本文選題：異構(gòu)蜂窩網(wǎng)絡(luò)　切入點(diǎn)：小蜂窩　出處：《國(guó)防科學(xué)技術(shù)大學(xué)》2017年博士論文　論文類型：學(xué)位論文

【摘要】：隨著無(wú)線智能終端的普及和移動(dòng)應(yīng)用的快速發(fā)展,人們要求現(xiàn)有的基礎(chǔ)設(shè)施架構(gòu)既能大幅地提升網(wǎng)絡(luò)的容量,又能顯著地降低網(wǎng)絡(luò)的能耗。通過(guò)在現(xiàn)有的宏蜂窩網(wǎng)絡(luò)中部署低發(fā)射功率的小基站,構(gòu)建異構(gòu)蜂窩網(wǎng)絡(luò)(Heterogeneous Cellular Network,HCN),可以快速有效地增強(qiáng)對(duì)用戶的覆蓋,并提升整個(gè)蜂窩網(wǎng)絡(luò)的容量。但是,考慮到動(dòng)態(tài)流量和頻譜復(fù)用,異構(gòu)蜂窩網(wǎng)絡(luò)中存在著復(fù)雜的同頻干擾和負(fù)載失衡問(wèn)題,這對(duì)異構(gòu)蜂窩網(wǎng)絡(luò)的頻譜效率和能量效率的提升帶來(lái)了嚴(yán)峻的挑戰(zhàn)。本文針對(duì)異構(gòu)蜂窩網(wǎng)絡(luò)的規(guī)劃部署和資源管理問(wèn)題展開(kāi)了研究,以頻譜效率或能量效率為目標(biāo)對(duì)小基站的部署規(guī)劃、基站和用戶的連接關(guān)系(User Association,用戶關(guān)聯(lián))以及下行鏈路傳輸過(guò)程中的資源分配問(wèn)題進(jìn)行了建模,并給出了優(yōu)化解決方案。主要工作和意義包括:針對(duì)現(xiàn)實(shí)場(chǎng)景中由于用戶活動(dòng)所造成的空間流量動(dòng)態(tài)變化的問(wèn)題,本文采用了一種基于隨機(jī)幾何的統(tǒng)計(jì)學(xué)方法建立不同流量形態(tài)的模型,該方法可以有效地模擬真實(shí)場(chǎng)景中的用戶分布。在此基礎(chǔ)上,本文提出了一種在傳統(tǒng)的宏蜂窩網(wǎng)絡(luò)中部署小基站并且根據(jù)不同的流量形態(tài)對(duì)基站狀態(tài)進(jìn)行規(guī)劃的算法,可以在保證服務(wù)質(zhì)量的前提下有效地減少小基站的部署數(shù)量,并且基站狀態(tài)能夠動(dòng)態(tài)適應(yīng)空間流量的變化。首先,在指定部署區(qū)域盡可能稠密地預(yù)設(shè)好小基站部署點(diǎn),在初始狀態(tài)下假設(shè)在每個(gè)位置上都部署有一個(gè)活動(dòng)的小基站。其次,對(duì)每一個(gè)流量形態(tài),通過(guò)不斷迭代更新基站(包括宏基站和小基站)狀態(tài)和用戶關(guān)聯(lián)逐步關(guān)閉冗余活動(dòng)基站,直到活動(dòng)基站的數(shù)量不再減少,從而獲得與流量形態(tài)對(duì)應(yīng)的基站狀態(tài)可行解。對(duì)于同一流量形態(tài),有可能得到多個(gè)基站狀態(tài)可行解。因此,本文最后以最小化部署的基站數(shù)為目標(biāo),對(duì)每一個(gè)流量形態(tài)從各自的可行解的集合中選取了一個(gè)最優(yōu)解,將最終部署的小基站表示為在所有流量形態(tài)下的活動(dòng)小基站的并集。當(dāng)流量形態(tài)發(fā)生變化時(shí),可以參照得到的最優(yōu)基站狀態(tài)對(duì)基站進(jìn)行狀態(tài)控制(切換到活動(dòng)或睡眠狀態(tài)),以滿足在不同流量形態(tài)下的服務(wù)需求。仿真結(jié)果證明了本方法可以在保障用戶服務(wù)質(zhì)量的前提下有效地降低異構(gòu)蜂窩網(wǎng)絡(luò)部署成本,并提高系統(tǒng)的能量效率。針對(duì)部署"小宏共存"的異構(gòu)蜂窩網(wǎng)絡(luò)后用戶仍傾向與宏基站關(guān)聯(lián)而造成網(wǎng)絡(luò)負(fù)載不均衡的缺陷,本文提出了一種基于多點(diǎn)傳輸(用戶和多個(gè)基站關(guān)聯(lián))的用戶關(guān)聯(lián)算法對(duì)蜂窩網(wǎng)絡(luò)中的負(fù)載進(jìn)行調(diào)控,以實(shí)現(xiàn)整個(gè)異構(gòu)蜂窩網(wǎng)絡(luò)的負(fù)載均衡。首先,本文將保證負(fù)載均衡的用戶關(guān)聯(lián)問(wèn)題建模為最大化效用目標(biāo)函數(shù)的問(wèn)題,該問(wèn)題是一個(gè)NP難的混合整數(shù)規(guī)劃問(wèn)題。為了求解這一問(wèn)題,本文將基站與用戶之間的一對(duì)一關(guān)聯(lián)松弛為分?jǐn)?shù)階用戶關(guān)聯(lián),即允許每個(gè)用戶和多個(gè)基站關(guān)聯(lián),不但將原本難以求解的混合整數(shù)規(guī)劃問(wèn)題轉(zhuǎn)化成了凸優(yōu)化問(wèn)題,也可以獲得目標(biāo)函數(shù)的上界。其次,本文證明了采用等額資源分配能夠獲得長(zhǎng)期的實(shí)際最優(yōu)性能,這個(gè)結(jié)論使得問(wèn)題得到了進(jìn)一步的簡(jiǎn)化。再次,本文通過(guò)采用對(duì)偶分解的方法,提出了一種高效且低復(fù)雜度的迭代算法,該算法能保證以最大的步長(zhǎng)收斂到最優(yōu)解。該算法對(duì)基站和用戶的分布比較敏感,以致于該算法必須不斷地重復(fù)運(yùn)行以跟蹤網(wǎng)絡(luò)的動(dòng)態(tài)變化,具體實(shí)現(xiàn)起來(lái)復(fù)雜度較高。因此,本文研究了通過(guò)簡(jiǎn)單設(shè)置偏置因子調(diào)控小區(qū)覆蓋范圍,從而實(shí)現(xiàn)負(fù)載均衡的問(wèn)題,并給出了偏置因子的設(shè)計(jì)方法。實(shí)驗(yàn)結(jié)果表明,采用這兩類方法均可以實(shí)現(xiàn)基站之間的負(fù)載均衡,采用基于多點(diǎn)傳輸?shù)乃惴ǹ梢垣@得最優(yōu)的性能,而采用基于偏置的方法可以獲得接近最優(yōu)的性能。針對(duì)異構(gòu)蜂窩網(wǎng)絡(luò)的信號(hào)傳輸過(guò)程中存在的同頻干擾問(wèn)題,本文提出了一種基于圖的結(jié)合干擾協(xié)調(diào)的資源分配算法。該算法通過(guò)動(dòng)態(tài)地分配子信道和功率,可以有效抑制異構(gòu)蜂窩網(wǎng)絡(luò)的干擾,進(jìn)而提升整個(gè)網(wǎng)絡(luò)的頻譜效率。首先,通過(guò)判斷各小區(qū)之間的鄰居關(guān)系將小區(qū)劃分為小區(qū)簇,對(duì)每個(gè)小區(qū)簇采取獨(dú)立的干擾協(xié)調(diào)和資源分配,以降低問(wèn)題的復(fù)雜度。其次,利用用戶聚類算法將每個(gè)小區(qū)簇中的用戶劃分為用戶簇,以最小化用戶簇內(nèi)的干擾。最后,本文采取了一種比例公平的方法對(duì)每個(gè)小區(qū)簇中的用戶簇進(jìn)行子信道分配,在此基礎(chǔ)上,采用注水法對(duì)每個(gè)小區(qū)基站的發(fā)射功率進(jìn)行分配。實(shí)驗(yàn)結(jié)果表明,本文所提出的基于圖的干擾協(xié)調(diào)算法能取得網(wǎng)絡(luò)頻譜效率達(dá)到了最優(yōu)性能的95%,并且相對(duì)于已有的算法和不考慮干擾協(xié)調(diào)的算法有較大的性能提升,該算法具有很低的復(fù)雜度,更加適合于實(shí)時(shí)應(yīng)用的場(chǎng)景。針對(duì)能量受限的異構(gòu)蜂窩網(wǎng)絡(luò)場(chǎng)景,本文提出了一種保證能量效率的資源分配算法,通過(guò)聯(lián)合優(yōu)化對(duì)用戶的子信道和功率的分配,進(jìn)一步提升異構(gòu)蜂窩網(wǎng)絡(luò)的能量效率。首先,將資源分配問(wèn)題建模為一個(gè)以考慮服務(wù)質(zhì)量的能量效率(QoS-aware Energy Efficiency,QEE)為目標(biāo)函數(shù)的最優(yōu)化問(wèn)題。為了求解該問(wèn)題,本文采用Dinkelbach方法消除了目標(biāo)函數(shù)的分式,將問(wèn)題轉(zhuǎn)化為以能量效率為參數(shù)的規(guī)劃問(wèn)題,并用迭代方法來(lái)更新該參數(shù)。在每一次迭代過(guò)程中,對(duì)于固定的能量效率參數(shù),問(wèn)題可以被看作混合整數(shù)規(guī)劃問(wèn)題,將該問(wèn)題轉(zhuǎn)化為對(duì)偶形式并采用次梯度搜索方法可以獲得最優(yōu)的子信道和功率分配。然后,根據(jù)最優(yōu)的子信道和功率分配結(jié)果來(lái)更新能量效率參數(shù),并將該參數(shù)值用于下一次迭代,直到參數(shù)值收斂為止,收斂后的參數(shù)值即為最優(yōu)的能量效率,對(duì)應(yīng)的子信道和功率分配為最優(yōu)資源分配方案。實(shí)驗(yàn)證明了該算法可以有效地提升系統(tǒng)的能量效率,算法中的參數(shù)值更新過(guò)程和次梯度搜索都可以在較少的迭代次數(shù)之內(nèi)收斂。
[Abstract]:With the rapid development of the popularity of wireless intelligent terminal and mobile applications, people require existing infrastructure can greatly enhance the network capacity, and can significantly reduce the energy consumption of the network. Through the small base station deployment of low transmitting power in the macrocell network existing in the construction of heterogeneous cellular network (Heterogeneous Cellular Network, HCN), can quickly and effectively enhance the coverage of users, and enhance the capacity of cellular networks. However, considering the dynamic flow and spectrum reuse, complicated with frequency interference and load imbalance problem of heterogeneous cellular network, the heterogeneous cellular network spectrum efficiency and energy efficiency improvement has brought severe challenges the planning for the deployment. Heterogeneous cellular network and resource management issues to study, spectrum efficiency and energy efficiency as the Department of planning target of small base stations, base Connection between the station and the users (User Association, user association) resource allocation and downlink transmission in the process of modeling, and optimization solution is proposed. The main work includes: the dynamic changes and significance of space flow according to the reality of the scene due to user activity caused by the problem, this paper proposes a set of different flow statistical methods based on random geometric shape model, this method can effectively simulate the distribution of users in real scenes. On this basis, this paper proposes a in traditional macrocell network deployment of small base station and according to different traffic patterns to the planning of the base station state algorithm, in the premise of guaranteeing the service quality to effectively reduce the number of the deployment of a small base station, and the base station can adapt the dynamic change of space flow. First, as specified in the deployment area Can preset dense small base station deployment, assuming small base station has a activities are deployed at each position in the initial state. Secondly, for each flow pattern, by iteratively updating base station (including Acer station and small base station) state and user association gradually shutting down redundant activities until the number of base stations, activities the base station is no longer reduced, so as to obtain the corresponding base station state and flow form. The feasible solution for the same flow form, may have been more base state of feasible solution. Therefore, the number of base stations at the end of this paper is to minimize the deployment is the goal for each flow form from each feasible solution set in the selection of a the optimal solution will be the final deployment of small base station said the union is a small base in all activities under the form of flow. When the flow form changes, the optimal state of base station can refer to the state of the base station Control (switch to active or sleep state), to meet the different traffic patterns in the service demand. The simulation results show that this method can guarantee the service quality of the users effectively reduce heterogeneous cellular network deployment cost, and improve the energy efficiency of the system. According to the deployment of the "small macro heterogeneous cellular network coexistence" after users still tend to associated with the macro base station caused by network load imbalance defects, this paper proposes a transmission based on multi point (the associated user and a plurality of base stations) user association algorithm to control the load in a cellular network, in order to achieve load balancing across heterogeneous cellular networks. First of all, this article will ensure that the user load modeling association equilibrium to maximize the utility of objective function, the problem is a mixed integer programming problem is NP hard. In order to solve this problem, this paper will be between the base station and the users 鐨勪竴瀵逛竴鍏寵仈鏉懼紱涓哄垎鏁伴樁鐢ㄦ埛鍏寵仈,鍗沖厑璁告瘡涓敤鎴峰拰澶氫釜鍩虹珯鍏寵仈,涓嶄絾灝嗗師鏈毦浠ユ眰瑙ｇ殑娣峰悎鏁存暟瑙勫垝闂杞寲鎴愪簡(jiǎn)鍑鎬紭鍖栭棶棰，

本文編號(hào)：1592088