超密集网络中用户归属与功率控制策略研究

发布时间：2018-03-07 04:09

本文选题：用户归属　切入点：超密集网络　出处：《中国科学技术大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着移动通信技术的飞速发展,数据业务需求飞速增长。超密集网络通过部署更加密集的小基站,可以使系统获得更高的频谱复用效率,从而提高系统容量。但在超密集网络中基站的覆盖范围小,因此会出现基站覆盖区域重叠的问题,而传统的用户归属方案使得用户仅能归属到一个基站,其它基站将成为强干扰源,严重制约系统性能。一种有效的方案是采用多连接用户归属方式,即一个用户可以归属于多个基站,不同基站通过协作提升用户服务质量。值得注意的是,多连接场景下的小区干扰仍是限制系统性能的主要瓶颈,如何有效限制基站间干扰仍需要进一步研究。已有研究主要在时域、频域、空域和功率域上通过相应的干扰协调策略降低基站间干扰。其中功率控制技术,通过调整基站发射功率,适当降低服务基站周围的其它基站的功率水平,能够提升系统吞吐量。本文针对超密集部署的网络,以提升系统频谱效率和能量效率为目标,对超密集网络中的用户归属与功率控制策略进行了研究,主要内容包括:针对超密集网络,本文提出了一种基于多连接的用户归属与功率控制联合优化算法。根据多连接用户归属及超密集网络的特点,本文形成了以最大化系统对数和速率为优化目标,联合用户归属与功率控制的优化问题。由于所提算法难以求出最优解,本文提出一种交替迭代算法,交替优化用户归属与功率分配。通过系统级仿真,可以说明本文提出算法能够在保证用户公平性的前提下,提升系统吞吐量。针对超密集网络,本文提出了一种基于用户归属和功率控制的基站休眠方法。根据超密集网络基站密度大的特点,本文形成了以最大化系统能量效率为优化目标,联合用户归属与功率控制的优化问题。由于所提算法难以求出最优解,本文提出了遍历基站开关个数,在基站开关个数固定的情况下求出目标函数最优的用户归属与功率控制策略,通过比较确定使系统能效最优的用户归属与功率控制方案。之后通过进一步优化降低了该方法的复杂度。仿真结果表明,所提算法能够明显提高系统的频谱效率与能量效率。
[Abstract]:With the rapid development of mobile communication technology, the demand for data services grows rapidly. By deploying more dense small base stations, ultra-dense networks can make the system achieve higher spectral multiplexing efficiency. So as to improve the system capacity, but in the ultra-dense network, the base station coverage range is small, so the base station coverage area will overlap, and the traditional user home scheme makes the user can only belong to one base station. Other base stations will become strong interference sources, severely constraining the performance of the system. One effective solution is to adopt a multi-connected user home mode, that is, one user can be assigned to multiple base stations. Different base stations improve the quality of service of users through collaboration. It is worth noting that cell interference in multi-connection scenarios is still the main bottleneck that limits the performance of the system. How to effectively limit the interference between base stations still needs further research. In the time domain, frequency domain, spatial domain and power domain, the interference between base stations can be reduced by the corresponding interference coordination strategy. By adjusting the transmission power of the base station and lowering the power level of other base stations around the base station, the throughput of the system can be improved. In this paper, the user home and power control strategy in ultra dense network is studied. The main contents are as follows: for super dense network, In this paper, a joint optimization algorithm of user home and power control based on multi-connection is proposed. According to the characteristics of multi-connection user home and super-dense network, the optimization goal is to maximize the logarithm and rate of the system. Joint user attribution and power control optimization problem. Because the proposed algorithm is difficult to find the optimal solution, this paper proposes an alternating iterative algorithm to alternately optimize user attribution and power allocation. It can be concluded that the proposed algorithm can improve the throughput of the system on the premise of ensuring the fairness of users. In this paper, a method of base station dormancy based on user home and power control is proposed. According to the characteristics of high density of base station in super-dense network, the optimization goal is to maximize the energy efficiency of the system. Joint user home and power control optimization problem. Because the proposed algorithm is difficult to find the optimal solution, this paper proposes the number of ergodic base station switches. When the number of switches in the base station is fixed, the optimal user home and power control strategy of the objective function is obtained. The user home and power control scheme which optimizes the energy efficiency of the system is determined by comparison, and the complexity of the method is reduced by further optimization. The simulation results show that the proposed algorithm can significantly improve the spectral efficiency and energy efficiency of the system.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.5

【相似文献】