本文選題：數(shù)能一體化網(wǎng)絡(luò) + 能量收集��； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：迅猛發(fā)展的無(wú)線通信技術(shù)同時(shí)也帶來(lái)了日益突出的能量消耗問(wèn)題,數(shù)能一體化無(wú)線通信網(wǎng)絡(luò)(Data and Energy Integrated Communication Networks, DEINs)的提出旨在解決傳統(tǒng)無(wú)限通信系統(tǒng)中的能量消耗問(wèn)題,在一定程度上實(shí)現(xiàn)無(wú)線通信的節(jié)能減排。本文基于數(shù)能一體化網(wǎng)絡(luò)相關(guān)研究背景和相應(yīng)的網(wǎng)絡(luò)服務(wù)指標(biāo)(Quality of Service, QoS),主要從兩個(gè)方面對(duì)數(shù)能一體化網(wǎng)絡(luò)展開(kāi)研究,即數(shù)能一體化網(wǎng)絡(luò)的資源分配和傳輸策略。本文首先介紹了數(shù)能一體化無(wú)線通信網(wǎng)絡(luò)的研究現(xiàn)狀和相關(guān)技術(shù),主要包括能量收集技術(shù)、數(shù)能一體傳輸技術(shù)、數(shù)能一體化網(wǎng)絡(luò)中的資源分配以及數(shù)能一體化網(wǎng)絡(luò)與其他具體網(wǎng)絡(luò)的融合應(yīng)用。能量收集技術(shù)和數(shù)能一體傳輸技術(shù)是數(shù)能一體化網(wǎng)絡(luò)的技術(shù)基礎(chǔ)。本文著重描述了目前數(shù)能一體化網(wǎng)絡(luò)關(guān)于資源分配問(wèn)題,數(shù)能一體化網(wǎng)絡(luò)中的資源分配策略需要考慮無(wú)線傳輸能量的影響因素。然后,本文研究了關(guān)于數(shù)能一體化網(wǎng)絡(luò)中上行傳輸數(shù)據(jù)、下行傳輸數(shù)據(jù)和能量的傳輸決策問(wèn)題�；谝话愕木W(wǎng)絡(luò)系統(tǒng)模型,采用馬爾科夫決策過(guò)程分析系統(tǒng)模型,在滿足上行傳輸和下行傳輸在一定數(shù)據(jù)包丟失率約束下,建立了關(guān)于系統(tǒng)吞吐量最大回報(bào)的最優(yōu)決策分析模型。通過(guò)求解模型,得到某一個(gè)系統(tǒng)狀態(tài)下系統(tǒng)各個(gè)決策的概率。最后仿真說(shuō)明了相關(guān)系統(tǒng)狀態(tài)下的各個(gè)決策的概率,并分析對(duì)比了一般策略下和最優(yōu)策略下系統(tǒng)吞吐量,說(shuō)明了最優(yōu)策略的性能。最后,本文針對(duì)數(shù)能一體傳輸中的資源分配展開(kāi)研究�；趥鹘y(tǒng)的蜂窩網(wǎng)絡(luò),考慮下行數(shù)能一體傳輸和上行傳輸數(shù)據(jù)的系統(tǒng)模型,通過(guò)下行的能量傳輸來(lái)滿足上行傳輸數(shù)據(jù)所需要的能量,建立了以系統(tǒng)下行吞吐量為優(yōu)化目標(biāo),滿足上行數(shù)據(jù)傳輸所需能量的上下行聯(lián)合優(yōu)化模型。另外,在多用戶的系統(tǒng)中,考慮公平性因素,建立最大化最小模型。通過(guò)對(duì)模型的求解仿真,描述了系統(tǒng)對(duì)于資源的分配結(jié)果,并通過(guò)仿真分析說(shuō)明了公平性因素的影響。
[Abstract]:The rapid development of wireless communication technology also brings more and more prominent energy consumption problem. The purpose of data and Energy Integrated Communication Networks, DEINs) is to solve the problem of energy consumption in traditional infinite communication system. To a certain extent, to achieve wireless communication energy saving and emission reduction. Based on the related research background of digital energy integration network and the corresponding network service index, quality of Service, QoS, this paper focuses on two aspects of logarithmic energy integration network, that is, the resource allocation and transmission strategy of digital energy integrated network. Firstly, this paper introduces the research status and related technologies of digital energy integrated wireless communication network, mainly including energy collection technology, digital energy integrated transmission technology. The resource allocation in the digital energy integration network and the fusion application of the digital energy integration network with other specific networks. Energy collection technology and digital energy transmission technology are the technical foundation of digital energy integrated network. In this paper, the problem of resource allocation in digital energy integrated networks is described. The influence factors of wireless transmission energy need to be considered in the resource allocation strategy of digital energy integration networks. Then, this paper studies the transmission decision of uplink data, downlink transmission data and energy in digital energy integrated network. Based on the general network system model, the Markov decision process analysis system model is used to satisfy the uplink transmission and downlink transmission with a certain packet loss rate constraint. An optimal decision analysis model for the maximum return of system throughput is established. By solving the model, the probability of each decision in a system state is obtained. Finally, the simulation shows the probability of each decision under the relevant system state, and analyzes and compares the system throughput under the general strategy and the optimal strategy, and explains the performance of the optimal strategy. Finally, this paper studies the resource allocation in digital-energy transmission. Based on the traditional cellular network, considering the downlink digital transmission and uplink data transmission system model, the downlink energy transmission is used to satisfy the energy needed for uplink transmission data, and the system downlink throughput is established as the optimization goal. Uplink and downlink joint optimization model to satisfy the energy required for uplink data transmission. In addition, in multi-user systems, the fairness factor is taken into account, and the maximum minimum model is established. The results of resource allocation of the system are described through the simulation of the model, and the influence of fairness factors is explained by simulation.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN92

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本文編號(hào)：1925072