蜂窝网络下行链路中基于干扰图的干扰对齐算法

针对多用户蜂窝网络的下行链路,提出了基于干扰图的干扰对齐算法。首先用干扰图表示蜂窝网络的小区间干扰（ICI）分布,每五个相邻小区划分为一簇。然后在簇内先根据对称信道和不对称信道,分别设计接收波束赋形矩阵将四个ICI对齐到两个干扰子空间,再设计发送波束赋形矩阵与干扰子空间正交。最后通过簇的平移实现蜂窝网络内所有活动小区的干扰对齐。该算法减少了基站和用户的天线数及计算复杂度。仿真结果表明所提算法消除了ICI、用户间干扰（IUI）及簇间干扰,有效提高了蜂窝网络容量。      

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。      

多小区MIMO系统中基于泄漏的多用户下行链路预编码新算法

针对多小区MIMO系统的多用户下行链路预编码设计是未来移动通信系统研究的重要内容之一。本文在蜂窝多小区MIMO通信环境下,基于信漏噪声比(Signal-to-Leakage-Noise Ratio,SLNR)最大准则,提出了改进的多用户下行链路线性预编码算法。通过在优化目标中考虑接收端白化滤波器的影响,该预编码算法实现了对用户间干扰和小区间干扰(OCI)的同步抑制消除;在求解预编码矩阵优化问题时,本文基于QR分解提出了一种新的低复杂度求解算法,该算法能以较低的计算复杂度实现系统性能的有效提升。仿真实验结果表明,本文的改进SLNR线性预编码算法能有效抑制小区间干扰(OCI)的影响,使多小区MIMO系统获得更高的和速率(Sum Rate)容量。      

多用户认知网络中基于Grassmann流形梯度法的干扰对齐算法

针对多个主用户和多个次用户的MIMO认知网络,本文给出了一种不依赖信道互惠性和不需要前后向链路交替式迭代的干扰对齐方法。对于次用户,首先,通过对其进行编码,建立了消除主次用户间相互干扰后的等效模型。然后,在等效模型的基础上,以最大化总容量为目标函数设计预编码矩阵,并采用基于Grassmann流形上的梯度法对目标函数进行求解得到预编码。最后,在接收端以最大信干噪比准则来设计接收滤波器矩阵。仿真结果显示,在低信噪比时,本文算法与现有典型算法性能相同,而在高信噪比时本文算法性能更优。      

基于公平性对偶理论的多小区下行协同波束成形算法

为了实现最大化最差用户速率准则下的多小区下行协同波束成形优化,首先基于拉格朗日对偶理论推导出了多小区下行波束成形最差用户信干噪比最大化优化问题的虚拟上行对偶问题,进而提出了一种联合利用二分法和几何规划有效迭代解决该虚拟上行对偶问题的算法。通过把对偶上行链路优化问题的解转换到原始下行链路优化问题,从而获得多小区下行链路波束成形的波束矢量和发射功率。仿真结果表明,相比已有的多小区下行协同波束成形算法,所提算法在最差用户速率性能以及能效归一化速率性能指标方面都具有明显优势。     

基于容量最大化的非迭代干扰对齐优化算法

现有分布式迭代干扰对齐算法没有考虑基站与用户终端处理能力的差异,使得基站和用户端都具有较高的计算复杂度和系统开销。针对下行链路和上行链路分别提出一种基于容量最大化准则的非迭代干扰对齐优化算法。所提算法在基站端使用基于容量最大化准则的优化方法求解发送预编码矩阵或接收抑制矩阵,在用户端使用迫零准则生成接收抑制矩阵或随机生成发送预编码矩阵。分析和仿真结果表明,所提的2种算法基站端的计算复杂度和系统开销与现有算法相当,但用户端的计算复杂度和系统开销大大降低,并且可以获得与最大信干噪比(Max-SINR)算法相当的系统性能。     

3用户MIMO干扰信道中稳健的机会干扰对齐方法

本文根据传统的干扰对齐思想,在缩小干扰空间之间弦距的同时尽量增大信号-干扰空间之间弦距,以此为目标设计用户选择准则,并在接收端采用预处理矩阵智能选取技术最大化信道容量,提出了3用户MIMO干扰信道中稳健的机会干扰对齐方法。相比文献[1]方法,所提出的方法不但具有更高的系统瞬时性能稳健性,而且还具有更高的信道容量,只是在复杂度方面略有增加。仿真结果证明了本文方法的有效性。     

三小区环境中基于三角分解的低复杂度干扰对齐算法

针对3小区每小区2个边缘用户的MIMO下行链路,在不改变系统自由度的前提下对联合信道进行下三角分解得到系统的等效模型;在此基础上,根据不同小区受到的干扰不同,该文提出一种线性干扰对齐算法同时消除小区间干扰和小区内干扰.该算法利用联合信道分解可以先消除一半的小区间干扰,并根据不同小区的用户设计其干扰抑制矩阵,减少了用户端接收矩阵个数及相应处理,从而降低了接收端的复杂度.仿真表明,随着数据流的增加,系统容量和能量效率都有明显的提高,在自由度相同的情况下,天线数不同对系统容量和能量效率有较大影响.     

能量有效的认知多小区协同波束赋形算法

针对认知多小区多用户下行传输链路,提出了一种基于能量效率最大化准则的协同波束赋形优化方法。该方法采用迫零消除小区内用户间干扰,在保证用户最小速率需求及认知干扰约束的同时,实现了能量效率和频谱效率的同步改善。为了分布式求解优化问题,通过约束泄露干扰并利用半定松弛,将其转换为凸问题,在此基础上,采用部分对偶分解方法将多小区联合优化问题分解为一组单小区优化问题,从而实现了分布式求解。仿真结果表明,该方法不仅实现了能量效率和频谱效率的有效折中,而且达到了集中式算法的性能。     

一种基于动态功率分配的干扰对齐优化方法

干扰对齐是解决无线通信系统中同频干扰的有效手段,显著地提高了多小区MIMO系统的容量。为了解决常规干扰对齐算法只在空间域上对齐的不足,提出一种联合功率分配的干扰对齐优化方案。基于信道矩阵的弦距离计算和优化干扰对齐方案的预编码矩阵;然后在用户的各个数据流之间进行动态功率分配,使干扰进一步减小。仿真结果表明,相比已有的预编码矩阵设计方案,系统容量有明显的改进,降低了系统误码率,具有良好的工程适用性。     

基于干扰对齐的认知无线电网络中用户性能的优化设计

干扰对齐（IA）是一种新兴的干扰管理技术,被广泛的应用于认知无线电（CR）网络之中,以期消除认知用户（CU）对授权用户（AU）的干扰。然而干扰对齐技术带来了授权用户接收信干噪比（SINR）的下降,使得授权用户的传输速率受到影响。在低信噪比下,授权用户性能无法保证。本文创新性地提出了两种算法设计授权用户预编码滤波器,优化其传输速率,极大地提高了授权用户的性能。同时,为了提高认知用户的性能,本文给出了认知用户的干扰抑制滤波器的优化方案,最大化认知用户的和速率。仿真结果表明,这两种算法都能在一定程度上提高授权用户的传输速率,同时认知用户的传输速率也得以保证。      

Ad hoc self-healing of OFDMA networks using UAV-based relays

Due to the demand for constantly increasing data rates, especially within LTE (Long Term Evolution) and LTE-Advanced networks, today’s mobile radio systems are operating more and more at their capacity limits. Consequently, techniques to compensate cell outage and cell overload situations are needed. Existing approaches to address these problems are typically static solutions and are accompanied by Inter-Cell Interference (ICI), like the introduction of femtocells. In this paper we propose an UAV-based (Unmanned Aerial Vehicle) approach in order to provide ad hoc self-healing of cellular networks. Thereby, the UAVs are acting as mobile relays and offload traffic to surrounding cells with free resources. We introduce the genetic Interference-aware Positioning of Aerial Relays (IPAR) algorithm which is able to find suitable positions for the UAVs that maximize the downlink throughput of the cellular network. The algorithm is evaluated for hexagonal as well as irregular cellular network layouts and is based on an analytical model for deriving the key performance indicators throughput, system loss and free remaining resources in the cells for the downlink. In order to decrease the introduced interference between the UAVs and surrounding base stations, a random frequency allocation scheme is applied. Based on an exact analytical model for the SINR (Signal to Interference and Noise Ratio) of users served by UAVs, it is shown that by different hopping distributions a reduction of the SINR up to 11.6 dB can be achieved compared to the worst case performance of systems without resource planning.     

Coverage probability of cellular networks using interference alignment under imperfect CSI

Interference alignment (IA) is well understood to approach the capacity of interference channels, and believed to be crucial in cellular networks in which the ability to control and exploit interference is key. However, the achievable performance of IA in cellular networks depends on the quality of channel state information (CSI) and how effective IA is in practical settings is not known. This paper studies the use of IA to mitigate inter-cell interference of cellular networks under imperfect CSI conditions. Our analysis is based on stochastic geometry where the structure of the base station (BS) locations is considered by a Poisson point process (PPP). Our main contribution is the coverage probability of the network and simulation results confirm the accuracy.     

Resource Allocation for QoS-Aware OFDMA Using Distributed Network Coordination

This paper considers resource allocation for downlink orthogonal frequency-division multiple access (OFDMA). We propose a subcarrier and power-allocation method that differentiates users per service type to fulfill the quality-of-service (QoS) requirements of each user. Network coordination improves the performance of users at the border of the cells and, thus, decreases the minimum sum power for users with guaranteed performance (GP). Best effort (BE) users are then scheduled to maximize their sum capacity. The proposed method only assumes causal coordination between base stations and can be seen as a generalized macro diversity scheme suited for distributed networks. Numerical results show that network coordination increases the ratio of satisfied GP users, as well as the average data rate of BE users.     

Joint backhaul bandwidth and power allocation in heterogeneous cellular networks: A two‐level approach

We investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.     

认知OFDM系统中针对视频业务用户的资源分配方案

在多用户认知无线电OFDM系统中,针对实时视频业务用户,本文提出一种计算复杂度低的资源分配方案。该方案采用鱼群算法分配子载波,并提出简单功率干扰(Simple Power Interference, SPI)约束功率分配算法。目标是在满足总功率预算并且保证不干扰主用户的前提下,最大化系统的下行系统容量。仿真分析表明,在视频业务用户场景中,本文所提算法能有效提高下行系统速率,性能接近最优且复杂度低。     

认知无线电下行链路中的频谱共享算法

该文提出了一种适用于下行认知无线电系统的频谱共享算法,在保证对授权用户的干扰低于限制和总发射功率受限的条件下最大化系统总容量。分析了对授权用户的干扰并给出设置干扰限制的方法,在此基础上分两步实现总容量的最大化:先通过最大信道信干噪比原则实现最优的子载波分配;然后利用提出的双注水方法计算最优的功率分配。仿真结果表明,相比传统的频谱共享算法,该算法可以获得显著的系统容量增益。     

功率约束条件不同时下行相干CoMP传输异构网的性能

本文研究在异构网中进行相干协作多点传输时低复杂度迫零（ZF）预编码的性能。我们考虑在同构网中常用的低复杂度ZF预编码结构,即先进行ZF波束形成再进行功率分配,分析在采用和功率约束及单基站功率约束下使和数据率最大的功率分配时协作传输的性能,并与实际系统中常用的等功率分配的性能进行比较。分析结果表明,在异构网多载波系统中进行下行相干协作传输时,与在同构网中的结果不同,协作传输后单基站功率约束的性能远优于其他功率约束下的性能。但是,当协作的宏基站与微基站最大功率差别较大时,宏用户的性能在协作后甚至不如不进行协作传输,而微用户的性能会有所提高。这意味着在异构网中设计协作预编码时必须考虑单基站功率约束,而且不能只对功率分配进行优化设计。      

Energy-Efficient Resource Allocation in Mobile Networks with Distributed Antenna Transmission

This paper address the energy conservation issues in wireless downlink of mobile networks with distributed antenna transmission. From the basic information theory for MIMO channels, we derived a simple energy efficiency defined as number of bits per Watt. we then identified three approaches to improve the optimal energy efficiency with a higher capacity, which include alleviating channel fading loss, mitigating the interference, and increasing the number of antennas. We considered the scenario of a single cell with distributed antennas to jointly investigate above three factors. We first proposed a beamforming based energy-efficient resource allocation algorithm, which can achieve the optimal energy efficiency with a higher capacity through adaptively allocating resources and managing interferences. Due to the computational complexity of this algorithm and real-time processing requirement, we further proposed a low-complexity antenna-selection based resource allocation algorithm, which is more tractable and only with slightly performance loss. Finally, we compared different network configurations with these algorithms by extensive simulations, the results demonstrate that the proposed algorithms in distributed antenna configurations achieve better energy efficiency at a high operational throughput point.