一种提高小区边缘用户性能的多用户子载波功率分配策略

在OFDMA系统中,小区边缘用户由于受到来自相邻小区的同频干扰,通信质量严重下降.有效地抑制小区间干扰,极大地提升无线网络性能尤其是小区边缘用户性能是5G移动通信系统的目标之一.基于速率自适应(RA)准则提出了一种提高小区边缘用户性能的子载波和功率的联合资源分配算法,该算法分为子载波分配和混合功率分配两步,在提高小区边缘用户性能的同时,最大化链路吞吐量.仿真结果表明,小区边缘用户吞吐量增益为25％以上,混合功率分配下的系统吞吐量逼近全部用户使用注水法时的系统吞吐量,并且降低了运算复杂度.     

超密集网络以用户为中心及多维协作的用户分簇算法

为了降低超密集网络中小区间的干扰,提升频谱效率,给出一种在以用户为中心的可重叠虚拟小区场景下,基于边权重和贪婪树增长(Greedy Tree Growing Algorithm,GTGA)算法的用户分簇方案.考虑到每个用户对其他用户产生干扰的同时,又受到其他用户的干扰,权重设计采用协作传输的平衡策略.针对用户分簇,改进的K-means聚类算法通过能够拟合高斯分布的权重统计量来动态调整用户分群的大小.仿真结果表明,所提算法能有效地降低复杂度,减少干扰,提高超密集网络的频谱效率.     

OFDMA毫微微小区双层网络中基于分组的资源分配

毫微微小区(Femtocell)网络能够增强室内覆盖,提高系统容量,但是在频谱共享的正交频分多址(OFDMA)毫微微小区网络中,毫微微小区之间的同层干扰以及毫微微小区与宏小区(Macrocell)之间的跨层干扰严重限制了系统的性能。针对这两种干扰,该文提出一种基于分组的资源分配算法。该算法包括两部分:一部分是宏基站先利用改进的匈牙利算法为宏小区用户分配信道,再用注水算法分配功率,保证宏小区用户的正常传输;另一部分是在避免干扰宏小区用户的基础上,先采用模拟退火算法对毫微微小区进行分组,再进行信道和功率分配,满足毫微微小区用户的数据速率需求,最大化频谱效率。仿真结果表明,该算法有效地抑制了这两种干扰,既能保证用户的数据速率需求,又能有效提升网络频谱效率。     

异构无线网络中边缘用户小区选择算法

在异构网络中,部署多种小功率基站,会出现大量的“小区边缘区域”,边缘用户的性能会受到影响。为此,提出了一种边缘用户小区选择算法,通过发送端进行预编码抑制干扰,提高用户接收到的信干噪比。根据小区当前用户数,动态地调整给微小区边缘用户参考接收信号添加的偏置值,避免边缘用户接入用户数过多的拥堵小区。仿真结果表明,该算法与参考算法相比,能够有效降低宏小区负载,平衡不同微小区间负载,提高轻载微小区的网络吞吐量和资源利用率。     

多小区OFDMA系统中基于POMDP的干扰协调

对OFDMA系统中的干扰协调进行了研究,提出了一种基于部分可观察马尔可夫决策过程理论的动态干扰协调算法。该算法结合干扰的统计模型和信道的信干噪比对边缘用户进行信道分配。仿真结果表明,该算法能够有效地使边缘用户避开邻小区干扰,而且不需要小区间交互,节省了系统开销。另外,利用粒子滤波法建立了SINR和干扰的似然关系,不需要系统额外对干扰进行测量。     

多天线两小区蜂窝系统的低开销线性干扰对齐算法

在蜂窝系统中,由于干扰的存在,用户性能受到影响,特别是对于小区边缘用户,其通信质量较差。干扰对齐作为一种能够消除干扰、提高系统容量的技术,近年来受到广泛关注。针对多天线两小区蜂窝系统的边缘用户,本文给出了一种系统开销小、需要天线数少的线性干扰对齐算法。该算法利用发送端预编码矢量消除小区内干扰,利用接收端干扰抑制矢量消除小区间干扰。采用本文算法后,在每个小区有 个用户、基站有 根发送天线、用户有 根接收天线的情况下,只需 和 就可以实现上下行的干扰对齐,整个系统可以达到 的自由度,并且在下行链路中不需要小区间反馈,而在上行链路中只需要较小的小区间反馈。仿真结果表明,本文提出的算法能够以较小的开销实现比以往的算法更好的性能。      

超密集小区中基于二分图优先级的资源分配算法

由于在超密集网络中小基站密集的部署,用户数据量空前增加,对数据速率的要求不断提高,所以在有限的资源下如何高效地将资源分配给用户尤为重要。提出了一种小区分簇算法,根据小区簇的通信业务、通信负载量等条件将各个小区分为不同的优先级,引入二分图,以小区簇的优先级为依据建立频谱资源与小区簇之间的匹配关系,并提出一种低复杂度的贪婪算法。仿真结果表明所提算法能够有效提高系统性能,并且有效完成频谱资源的分配。     

分布式小区架构结合STBC的小区间干扰协调策略

针对分布式小区架构的小区间干扰抑制问题,提出了一种结合空时分组码(STBC)的小区间干扰协调策略.该策略一方面通过小区间干扰协调对一个小区的可用频率资源进行某种限制,从而提高相邻小区在这些资源上的信干比和小区边缘的数据速率.另一方面,针对分布式小区架构的多天线技术优势,通过在多天线结构中使用STBC编码获得分集增益,进一步提高小区边缘用户性能.为了减少多天线同时服务引入的额外干扰,只有当用户收到的来自多个小区的信号强度相差不大时,才使用STBC编码.否则,选用一个信号强度最大的小区为用户提供服务.仿真结果表明,所提出的方法有效地提高了系统吞吐量,特别是小区边缘用户的吞吐量.     

异构网中D2D通信的模式选择和资源分配的研究

将D2D(Device to Device)和家庭小区技术引入蜂窝网络,是未来第五代蜂窝移动通信(5G)的趋势之一.主要研究由宏基站(MBS)和家庭基站(FBS)组成的异构网中的D2D(Device to Device)通信的干扰问题,并提出了一种新颖的模式选择算法和资源分配算法来减少系统间干扰.仿真结果显示,文中提出的算法可以有效减少干扰,提高D2D用户的速率,增加系统吞吐量.     

超密集异构网中的Q学习资源调度算法

在超密集异构蜂窝网络中,随着低功率基站大量增加,且复用相同的频谱资源,小区间干扰(ICI)可能会变得很强,从而降低系统整体吞吐量。因此,文中提出一种基于Q学习的资源调度(QLRS)算法以尽可能地最大化系统容量。算法首先将小基站进行分簇,在每个调度周期根据簇内用户数量为每个簇调度资源;然后以系统整体吞吐量和能效为优化目标,对簇内有关联用户的小小区进行资源变更和优化,并将收益记录于Q表中,Q表经多次迭代收敛后,得到系统最优资源分配方案。仿真结果表明,与其他资源分配算法相比,文中提出的算法在保证能源效率与宏蜂窝吞吐量的条件下,进一步提高了系统整体吞吐量。     

超密集网络中干扰协调方法及性能分析

在超密集网络中,全频重用能够提升网络的平均吞吐量,但严重的小区间干扰限制了边缘用户数据率的提升。如何有效地管理超密集网络中的干扰、提升边缘用户数据率是重要的研究问题。本文研究了超密集网络中两种有代表性的干扰协调方法,随机干扰协调和基于拓扑干扰协调,分析了这两种方法的平均数据率和边缘用户数据率、以及系统参数对其性能的影响。理论分析和仿真的结果表明,采用随机干扰协调能够提升边缘用户的信干噪比,但不能提升边缘用户数据率。这使得当系统增加频率重用因子时,会牺牲平均用户数据率同时也不能提高边缘用户数据率。采用基于拓扑的干扰协调能够同时提升边缘用户的信干噪比和数据率;当频率重用因子较低时,提高重用因子可以通过以较少牺牲平均用户数据率为代价有效提高边缘用户数据率,从而实现平均数据率和边缘用户数据率的折中。      

Dynamic computation offloading in time-varying environment forultra-dense networks: a stochastic game approach

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications, combining ultra-dense networks (UDNs) and mobile edge computing (MEC)are considered as important solutions. In the MEC enabled UDNs, one of the most important issues is computation offloading. Although a number of work have been done toward this issue, the problem of dynamic computation offloading in time-varying environment, especially the dynamic computation offloading problem for multi-user, has not been fully considered. Therefore, in order to fill this gap, the dynamic computation offloading problem in time-varying environment for multi-user is considered in this paper. By considering the dynamic changes of channel state and users queue state, the dynamic computation offloading problem for multi-user is formulated as a stochastic game, which aims to optimize the delay and packet loss rate of users. To find the optimal solution of the formulated optimization problem, Nash Q-learning (NQLN) algorithm is proposed which can be quickly converged to a Nash equilibrium solution. Finally, extensive simulation results are presented to demonstrate the superiority of NQLN algorithm. It is shown that NQLN algorithm has better optimization performance than the benchmark schemes.     

基于功率控制的LTE系统下行ICIC算法研究

在LTE系统中,小区的边缘用户会同时受到邻区同频用户的干扰,需要一种小区间的干扰协调技术（ICIC）来解决这个问题。分析传统ICIC算法的不足,提出一种优化的算法,通过计算边缘用户在单个干扰用户和多个干扰用户情况下的容量,得出邻区基站较为精确的发射功率控制量。参考3GPP标准案例仿真模型,对该算法进行了仿真,仿真结果表明与传统ICIC算法的相比,该算法可以明显提高边缘用户的吞吐量。     

基于信泄噪比的改进预编码算法研究

为改善用户自身天线间的干扰和信道噪声干扰对SLNR算法性能的影响,对SLNR算法进行了改进,包括两种改进算法：SLNR_ZF算法是利用ZF算法可以消除用户自身天线间干扰的作用将其引入到SLNR算法中;SLNR_MMSE算法是在SLNR_ZF算法的基础上引入规范化因子,消除噪声干扰。通过仿真结果表明,改进的两种优化算法可以有效地消除包括用户自身天线间干扰、用户间干扰和噪声干扰的多种干扰,提高小区边缘用户的吞吐量和误码率。     

Power control and clustering in heterogeneous cellular networks

Recently, it is widely believed that significant coverage and performance improvement can be achieved through the deployment of small cells in conjunction with the well-established macro cells. However, it is expected that the high density of base stations in such heterogeneous cellular networks will give rise to multiple design problems related to both co-tier (small-to-small) and cross-tier (between small and macro cells) interference. Fortunately, cooperation between base stations will play a major role to cope with these problems and hence to enhance the users’ data rates. In this paper, we consider a two-tier cellular network comprised of a macro cell underlaid with multiple small cells where both co-tier and cross-tier interference are taken into account. We study the scenario where the small cell base stations seek to maximize a common objective by forming multiple clusters through cooperation. These base stations have also to allocate power to their associated users and, at the same time, control the total aggregate interference caused to the macro cell user which has to be kept below a threshold prefixed by the macro cell base station. We consider two utility functions: the overall sum rate of the small cell network and the minimum data rate of the small cell users. We formulate the studied problems as mixed integer nonlinear optimization problems and we discuss their NP hardness. Therefore, due to the complexity of finding the optimal solution, we design heuristic algorithms which resolves efficiently the tradeoff between computational complexity and performance. We show through simulations that the designed heuristics approach the optimal solution (obtained using the complex exhaustive search algorithm) with highly reduced computational complexity.     

Dynamic resource allocation in mobile heterogeneous cellular networks

User mobility is a challenging issue in macro and femto cellular networks for the fifth-generation and newer mobile communications due to the time-varying interference and topology experienced. In this paper, we consider an OFDMA-based two-tier network with one macro cell and several femto cells, wherein each macro user and/or femto user can leave or enter its serving cell frequently, referred to as user mobility. A resource allocation problem with different rate requirements of mobile users is then formulated. Assuming well knowledge of the user locations and the channel state information, we propose a dynamic algorithm with static and dynamic parts for a better trade-of between computational complexity and system throughput. The static algorithm, named interference weighted cluster algorithm in this paper, is based on the graph theory to cluster the femtocells by minimizing the interference between clusters, while the dynamic algorithm is to deal with the user mobility by sharing the resource blocks under the constraints of rate requirements. Numerical results are demonstrated to show the effectiveness of the proposed dynamic resource allocation algorithm in terms of capacity, computational time, and outage probability.

     

联合信号子空间和功率分配的认知干扰对齐传输方案设计

本文针对由一条授权通信链路和多条次用户干扰信道组成的认知多输入多输出（Multiple Input Multiple Output,MIMO）系统,首先提出了基于信号子空间的认知干扰对齐迭代优化算法,并且利用单调有界理论证明了该算法可以收敛到稳定点。为了进一步提升系统的和速率性能,提出了一种联合信号子空间和功率分配的增强认知干扰对齐算法。该算法通过在每个次用户的多个数据流之间进行自适应功率分配,解决了次用户的有用信号空间中总是有残余的干扰信号的问题。数值仿真结果表明,相对于传统的认知干扰对齐算法,所提的算法能够获得较为明显的性能提升。      

基于用户分簇的认知超密集网络资源分配

针对5G时代小基站的密集部署带来的复杂干扰问题,对下行的认知无线电超密集网络下的资源分配进行了研究。为减小网络干扰,提高次用户吞吐量,提出了一种改进的基于用户分簇的资源分配算法。基于基站的覆盖范围,选出用户的强干扰基站,以用户-基站干扰关系建立用户-用户干扰图,按用户受到的平均弱干扰划分优先级对用户分簇,再为簇集群预分配频段,为每个簇分配对应频段中效用最大的信道。该资源分配算法能准确反映用户间的干扰关系,保障资源分配公平性。仿真结果表明,当用户密度与基站密度均较大时,与相同场景的已有算法相比,该改进算法有较好的抗干扰能力,能有效提高次用户的吞吐量。     

Distributed self-optimizing interference management in ultra-dense networks with non-orthogonal multiple access

Both non-orthogonal multiple access (NOMA) and ultra-dense network (UDN) are promising technologies in future wireless networks. However, considering the overlapped coverage of small base stations (SBSs) and the spectrum sharing with NOMA, interference management (IM) becomes a more complex and fundamental problem. Moreover, considering the massive SBSs and dynamic network conditions in UDN, more efficient mechanisms need to be designed to deal with the IM issue. Thus, we propose a distributed self-optimizing interference management approach to address both the intra-cell interference caused by NOMA and the inter-cell interference among dense deployed SBSs. Aiming to minimize the interference and guarantee the users’ requirements, we mathematically formulate the joint resource allocation and user selection problem with consideration of the diverse user requirements, complicated interference topology, and limited resources. Furthermore, we consider the imperfections of successive interference cancellation at receivers for separating and decoding superimposed signals and analyze the impacts of residual interference and outage probability in NOMA-based UDNs. For tractability purpose, we introduce interference graph and satisfaction game theory and propose distributed algorithms to solve the problem. Simulation results show that interference can be reduced significantly in UDNs with NOMA compared with the traditional IM approaches.