本文選題：超密集異構(gòu)網(wǎng)絡(luò) + 干擾協(xié)調(diào)��； 參考：《東南大學(xué)》2016年博士論文

【摘要】：隨著移動智能終端的大規(guī)模流行和無線多媒體數(shù)據(jù)業(yè)務(wù)的廣泛應(yīng)用,移動寬帶無線網(wǎng)絡(luò)中的數(shù)據(jù)業(yè)務(wù)需求呈爆炸式增長,如何更加高效地利用有限的無線頻譜資源,提升頻譜資源效率,擴(kuò)大網(wǎng)絡(luò)容量,降低干擾,保證用戶服務(wù)質(zhì)量需求,降低中斷概率,在滿足用戶服務(wù)質(zhì)量需求的基礎(chǔ)上進(jìn)行快速數(shù)據(jù)傳輸,成為新一代寬帶無線網(wǎng)絡(luò)設(shè)計中亟待解決的問題。為緩解網(wǎng)絡(luò)壓力,實現(xiàn)未來10年內(nèi)移動數(shù)據(jù)業(yè)務(wù)流量增長1000倍的發(fā)展需求,業(yè)界對新一代移動寬帶無線網(wǎng)絡(luò)關(guān)鍵技術(shù)進(jìn)行廣泛研究,提出通過引入超密集異構(gòu)網(wǎng)絡(luò)等有效的體系結(jié)構(gòu)和具有網(wǎng)絡(luò)自感知、自調(diào)整等能力的智能化網(wǎng)絡(luò)提升新一代移動寬帶無線網(wǎng)絡(luò)的業(yè)務(wù)能力。通過在傳統(tǒng)大功率宏小區(qū)的覆蓋范圍內(nèi)大規(guī)模密集部署低功率小小區(qū),超密集異構(gòu)網(wǎng)絡(luò)能夠有效提升頻譜復(fù)用率,拉近用戶與服務(wù)小區(qū)之間的距離,提升系統(tǒng)吞吐量。然而,低功率小小區(qū)的密集部署會增加網(wǎng)絡(luò)中的信道干擾,同時造成小區(qū)間負(fù)載不均衡,降低用戶公平性,從而制約網(wǎng)絡(luò)性能的提升。因此在超密集異構(gòu)網(wǎng)絡(luò)中研究具有自感知、自調(diào)整能力的干擾協(xié)調(diào)及用戶關(guān)聯(lián)技術(shù)具有重大意義。由于功率調(diào)整與用戶關(guān)聯(lián)、小區(qū)范圍擴(kuò)展偏置調(diào)整與用戶關(guān)聯(lián)、用戶關(guān)聯(lián)與調(diào)度之間強烈的耦合關(guān)系,超密集網(wǎng)絡(luò)中基于功率調(diào)整的干擾協(xié)調(diào)問題和基于小區(qū)范圍擴(kuò)展偏置調(diào)整的用戶關(guān)聯(lián)問題均是非確定性多項式困難問題,在多項式時間內(nèi)無法精確求解。論文主要針對超密集異構(gòu)網(wǎng)絡(luò)場景下基于功率調(diào)整的干擾協(xié)調(diào)問題及基于小區(qū)范圍擴(kuò)展偏置的用戶關(guān)聯(lián)技術(shù)進(jìn)行了深入研究,通過啟發(fā)式算法解決以上問題,最大化系統(tǒng)吞吐量和比例公平吞吐量,最小化中斷概率。論文的主要貢獻(xiàn)如下:研究了超密集異構(gòu)網(wǎng)絡(luò)中基于改進(jìn)粒子群的干擾協(xié)調(diào)算法。由于系統(tǒng)吞吐量是干擾協(xié)調(diào)算法的一項關(guān)鍵評價標(biāo)準(zhǔn),而功率調(diào)整是一種有效的干擾協(xié)調(diào)手段,因此提出一種最大化系統(tǒng)吞吐量的功率調(diào)整算法。深入研究超密集異構(gòu)網(wǎng)絡(luò)下行鏈路干擾問題,考慮到超密集異構(gòu)網(wǎng)絡(luò)中,大量密集部署的小小區(qū)產(chǎn)生嚴(yán)重的疊加干擾從而制約網(wǎng)絡(luò)吞吐量的提升,提出通過調(diào)整小小區(qū)的發(fā)送功率降低小區(qū)間干擾,提升系統(tǒng)吞吐量�？紤]到功率調(diào)整會導(dǎo)致用戶服務(wù)小區(qū)的變化,已有的功率調(diào)整算法很難求解最優(yōu)發(fā)送功率,提出基于改進(jìn)粒子群的功率調(diào)整算法,研究改進(jìn)粒子群算法中的收斂條件及最優(yōu)性保障條件,引入隨機局部搜索和多次初始化過程保證解的最優(yōu)性,獲得最優(yōu)小區(qū)發(fā)送功率,最大化系統(tǒng)吞吐量。仿真結(jié)果表明,相比于已有功率調(diào)整算法中采用的固定的用戶服務(wù)小區(qū),考慮用戶服務(wù)小區(qū)隨功率調(diào)整的變化能夠使系統(tǒng)吞吐量獲得進(jìn)一步提升,提出的基于改進(jìn)粒子群的干擾協(xié)調(diào)算法能夠以多項式復(fù)雜度獲得全局最優(yōu)小區(qū)發(fā)送功率。研究了超密集異構(gòu)網(wǎng)絡(luò)中用戶服務(wù)質(zhì)量約束下最大化系統(tǒng)吞吐量的功率調(diào)整算法�？紤]到超密集異構(gòu)網(wǎng)絡(luò)中功率調(diào)整算法可能導(dǎo)致宏小區(qū)邊緣用戶服務(wù)質(zhì)量下降的問題,把用戶服務(wù)質(zhì)量需求考慮在干擾協(xié)調(diào)問題建模中,同時考慮用戶服務(wù)質(zhì)量需求和用戶服務(wù)小區(qū)隨功率調(diào)整的變化,通過舍棄與服務(wù)質(zhì)量約束沖突的不可行解,利用改進(jìn)粒子群優(yōu)化小小區(qū)的發(fā)送功率。由于舍棄不可行解、搜索可行解的過程需要額外的計算復(fù)雜度、更多的迭代次數(shù)和運行時間,因此提出將拉格朗日對偶引入改進(jìn)粒子群算法,提升初始粒子質(zhì)量,通過拉格朗日對偶和改進(jìn)粒子群算法的結(jié)合,節(jié)省算法運行時間,降低搜索到最優(yōu)解所需的計算復(fù)雜度。仿真結(jié)果表明提出的算法能夠在保證用戶服務(wù)質(zhì)量需求的同時獲得最優(yōu)的系統(tǒng)吞吐量。研究超密集異構(gòu)網(wǎng)絡(luò)中的小區(qū)范圍擴(kuò)展偏置優(yōu)化問題,利用吉布斯采樣,提出最大化速率相關(guān)效用函數(shù)的小區(qū)范圍擴(kuò)展偏置優(yōu)化算法。針對傳統(tǒng)用戶關(guān)聯(lián)方法導(dǎo)致的小小區(qū)范圍受限、小區(qū)間負(fù)載不均衡及吞吐量提升能力受限等問題,提出通過優(yōu)化各小小區(qū)的小區(qū)范圍擴(kuò)展偏置優(yōu)化用戶關(guān)聯(lián),提升系統(tǒng)吞吐量,降低低速率用戶數(shù)量,提升系統(tǒng)的比例公平吞吐量�？紤]到速率相關(guān)效用函數(shù)優(yōu)化問題中,小區(qū)范圍擴(kuò)展偏置與用戶關(guān)聯(lián)和調(diào)度間復(fù)雜的耦合關(guān)系,無法直接求解最優(yōu)小區(qū)范圍擴(kuò)展偏置,因此提出基于吉布斯采樣的小區(qū)范圍擴(kuò)展偏置優(yōu)化算法�？紤]到集中式基于吉布斯采樣的小區(qū)范圍擴(kuò)展偏置優(yōu)化算法需要知道所有小區(qū)與用戶間信道增益,其信息交換開銷及計算復(fù)雜度隨著網(wǎng)絡(luò)規(guī)模的擴(kuò)大迅速增加,巧妙推導(dǎo)得到僅需局部信息交換的分布式小區(qū)范圍擴(kuò)展偏置優(yōu)化算法,并證明算法的最優(yōu)性。仿真結(jié)果表明,提出的集中式及分布式小區(qū)范圍擴(kuò)展偏置優(yōu)化算法均能獲得全局最優(yōu)的小小區(qū)范圍擴(kuò)展偏置,提出的分布式算法的計算復(fù)雜度及信息交換開銷遠(yuǎn)小于集中式算法。研究低復(fù)雜度中心輔助分布式小區(qū)范圍擴(kuò)展偏置優(yōu)化算法。針對超密集異構(gòu)網(wǎng)絡(luò)小區(qū)范圍擴(kuò)展偏置優(yōu)化算法的計算復(fù)雜度、迭代次數(shù)隨著網(wǎng)絡(luò)規(guī)模擴(kuò)大而迅速升高的問題,研究并提出小小區(qū)間范圍擴(kuò)展影響關(guān)系圖的建立方法、基于圖著色的小區(qū)分組算法及用戶候選服務(wù)小區(qū)的選擇方法。提出基于吉布斯采樣的中心輔助分布式小區(qū)范圍擴(kuò)展偏置優(yōu)化算法降低算法信令開銷、計算復(fù)雜度及迭代次數(shù)。分析算法的計算復(fù)雜度及信令開銷,證明算法的最優(yōu)性。仿真結(jié)果表明,與集中式及分布式算法相比,提出的中心輔助分布式小區(qū)范圍擴(kuò)展偏置優(yōu)化算法能夠以最少的迭代次數(shù)、最低的計算復(fù)雜度和最小的信息交換開銷獲得各小小區(qū)范圍擴(kuò)展偏置的最優(yōu)解。
[Abstract]:With the widespread popularity of mobile intelligent terminals and the wide application of wireless multimedia data services, the demand for data services in mobile broadband wireless networks is increasing. How to use the limited wireless spectrum resources more efficiently, improve the efficiency of the spectrum resources, expand the network capacity, reduce interference, and ensure the demand for the quality of service of the users, To reduce the probability of interruption and carry out rapid data transmission on the basis of customer service quality requirements, it has become an urgent problem in the design of a new generation broadband wireless network. In order to alleviate the network pressure and achieve the development demand of 1000 times the growth of mobile data traffic in the next 10 years, the industry has a key technology for the new generation of mobile broadband wireless network. Extensive research has been carried out to improve the business capability of a new generation of mobile broadband wireless networks by introducing efficient architectures such as super dense heterogeneous networks and intelligent networks with self-awareness and self-tuning capabilities. The set of heterogeneous networks can effectively enhance the spectrum reuse rate, close the distance between the user and the service community, and improve the system throughput. However, the dense deployment of the low power small cell will increase the channel interference in the network, and cause the uneven load between the communities, reduce the fairness of the users, and thus restrict the network performance. In heterogeneous networks, it is of great significance to study the interference coordination and user association techniques with self perception and self-tuning ability. Due to the power adjustment and user association, the area extended offset adjustment is associated with the user, the strong coupling relationship between the user association and scheduling, the interference coordination problem and the base based on the power adjustment in the ultra dense network. The problem of user association in the area extended offset adjustment is a nondeterministic polynomial difficult problem, which can not be solved accurately in polynomial time. This paper focuses on the interference coordination problem based on power adjustment in the super dense heterogeneous network and the user association technology based on the extended partial location based on the cell range. The main contributions of the thesis are as follows: the main contribution of this paper is as follows: the interference coordination algorithm based on Improved Particle Swarm Optimization in the super dense heterogeneous network is studied. As an effective means of interference coordination, a power adjustment algorithm is proposed to maximize the throughput of the system. The problem of the downlink interference in the ultra dense heterogeneous network is deeply studied. In the ultra dense heterogeneous network, a large number of small cells with dense deployment are seriously overlaid and thus restrict the increase of network throughput. The transmission power of small cell is adjusted to reduce inter cell interference and improve the system throughput. Considering that the power adjustment will lead to the change of the user service area, the existing power adjustment algorithm is difficult to solve the optimal transmission power. A power adjustment algorithm based on improved particle swarm optimization is proposed, and the convergence conditions and optimality in the particle swarm optimization algorithm are improved. The optimal cell transmission power and maximum system throughput are obtained by introducing random local search and multiple initialization process to obtain optimal cell transmission power and maximize system throughput. The simulation results show that the system can swallow up the system with the change of power adjustment in the user service cell compared to the fixed user service area used in the existing power adjustment algorithm. The improved particle swarm optimization algorithm based on improved particle swarm optimization can obtain the global optimal cell transmission power with the polynomial complexity. The power adjustment algorithm of the maximum system throughput under the user service quality constraint in the ultra dense heterogeneous network is studied. The method may lead to a decline in the quality of the user's service quality at the edge of the macro cell. Considering the user service quality requirement in the modeling of the interference coordination problem, the user service quality requirement and the change of the user service community with the power adjustment are taken into consideration, and the improved particle swarm optimization is used by the improved particle swarm optimization by abandoning the infeasible solution of the conflict of the service quality constraints. As a result of abandoning the unfeasible solution, the process of searching the feasible solution needs additional computational complexity, more iteration times and running time. Therefore, the Lagrange dual is introduced to improve the particle swarm optimization to improve the initial particle quality. The combination of Lagrange pair and improved particle swarm optimization can save the operation of the algorithm. The simulation results show that the proposed algorithm can obtain the optimal system throughput while guaranteeing the quality of the user's service quality. The area extended offset optimization problem in the ultra dense heterogeneous network is studied, and the maximum rate related utility function is proposed by using Gibbs sampling. In order to improve the system throughput, improve the throughput of the system, reduce the number of low rate users and improve the number of low rate users, the area extended offset optimization algorithm, which is caused by the traditional user association method, is limited by the small cell scope, the unbalance between the cells and the Limited throughput. Proportional fair throughput of the system. Considering the rate dependent utility function optimization problem, the complex coupling relationship between the cell range extension bias and the user association and scheduling can not be solved directly to the optimal cell range extension bias. Therefore, a neighborhood extended bias optimization algorithm based on Gibbs sampling is proposed. The area extended offset optimization algorithm based on Gibbs sampling needs to know the channel gain between all cell and the user. The information exchange cost and computational complexity increase rapidly with the expansion of the network scale, and the distributed area spread offset optimization algorithm, which needs only local information exchange, is derived, and the optimal algorithm is proved. The simulation results show that the proposed centralized and distributed area extended offset optimization algorithm can obtain the global optimal small cell range extension bias. The computational complexity and the information exchange cost of the proposed distributed algorithm are far less than the centralized algorithm. According to the computational complexity of the extended biasing optimization algorithm for the cell range extension of the ultra dense heterogeneous network, the number of iterations increases rapidly with the expansion of the network scale. The method of establishing a small interval extended influence relation graph is studied and proposed. The plot grouping algorithm based on graph coloring and the selection of the user candidate service community are based on the graph coloring. Method. A center assisted distributed cell range extended offset optimization algorithm based on Gibbs sampling is proposed to reduce the algorithm signaling overhead, computational complexity and iteration times. The computational complexity and signaling overhead of the algorithm are analyzed, and the optimization of the algorithm is proved. The simulation results show that the central sub division is compared with the centralized and distributed algorithm. The spread bias optimization algorithm can obtain the optimal solution for the extended offset of small cell range with the minimum number of iterations, the lowest computational complexity and the minimum information exchange overhead.

【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN92

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