新一代宽带无线网络关键技术研究

发布时间:2018-04-21 04:36

  本文选题:超密集异构网络 + 干扰协调 ; 参考:《东南大学》2016年博士论文


【摘要】:随着移动智能终端的大规模流行和无线多媒体数据业务的广泛应用,移动宽带无线网络中的数据业务需求呈爆炸式增长,如何更加高效地利用有限的无线频谱资源,提升频谱资源效率,扩大网络容量,降低干扰,保证用户服务质量需求,降低中断概率,在满足用户服务质量需求的基础上进行快速数据传输,成为新一代宽带无线网络设计中亟待解决的问题。为缓解网络压力,实现未来10年内移动数据业务流量增长1000倍的发展需求,业界对新一代移动宽带无线网络关键技术进行广泛研究,提出通过引入超密集异构网络等有效的体系结构和具有网络自感知、自调整等能力的智能化网络提升新一代移动宽带无线网络的业务能力。通过在传统大功率宏小区的覆盖范围内大规模密集部署低功率小小区,超密集异构网络能够有效提升频谱复用率,拉近用户与服务小区之间的距离,提升系统吞吐量。然而,低功率小小区的密集部署会增加网络中的信道干扰,同时造成小区间负载不均衡,降低用户公平性,从而制约网络性能的提升。因此在超密集异构网络中研究具有自感知、自调整能力的干扰协调及用户关联技术具有重大意义。由于功率调整与用户关联、小区范围扩展偏置调整与用户关联、用户关联与调度之间强烈的耦合关系,超密集网络中基于功率调整的干扰协调问题和基于小区范围扩展偏置调整的用户关联问题均是非确定性多项式困难问题,在多项式时间内无法精确求解。论文主要针对超密集异构网络场景下基于功率调整的干扰协调问题及基于小区范围扩展偏置的用户关联技术进行了深入研究,通过启发式算法解决以上问题,最大化系统吞吐量和比例公平吞吐量,最小化中断概率。论文的主要贡献如下:研究了超密集异构网络中基于改进粒子群的干扰协调算法。由于系统吞吐量是干扰协调算法的一项关键评价标准,而功率调整是一种有效的干扰协调手段,因此提出一种最大化系统吞吐量的功率调整算法。深入研究超密集异构网络下行链路干扰问题,考虑到超密集异构网络中,大量密集部署的小小区产生严重的叠加干扰从而制约网络吞吐量的提升,提出通过调整小小区的发送功率降低小区间干扰,提升系统吞吐量。考虑到功率调整会导致用户服务小区的变化,已有的功率调整算法很难求解最优发送功率,提出基于改进粒子群的功率调整算法,研究改进粒子群算法中的收敛条件及最优性保障条件,引入随机局部搜索和多次初始化过程保证解的最优性,获得最优小区发送功率,最大化系统吞吐量。仿真结果表明,相比于已有功率调整算法中采用的固定的用户服务小区,考虑用户服务小区随功率调整的变化能够使系统吞吐量获得进一步提升,提出的基于改进粒子群的干扰协调算法能够以多项式复杂度获得全局最优小区发送功率。研究了超密集异构网络中用户服务质量约束下最大化系统吞吐量的功率调整算法。考虑到超密集异构网络中功率调整算法可能导致宏小区边缘用户服务质量下降的问题,把用户服务质量需求考虑在干扰协调问题建模中,同时考虑用户服务质量需求和用户服务小区随功率调整的变化,通过舍弃与服务质量约束冲突的不可行解,利用改进粒子群优化小小区的发送功率。由于舍弃不可行解、搜索可行解的过程需要额外的计算复杂度、更多的迭代次数和运行时间,因此提出将拉格朗日对偶引入改进粒子群算法,提升初始粒子质量,通过拉格朗日对偶和改进粒子群算法的结合,节省算法运行时间,降低搜索到最优解所需的计算复杂度。仿真结果表明提出的算法能够在保证用户服务质量需求的同时获得最优的系统吞吐量。研究超密集异构网络中的小区范围扩展偏置优化问题,利用吉布斯采样,提出最大化速率相关效用函数的小区范围扩展偏置优化算法。针对传统用户关联方法导致的小小区范围受限、小区间负载不均衡及吞吐量提升能力受限等问题,提出通过优化各小小区的小区范围扩展偏置优化用户关联,提升系统吞吐量,降低低速率用户数量,提升系统的比例公平吞吐量。考虑到速率相关效用函数优化问题中,小区范围扩展偏置与用户关联和调度间复杂的耦合关系,无法直接求解最优小区范围扩展偏置,因此提出基于吉布斯采样的小区范围扩展偏置优化算法。考虑到集中式基于吉布斯采样的小区范围扩展偏置优化算法需要知道所有小区与用户间信道增益,其信息交换开销及计算复杂度随着网络规模的扩大迅速增加,巧妙推导得到仅需局部信息交换的分布式小区范围扩展偏置优化算法,并证明算法的最优性。仿真结果表明,提出的集中式及分布式小区范围扩展偏置优化算法均能获得全局最优的小小区范围扩展偏置,提出的分布式算法的计算复杂度及信息交换开销远小于集中式算法。研究低复杂度中心辅助分布式小区范围扩展偏置优化算法。针对超密集异构网络小区范围扩展偏置优化算法的计算复杂度、迭代次数随着网络规模扩大而迅速升高的问题,研究并提出小小区间范围扩展影响关系图的建立方法、基于图着色的小区分组算法及用户候选服务小区的选择方法。提出基于吉布斯采样的中心辅助分布式小区范围扩展偏置优化算法降低算法信令开销、计算复杂度及迭代次数。分析算法的计算复杂度及信令开销,证明算法的最优性。仿真结果表明,与集中式及分布式算法相比,提出的中心辅助分布式小区范围扩展偏置优化算法能够以最少的迭代次数、最低的计算复杂度和最小的信息交换开销获得各小小区范围扩展偏置的最优解。
[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.

【学位授予单位】:东南大学
【学位级别】:博士
【学位授予年份】:2016
【分类号】:TN92

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