陣導(dǎo)致高感知及傳輸能耗的問題,本文提出一種簡單易構(gòu)造的稀疏高斯矩陣。通過理論分析,我們證明了該矩陣每行只需少數(shù)高斯隨機數(shù)即可滿足約束等距性,保證重構(gòu)算法高精度恢復(fù)原始稀疏信號。相對于傳統(tǒng)的隨機矩陣,該矩陣能夠在節(jié)點端消耗較少時間及內(nèi)存的同時保證壓縮重構(gòu)性能。進(jìn)一步以有損鏈路下稀疏信號傳輸為例說明該矩陣的優(yōu)勢。
[Abstract]:The Internet of things is regarded as the third wave of the world's information industry after computers and the Internet. The application prospect is huge. The information perception and transmission play an important role in the Internet of things application. However, due to the influence of noise and interference, the data transmission between the low-power nodes in the Internet of things is not reliable, and the traditional information source. Channel coding usually introduces additional energy overhead and complex calculations for the node. Fast and accurate transmission of original information is an urgent problem. Considering the sparsity of many original information in the Internet of things, In this paper, the theory of compressed sensing is applied to the sparse signal transmission system of resource-constrained Internet of things to improve the network performance. The main research contents include the following parts: 1. Aiming at the problem that the lossy link transition area is wide and the data transmission is unreliable, In this paper, we use the inherent sparse characteristic of the signal. The data loss during lossy transmission is simulated as random compression sampling. The receiver can recover the original sparse signal by reconstructing some of the received data. Further consideration is given to the block caused by long packet transmission. Packet loss is not conducive to signal reconstruction, In order to ensure the transmission performance of the signal, we interleaved at the node end to randomize the data packet loss. The experimental results show that the proposed method greatly broadens the available range of lossy link space. Compared with the traditional data loss retransmission interpolation method, it can effectively reduce the energy consumption and improve the reconstruction accuracy. 2. Aiming at the channel access problem of some sensor nodes, this paper uses the structured sparse characteristic of perceptual information. The structured sparse signal random channel access is proposed. Considering that the projection matrix construction in the traditional multi-measurement vector model is not suitable for this scenario, We transform the multi-measurement vector problem into the single-measure vector problem, and introduce the concept of perceptual probability to control the traffic at the node end, and consider the requirements of random channel access and signal reconfiguration. The optimal perceptual probability is obtained to minimize the transmission energy consumption. 3. Aiming at the problem that the current compressed data collection consumes a lot of energy, this paper proposes the most sparse compressed data collection based on clustering structure. Compared with the existing data collection methods, In this mechanism, only one node is required for each measurement, which greatly reduces the amount of transmission. In this paper, the energy consumption is modeled for comparison, and the optimal cluster size is obtained under the above energy consumption model. This method can collect data under compressed sensing with less energy consumption. In view of the problem that dense projection matrix leads to high sensing and transmission energy consumption in compressed sensing applications, a simple and easy to construct sparse Gao Si matrix is proposed. It is proved that this matrix needs only a few Gao Si random numbers per row to satisfy the constraint equidistance, which ensures that the reconstruction algorithm can restore the original sparse signal with high accuracy. The matrix can consume less time and memory at the node end and ensure the compression and reconstruction performance. Further, the sparse signal transmission in the lossy link is taken as an example to illustrate the advantage of the matrix.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP391.44;TN929.5

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