认知MIMO网络中增强型干扰对齐算法

针对认知多输入多输出（MIMO）网络中传统基于最大信干噪比的干扰对齐算法,在发送多数据流时随着信噪比的增加不易收敛以及数据流之间的干扰突出的问题,提出一种充分考虑数据流间干扰并进行迭代限制的干扰对齐算法。首先,次用户通过编码设计消除主次间的干扰;然后,在消除主用户之间和次用户之间干扰时,根据信道互易性,运用广义瑞利熵计算基于最大信干噪比算法的预编码与干扰抑制矩阵,并在迭代过程中,每次迭代始终使预编码与干扰抑制矩阵先满足干扰功率在期望信号空间最小;最后,结合次用户间MIMO干扰信道、主次用户间构成的MIMO干扰信道以及次用户网络干扰对齐的必要性,推导出次用户可达自由度上限。实验结果表明,相比传统最大信干噪比算法,所提算法在信噪比较低时次用户总容量无明显提高,但随着信干噪比的增加其优势越来越明显;当达到收敛时,所提算法迭代次数比传统最大信干噪比算法约减少40%。因此,所提算法能够提高系统容量且加快收敛。     

基于并行干扰抵消的UFMC系统信道均衡

通用滤波多载波(UFMC)技术作为5G的一种候选波形,传输中不加循环冗余(CP),多径衰落信道下会产生符号间干扰(ISI)以及子载波间干扰(ICI)。针对该问题,提出一种基于并行干扰抵消的均衡算法。首先,根据分析得到多径信道下UFMC系统干扰数学表达式;其次,在采用迫零均衡后,对可靠区间外的数据,根据剩余干扰表达式,近似重构相邻载波及符号间干扰;最后,对各载波并行地进行迭代干扰抵消。通过仿真实验表明,在多径信道下,该算法能够一定程度上降低误码率,提高UFMC系统性能。     

Multicell power allocation method based on game theory for inter-cell interference coordination

As a new technology, coordinated multipoint (CoMP) transmission is included in LTE-Advanced study item. Moreover, the network architecture in LTE-Advanced system is modified to take into account coordinated transmission. Under this background, a novel power allocation game model is established to mitigate inter-cell interference with cellular coordination. In the light of cellular cooperation relationship and centralized control in eNodeB, the power allocation in each served antenna unit aims to make signal to interference plus noise ratio (SINR) balanced among inter-cells. Through the proposed power allocation game algorithm, the users’ SINR can reach the Nash equilibrium, making it feasible to reduce the co-frequency interference by decreasing the transmitted power. Numerical results show that the proposed power allocation algorithm improves the throughput both in cell-center and cell-edge. Moreover, the blocking rate in cell-edge is reduced too.     

非理想信道状态信息下权值可调干扰对齐算法

针对多输入多输出（MIMO）系统获取信道信息过程中存在估计误差、反馈延迟等问题,为了提高低信噪比（SNR）时的系统性能,提出了一种基于权值可调的稳健干扰对齐算法。首先,在理想信道的基础上考虑信道误差的影响重新构建系统模型;然后,采用矩阵投影技术对接收端的信号空间进行分解,分成期望信号子空间与干扰信号子空间两部分;其次,考虑期望信号和干扰信号之间的相互影响,把两者泄漏到相应的子空间的功率加权和作为目标函数运用迭代思想计算出预编码和干扰抑制矩阵;最后,利用计算出的预编码和干扰抑制矩阵推导出存在信道误差的和速率表达式。仿真结果表明与稳健最小干扰泄漏算法相比,在信噪比为10 dB、信道误差方差取值0.05时,系统的频谱效率提升了25%,能量效率提升了38%,因此所提算法在低信噪比时可以有效地提升系统性能。     

基于分簇和Stackelberg博弈的D2D资源分配策略

当前,车辆密集通信场景下存在通信资源利用率低、DUE(D2D user)用户通信质量差等问题。针对上述问题,提出了一种基于分簇和Stackelberg博弈的D2D(device to device)资源分配策略,以解决DUE用户功率分配、信道匹配问题。首先,基于每个信道内DUE用户之间干扰最小原则,该模型对所有DUE用户进行分簇;然后,对于CUE(cellular user)用户与DUE用户簇,构建一对多的Stackelberg主从博弈模型,通过复用链路干扰参数和簇内干扰参数的迭代更新,优化每个DUE用户的发射功率;最后,利用匈牙利算法实现DUE用户簇与CUE用户的最佳信道匹配,最大化DUE用户的容量和。仿真结果表明,与基于价格迭代、等功率分配和高能效干扰约束的几种功率分配算法相比,所提算法能有效提升DUE用户的总容量。     

基于局部信息交互的博弈学习算法

博弈理论在无线通信领域的应用愈加广泛并逐渐成为解决无线频谱资源分配的重要方法之一。论文关注5G通信系统中的异构信道选择问题,针对该问题传统集中式优化机制系统效率较优但优化开销大,而传统分布式优化机制优化开销较少但系统效率受限。为实现系统效率与优化开销的有效折衷,论文将问题建模为局部合作博弈,提出基于局部信息交互的博弈学习算法,实现了系统在分布式优化机制下达到最优性能。仿真结果验证了算法的最优性,收敛性和稳健性。     

无人机自组织网络多包接收智能信号检测算法

在无人机自组织网络(UAV Ad Hoc Network, UANET)中,传统的基于单包接收的信号检测算法极大限制了多路传输共享的并发通信性能。针对此问题,利用迭代并行干扰消除技术和多输入多输出技术并联合机器学习设计出一种UANET多包接收智能信号检测算法。该算法保留了迭代并行干扰消除算法的整体结构,采用最合适的深度神经网络来代替传统的基于信道模型的复杂计算,使得分簇UANET的簇头节点不仅可以对任意无记忆固定信道进行处理,而且也不需要去获取准确的信道状态信息便可以同时正确接收来自多个发送节点并发传输过来的数据包。仿真结果表明,该算法可以在不同场景下有效降低系统误码率(Symbol Error Rate, SER),从而有效增加UANET的通信并发度。在线性信道多节点通信场景下,所提出的算法相比于最优MAP(Maximum A Posteriori,最大后验概率)检测算法,系统误码率可以降低约25%。     

认知无线网络的Rubinstein博弈频谱共享方法研究

针对传统的功率控制算法限制认知用户的发射功率而影响其服务质量(Quality of Service, QoS)的问题,提出了一种基于功率控制的多人Rubinstein博弈频谱分配算法。该算法通过牛顿迭代公式降低认知用户的发射功率并依据各个用户间的干扰得到相应的链路质量;将经济学中的贴现因子与用户的链路质量建立映射关系;通过链路质量调整认知用户子博弈顺序使得网络总传输速率达到一个相对稳定的状态。仿真结果表明:多个认知用户在同一信道下共享频谱时,采用多人Rubinstein博弈算法对系统的总传输速率有明显的提升,使系统处于稳定且高速的传输状态并节省了一半以上的频谱分配时间。     

基于前导干扰消除的FBMC系统加权信道估计算法

针对FBMC系统信道估计时存在虚部干扰问题,提出一种新的信道估计算法。首先,该算法采用了基于虚部干扰消除的新导频结构;然后,利用两列导频分别作粗信道估计;最后,对粗信道估计采用加权方式进行精信道估计,进一步提高信道估计性能。仿真结果表明,与传统虚部干扰消除算法（IIE）相比,新导频结构算法在误码率为1%时,可获得1~2 dB的性能提升。     

基于势博弈的认知全双工中继选择策略研究

在对主用户干扰功率限制、自干扰限制和总功率干扰限制的网络中,针对认知中继选择算法复杂度较高的问题,提出基于势博弈理论的认知全双工协作网络下中继选择策略。认知中继选择问题被建模为使用认知协作网络的系统速率作为共同效用函数的势博弈模型,并分析出在没有不可行策略集信息的前提下,所提的博弈可以保证纯策略纳什均衡(NE)的存在性和可行性条件。在此基础上,给出全双工中继选择迭代算法,并对算法的复杂度进行讨论。仿真分析表明,所提算法在较低复杂度的情况下,能够获得最优或者接近最优速率的性能,并与传统的半双工中继模式相比,性能也有明显提升。     

基于干扰估计的WSN链路自适应控制方法

针对现有的无线传感器网络传输功率控制算法未充分考虑实际信道干扰的问题,给出了基于干扰估计的最优传输功率计算方法;考虑到通信信道及活跃节点数目的时变性,提出了自适应速率调整算法;最后通过试验验证了所提方法的可行性。     

基于序贯博弈的Ad Hoc网络功率控制算法

针对分布式拓扑结构的Ad Hoc网络,将用户处于异步竞争方式下的功率控制问题抽象为动态博弈模型,通过多步迭代的逆向归纳法,逐步分析了两用户组及多用户组下的序贯博弈过程。并提出一种分布式的功率控制博弈算法,有效求解了用户发射功率的均衡策略,来优化用户之间的并发传输能力,提高频谱效率。仿真实验表明,建立基于序贯博弈的功率控制过程,能够有效地减轻用户之间的干扰影响,提升用户接收信干噪比质量,从而改善了系统吞吐率性能收益。     

MIMO系统中基于非正交多址接入的功率分配算法研究

随着移动通信技术的发展,通信服务已变成人类日常生活中不可或缺的部分.尤其是近年来各类智能终端的大众化,使得接入无线通信的用户数和人们对通信服务的需求均呈爆炸式的增长.但现如今可用的频谱资源是有限的,且传统的正交多址接入系统的用户接入数受限,很难满足用户日益增长的需求.非正交多址接入允许在同一时频资源上复用多个用户,极大...     

Power and delay optimisation in multi-hop wireless networks

In this paper, we study the optimisation problem of transmission power and delay in a multi-hop wireless network consisting of multiple nodes. The goal is to determine the optimal policy of transmission rates at various buffer and channel states in order to minimise the power consumption and the queueing delay of the whole network. With the assumptions of interference-free links and independently and identically distributed (i.i.d.) channel states, we formulate this problem using a semi-open Jackson network model for data transmission and a Markov model for channel states transition. We derive a difference equation of the system performance under any two different policies. The necessary and sufficient condition of optimal policy is obtained. We also prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate and the optimal transmission rate can be either maximal or minimal. That is, the ‘bang-bang’ control is an optimal control. This optimality structure greatly reduces the problem complexity. Furthermore, we develop an iterative algorithm to find the optimal solution. Finally, we conduct the simulation experiments to demonstrate the effectiveness of our approach. We hope our work can shed some insights on solving this complicated optimisation problem.     

面向高密度Wi-Fi部署环境下的抗干扰策略

高密度Wi-Fi部署环境如银行、写字楼、mall等,普遍存在的同质干扰问题是制约用户体验及网络质量提升的痛点。首先,针对该问题研究了干扰检测技术并提出一种识别和度量Wi-Fi节点干扰的干扰程度评估模型。随后提出了“容忍+避让”的抗干扰策略,容忍策略基于捕获效应理论及无线资源管理技术,提升了干扰条件下数据通信质量;避让策略采用局部化信道自协调算法和去中心分布式架构,解决同质干扰中的冲突问题。最后,实现了抗干扰机制WifiAAS。测试结果表明,该机制可提升10%设备性能,且未带来过大开销。     

Multi-agent graphical games with input constraints: an online learning solution

This paper studies an online iterative algorithm for solving discrete-time multi-agent dynamic graphical games with input constraints. In order to obtain the optimal strategy of each agent, it is necessary to solve a set of coupled Hamilton-Jacobi-Bellman (HJB) equations. It is very difficult to solve HJB equations by the traditional method. The relevant game problem will become more complex if the control input of each agent in the dynamic graphical game is constrained. In this paper, an online iterative algorithm is proposed to find the online solution to dynamic graphical game without the need for drift dynamics of agents. Actually, this algorithm is to find the optimal solution of Bellman equations online. This solution employs a distributed policy iteration process, using only the local information available to each agent. It can be proved that under certain conditions, when each agent updates its own strategy simultaneously, the whole multi-agent system will reach Nash equilibrium. In the process of algorithm implementation, for each agent, two layers of neural networks are used to fit the value function and control strategy, respectively. Finally, a simulation example is given to show the effectiveness of our method.     

分布式机器学习作业性能干扰分析与预测

通过分析分布式机器学习中作业性能干扰的问题,发现性能干扰是由于内存过载、带宽竞争等GPU资源分配不均导致的,为此设计并实现了快速预测作业间性能干扰的机制,该预测机制能够根据给定的GPU参数和作业类型自适应地预测作业干扰程度。首先,通过实验获取分布式机器学习作业运行时的GPU参数和干扰率,并分析出各类参数对性能干扰的影响;其次,依托多种预测技术建立GPU参数-干扰率模型进行作业干扰率误差分析;最后,建立自适应的作业干扰率预测算法,面向给定的设备环境和作业集合自动选择误差最小的预测模型,快速、准确地预测作业干扰率。选取5种常用的神经网络作业,在两种GPU设备上设计实验并进行结果分析。结果显示,所提出的自适应干扰预测（AIP）机制能够在不提供任何预先假设信息的前提下快速完成预测模型的选择和性能干扰预测,耗时在300 s以内,预测干扰率误差在2%~13%,可应用于作业调度和负载均衡等场景。     

Joint routing, scheduling, and power control for multichannel wireless sensor networks with physical interference

Reliability and real-time requirements bring new challenges to the energy-constrained wireless sensor networks, especially to the industrial wireless sensor networks. Meanwhile, the capacity of wireless sensor networks can be substantially increased by operating on multiple nonoverlapping channels. In this context, new routing, scheduling, and power control algorithms are required to achieve reliable and real-time communications and to fully utilize the increased bandwidth in multichannel wireless sensor networks. In this paper, we develop a distributed and online algorithm that jointly solves multipath routing, link scheduling, and power control problem, which can adapt automatically to the changes in the network topology and offered load. We particularly focus on finding the resource allocation that realizes trade-off among energy consumption, end-to-end delay, and network throughput for multichannel networks with physical interference model. Our algorithm jointly considers 1) delay and energy-aware power control for optimal transmission radius and rate with physical interference model, 2) throughput efficient multipath routing based on the given optimal transmission rate between the given source-destination pairs, and 3) reliable-aware and throughput efficient multichannel maximal link scheduling for time slots and channels based on the designated paths, and the new physical interference model that is updated by the optimal transmission radius. By proving and simulation, we show that our algorithm is provably efficient compared with the optimal centralized and offline algorithm and other comparable algorithms.     

WMN中的干扰避免部分重叠信道分配算法

针对无线mesh网络(wireless mesh networks,WMN)中存在的信道干扰问题,提出一种基于部分重叠信道(partially overlapping channels,POC)的负载平衡且干扰避免的信道分配算法。通过基于Huffman树的通信接口分配方法连接邻居节点的接口;根据网络干扰情况,对链路进行迭代信道分配,使用静态链路调度保证网络连接;利用启发式算法优先为重要程度较高的链路分配无干扰时隙,对链路调度进行优化。仿真结果表明,在具有混合流量的WMN中,所提算法可以显著提升网络吞吐量,降低网络干扰与平均丢包率,改善网络性能。     

Aprojected gradient based game theoretic approach for multi-user power control in cognitive radio network

The fifth generation (5G) networks have been envisioned to support the explosive growth of data demand caused by the increasing traditional high-rate mobile users and the expected rise of interconnections between human and things. To accommodate the ever-growing data traffic with scarce spectrum resources, cognitive radio (CR) is considered a promising technology to improve spectrum utilization. We study the power control problem for secondary users in an underlay CR network. Unlike most existing studies which simplify the problem by considering only a single primary user or channel, we investigate a more realistic scenario where multiple primary users share multiple channels with secondary users. We formulate the power control problem as a non-cooperative game with coupled constraints, where the Pareto optimality and achievable total throughput can be obtained by a Nash equilibrium (NE) solution. To achieve NE of the game, we first propose a projected gradient based dynamic model whose equilibrium points are equivalent to the NE of the original game, and then derive a centralized algorithm to solve the problem. Simulation results show that the convergence and effectiveness of our proposed solution, emphasizing the proposed algorithm, are competitive. Moreover, we demonstrate the robustness of our proposed solution as the network size increases.