超密集网络中基于干扰协调的资源分配算法

超密集网络中,严重的小区间干扰制约了终端用户的数据速率,针对该问题,该文提出一种基于干扰协调的资源分配方案。该方案分为两个模块：第一模块基于毫微微接入点(Femtocell Access Points, FAPs)间的干扰程度,将干扰强的FAPs分到同一簇内,同簇内的FAPs共享频带资源,通过FAPs间的协作使不同簇之间实现频谱的复用;第二模块基于最大功率和最低速率的公平性准则进行最优功率分配,动态分配资源。仿真结果表明,该算法在超密集网络场景下能够有效控制FAPs间的干扰,最大化系统吞吐量。     

超密集网络中小小区分簇和子载波分配算法

超密集网络中,严重的小区间干扰制约了终端用户尤其是边缘用户的数据速率。有效地对干扰进行管理、提升边缘用户的数据速率是超密集网络中的研究难点。在超密集网络架构的基础上,提出一种基于图论的不完全染色算法,对网络架构中的基站划分簇,同簇内的不同基站共享频带资源。同时,提出一种子载波分配算法,优先为边缘用户分配信道增益较优的子载波。通过仿真表明,不完全染色算法能够有效地减小小区间干扰,提升系统吞吐量,子载波分配算法在不影响系统吞吐量的基础上能够优化边缘用户的吞吐量。     

基于分簇的小基站用户资源分配方案

为降低密集网络系统层内干扰并提高系统容量,提出一种基于分簇的用户资源分配方案。在分簇阶段,根据小基站到簇头的最大距离方差与簇头间最小距离对初始分簇结果进行消除、合并及分裂操作,实现网络拓扑变化情况下的自适应分簇。在资源分配阶段,通过用户剩余资源有效利用及资源块微调,保障用户业务速率。仿真结果表明,与现有用户资源分配方案相比,该方案提高了系统容量及用户公平性。     

超密集网络中子信道和功率分配研究

超密集网络中,严重的小区间干扰制约了终端用户的数据速率,针对该问题,提出一种基于簇优先级的资源分配方案。该方案分为3个步骤:首先,采用基于图论的染色算法为毫微微接入点(Femtocell Access Points,FAPs)分簇;然后,以簇内每个毫微微用户(Femtocell User Equipments,FUEs)的待发送数据量、排队等待时延以及受干扰强度等作为优先级,计算每个簇的优先级,高优先级的簇可最先获得信道增益好的子信道;最后,利用卡罗需-库恩-塔克(Karush-Kuhn-Tucker,KKT)条件和注水算法为FUEs分配功率。仿真实验表明该方案能够有效地减小Femtocell间的干扰,并能够极大地满足用户的需求,同时提升系统的吞吐量和频谱效率。     

超密集网中基于分簇的功率优化控制方案

超密集网(UDN)的组建可增加热点地区的系统容量,但由于网络中存在严重区间干扰,限制了UDN的部署。为此,提出一种基于小区分簇的功率优化控制方案。通过预先设定的系统平均干扰阈值进行分簇,将系统中干扰较大的小区分到一个簇中,当簇中的小区对其他小区用户的干扰低于设定的阈值时,将此小区从这个簇中去除。该方案的功率控制由控制器根据簇内基站所占的权重值为各个基站分配相应的功率值,从而达到控制目的。仿真结果表明,该方案能够有效抑制网络中的干扰,提高系统吞吐量。     

认知超密集网络用户关联与资源分配联合优化遗传算法

针对下行的异构认知超密集异构网络（UDN）的多维资源配置问题,提出一种以毫微微小区用户最大吞吐量为目标的联合优化用户关联和资源分配的改进遗传算法。首先,在算法开始之前进行预处理,初始化用户可达基站和可用信道矩阵;其次,采用符号编码,将用户与基站以及用户与信道的匹配关系编码为一个二维的染色体;然后,将动态择优复制+轮盘赌作为选择算法,以加快种群的收敛;最后,为避免算法陷入局部最优,在变异阶段加入早熟判决的变异算子,从而在有限次迭代下求得基站、用户、信道的连接策略。实验结果表明,在基站与信道数量一定时,所提算法与三维匹配的遗传算法相比在用户总吞吐量方面提高了7.2%,在认知用户吞吐量方面提高了1.2%,且计算复杂度更低。所提算法缩小了可行解的搜索空间,能在较低复杂度下有效提高认知UDN的总吞吐量。     

基于DQN的超密集网络能效资源管理

小基站的密集随机部署会产生严重干扰和较高能耗问题,为降低网络干扰、保证用户网络服务质量(QoS)并提高网络能效,构建一种基于深度强化学习(DRL)的资源分配和功率控制联合优化框架.综合考虑超密集异构网络中的同层干扰和跨层干扰,提出对频谱与功率资源联合控制能效以及用户QoS的联合优化问题.针对该联合优化问题的NP-Har...     

非完美频谱感知下认知超密集网络的资源分配

针对实际认知超密集网络场景中认知无线电存在非完美频谱感知的情况,提出了一种基于非完美频谱感知的资源分配方案,目标是在考虑跨/同层干扰约束、保障用户服务质量下,最大化非完美频谱感知下认知超密集网络中次级网络的能效。为此,依据网络模型构建能效优化问题,其为混合整数非凸规划问题,先通过分时共享松弛法和丁克尔巴赫法将其转换成等价的凸优化问题,再使用拉格朗日对偶法求其最优解,以此获得最优能效时的子信道和功率分配策略。基于此,提出了一种迭代的子信道和功率分配算法;为权衡计算复杂度,还提出了一种实用的子信道和功率分配算法。仿真结果表明,所提算法都有效地提升了网络能效。     

密集家庭基站网络中基于分簇的资源分配方案

针对家庭基站密集部署情况下的下行干扰问题,提出一种基于分簇的资源分配方案。首先,采用部分频率复用（FFR）技术将网络中所有小区划分成不同的空间,既能抑制宏基站之间的同层干扰,又能降低边缘区域宏基站与家庭基站间的跨层干扰;然后,结合图论的知识及凸优化理论对家庭基站进行分簇,并采用基于用户速率公平的信道分配算法对家庭基站进行子信道分配,抑制家庭基站间的同层干扰;最后,采用分布式功率控制算法对家庭基站功率进行动态调整,进一步提升系统的性能。仿真结果表明：相比传统未分组算法,所提算法的信干噪比（SINR）和吞吐量有明显提高,其中,系统吞吐量低于4 Mb/s的概率降低为30%;同时,与未分组算法相比,所提算法公平性提高了12%,使用户获得更高的满意度。     

基于NOMA的5G超密网计算迁移与资源分配策略

针对5G超密网中移动设备计算能力不足、频谱资源有限的问题,提出了一种基于非正交多址接入（NOMA）的计算迁移与带宽分配策略。首先,对系统模型进行了分析,并在此基础上以最小化设备计算代价为目标对所研究的问题进行形式化定义;然后,将该问题分解成设备的计算迁移、系统的带宽分配和设备的分组匹配三个子问题,并利用模拟退火、内点法和贪心算法对这三个子问题进行求解;最后,通过联合优化算法对上述子问题进行交替性迭代求解,最终获得最优计算迁移和资源分配策略。仿真结果表明,所提出的联合优化策略不但优于传统的正交多址接入（OMA）方式,而且能获得比平均分配带宽的NOMA技术更低的设备计算代价。     

Optimization-based resource allocation in communication networks

The continuously growing number of applications competing for resources in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization Theory can provide the necessary tools to develop such mechanisms that will allocate network resources optimally and fairly among users. The aim of this paper is to provide a starting point for researchers interested in applying optimization techniques in the resource allocation problem for current communication networks. To achieve that we, first, describe the fundamental optimization theory tools necessary to design optimal resource allocation algorithms. Then, we describe the Network Utility Maximization (NUM) framework, a framework that has already found numerous applications in network optimization, along with some recent advancements of the initial NUM framework. Finally, we summarize some of our recent work in the area and discuss some of the remaining research challenges towards the development of a complete optimization-based resource allocation protocol.     

Time-aware utility-based resource allocation in wireless networks

This paper presents a time-aware admission control and resource allocation scheme in wireless networks in the context of a future generation cellular network. The quality levels (and their respective utility) of different connections are specified using discrete resource-utility (R-U) functions. The scheme uses these R-U functions for allocating and reallocating bandwidth to connections, aiming to maximize the accumulated utility of the system. However, different applications react differently to resource reallocations. Therefore, at each allocation time point, the following factors are taken into account: the age of the connection, a disconnection (drop) penalty, and the sensitiveness to reallocation frequency. The evaluation of our approach shows a superior performance compared to a recent adaptive bandwidth allocation scheme (RBBS). In addition, we have studied the overhead that performing a reallocation imposes on the infrastructure. To minimize this overhead, we present an algorithm that efficiently reduces the number of reallocations while remaining within a given utility bound.     

Dynamic routing and resource allocation in WDM transport networks

This contribution concentrates on dynamic routing in Wavelength Division Multiplexing (WDM) networks. It is shown that a strategy based on pre-calculated alternatives and an adaptive dynamic path search performs very well over a wide load range. Moreover, specific effects for the impact of resource allocation strategies in photonic WDM networks are highlighted, especially the influence of wavelength converter usage strategies in networks with partial conversion. The paper also investigates the way non-Poisson traffic behaviour affects performance of routing strategies and presents how the results from dynamic routing investigation can help to optimise the network planning process.     

Optimal resource allocation in multi-class networks with user-specified utility functions

In this work, we consider the problem of resource allocation in multi-class networks, where users specify the value they attach to obtaining different amounts of resource by means of a utility function. We develop a resource allocation scheme that maximizes the average aggregate utility per unit time. We formulate this resource allocation problem as a Markov decision process. We present numerical results that illustrate that our scheme performs better than the greedy resource allocation policy. We also discuss the implications of deliberate lying by users about their utility functions and develop a pricing scheme that prevents such lying.     

A novel model on dynamic resource allocation in optical networks

A novel model on dynamic resource allocation in the WDM optical networks is proposed, basing on the integrated considerations of the impacts of transmission impairments and service classification on dynamic resource allocation in the optical layer. In this model, the priorities of optical connection requests are mapped into different thresholds of transmission impairments, and a uniform method which is adopted to evaluate the virtual wavelength path (VWP) candidates is defined. The Advanced Preferred Wavelength Sets Algorithm (A-PWS) and the heuristic Dynamic Min-Cost & Optical Virtual Wavelength Path Algorithm (DMC-OVWP) are presented addressing the routing and wavelength assignment (RWA) problem based on dynamic traffic and multi priorities in wavelength-routed optical networks. For a received optical connection request, DMC-OVWP is employed to calculate a list of the VWP candidates, and an appropriate VWP which matches the request's priority is picked up to establish the lightpath by analyzing the tra     

基于效用函数的OFDM混合业务资源调度算法*

主要研究了QoS和BE两种混合业务场景下OFDM无线网络下行链路的资源调度问题,提出了一个基于效用函数的跨层资源调度模型,其能够自适应地对两种业务进行资源的联合优化分配。该模型被抽象为一个非线性整数规划问题,优化目标是系统总效用最大化,同时满足同信道干扰（CCI）约束以及QoS业务的质量要求。将该非线性整数规划问题转换为连续松弛凸规划问题进行求解,并结合最优松弛解,提出了一种简单的动态子载波分配算法,即MMU(mix-max-utility) 算法。仿真结果验证了该调度算法能使系统较好地支持混合业务,系统     

Reinforcement learning for resource allocation in LEO satellite networks.

In this paper, we develop and assess online decision-making algorithms for call admission and routing for low Earth orbit (LEO) satellite networks. It has been shown in a recent paper that, in a LEO satellite system, a semi-Markov decision process formulation of the call admission and routing problem can achieve better performance in terms of an average revenue function than existing routing methods. However, the conventional dynamic programming (DP) numerical solution becomes prohibited as the problem size increases. In this paper, two solution methods based on reinforcement learning (RL) are proposed in order to circumvent the computational burden of DP. The first method is based on an actor-critic method with temporal-difference (TD) learning. The second method is based on a critic-only method, called optimistic TD learning. The algorithms enhance performance in terms of requirements in storage, computational complexity and computational time, and in terms of an overall long-term average revenue function that penalizes blocked calls. Numerical studies are carried out, and the results obtained show that the RL framework can achieve up to 56% higher average revenue over existing routing methods used in LEO satellite networks with reasonable storage and computational requirements.     

A fair resource allocation protocol for multimedia wireless networks

Wireless networks are expected to support real-time interactive multimedia traffic and must be able, therefore, to provide their users with quality-of-service (QoS) guarantees. Although the QoS provisioning problem arises in wireline networks as well, mobility of hosts and scarcity of bandwidth makes QoS provisioning a challenging task in wireless networks. It has been noticed that multimedia applications can tolerate and gracefully adapt to transient fluctuations in the QoS that they receive from the network. The additional flexibility afforded by the ability of multimedia applications to tolerate and adapt to transient changes in QoS can be exploited by protocol designers to significantly improve the overall performance of wireless systems. This paper presents a fair resource allocation protocol for multimedia wireless networks that uses a combination of bandwidth reservation and bandwidth borrowing to provide network users with QoS in terms of guaranteed bandwidth, call blocking, and call dropping probabilities. Our view of fairness was inspired by the well-known max-min fairness allocation protocol for wireline networks. Simulation results are presented that compare our protocol to similar schemes.