基于深度强化学习的反向散射网络资源分配机制

为了提升反向散射网络中物联网设备的平均吞吐量,提出了一种资源分配机制,构建了用户配对和时隙分配联合优化资源分配模型。由于该模型直接利用深度强化学习(Deep Reinforcement Learning,DRL )算法求解导致动作空间维度较高且神经网络复杂,故将其分解为两层子问题以降低动作空间维度：首先,基于深度强化学习算法,利用历史信道信息推断当前的信道信息以进行最优的用户配对;然后,在用户固定配对的情况下,基于凸优化算法,以最大化物联网设备总吞吐量为目标进行最优的时隙分配。仿真结果表明,与其他资源分配方法相比,所提资源分配方法能有效提升系统吞吐量,且有较好的信道适应性和收敛性。     

Robust power and subcarrier allocation algorithm for cognitive network based on interference efficiency maximization

For the cognitive OFDMA uplink communication system,a robust power and subcarrier allocation algorithm based on maximum interference efficiency was proposed.Firstly,considering primary user interference constraint,secondary user transmit power constraint,subcarrier allocation constraint and secondary user minimum rate constraint,a robust resource optimization model based on outage probability was established.Then,by using Bernstein approximation and Dinkelbach’s method,the original non-convex problem based on outage probability was transformed into an equivalent convex optimization one,and the analytical solution was obtained by Lagrangian dual function method.Meanwhile,the computational complexity and robust sensitivity of the algorithm were analyzed.The simulation results show that the proposed algorithm has better interference efficiency and robustness.     

网络虚拟化中动态资源分配算法研究

针对网络虚拟化中资源分配问题,提出一种动态的资源分配算法。算法根据物理节点平均负载差异度,结合当前物理网络容忍的节点负载差异动态地进行虚拟节点迁移,并通过综合影响因子为虚拟节点选择合适的目标宿主,减少虚拟节点迁移对物理链路带宽和虚拟链路时延的影响。仿真实验表明,该算法能使物理节点上的负载分布均衡,同时对时延和带宽的影响较小。     

Joint downlink and uplink resource allocation for multi-carrier SWIPT system

A multi-carrier simultaneous wireless information and power transfer (SWIPT) communication system including one base station (BS) and one user was investigated,where both uplink and downlink adopt orthogonal frequency division multiplexing (OFDM).In the downlink,the BS transmited information and power to the user simultaneously.In the uplink,the user transmited information to the BS by using the power harvested from the BS in the downlink.The weighted sum of the downlink and uplink achievable rates by jointly optimizing subcarrier allocation and power allocation of the uplink and downlink were aimed to maximized.An optimal algorithm to solve the joint resource allocation problem was proposed,which was based on the Lagrange duality method and the ellipsoid method.Finally,the result shows the performances of the proposed algorithm by computer simulations.     

基于合作博弈的CPS通信资源分配算法

为了提高信息物理融合系统的通信速率,同时保证各用户的QoS需求,提出了一种基于合作博弈的CPS通信资源分配算法。将CPS中OFDMA网络下行链路的资源分配过程建模为多用户间的合作博弈,通过求解纳什议价解,实现用户间的Pareto最优性。仿真结果表明,该算法在系统速率最大化和用户公平性上获得了很好的折中,与最大化系统速率算法相比更具有公平性,与最大化最小公平性算法相比速率提高了34%,在一定程度上提高了CPS通信网络的性能。     

OFDMA协同通信系统子载波和功率联合分配算法

针对OFDMA协同通信系统资源分配仅考虑平均功率下的子载波分配,中继存在未用功率情况,研究子载波分配后中继剩余功率分配问题。提出一种既满足业务QoS需求又兼顾用户间公平性的子载波和功率联合分配算法,并设计一种基于二分法的功率注水分配方案。测试表明,该算法能在满足业务QoS需求及用户公平性的同时,提升系统容量。     

Energy-efficient resource allocation scheme for device-to-device communication underlaying LTE-A uplink

To cope with the co-channel interference between cellular links and device-to-device (D2D) links concurrently transmitting with the long term evolution-advanced (LTE-A) uplink spectrum,a joint resource allocation scheme was pro-posed to maximize the global energy efficiency of D2D links.The above problem can be decomposed into the power control subproblem and the channel assignment subproblem.Specifically,the power control subproblem can be optimally solved with the help of Dinkelbach method and Lagrange duality.Based on the above results,the channel assignment subproblem turns out to be the set packing problem which was generally NP-hard problem,Therefore,a heuristic algo-rithm was further devised to achieve a tradeoff between performance and complexity.Simulations show that the proposed joint resource allocation scheme outperforms the ones where only single resource variable is optimized,and it achieves the polynomial-time complexity at only minor performance loss when compared to the global optimum.     

Cluster-based resource allocation mechanism for Femtocell network

A based-cluster resource allocation mechanism was proposed for suppressing interference between Femtocell and Macrocell in Macro-Femto network.The mechanism included three parts,FBS was put in disjoint clusters by graph theory and convex optimization,then a sub-channel allocation algorithm was adopted to assign sub-channel to the FUE and MUE based on fairness of rate.In the end,power allocation was performed by using distributed algorithm.Simulation results show that the proposed mechanism effectively suppresses cross-tier and co-tier interference in the Macro-Femto network,improves the average rate,meanwhile satisfies the requirements of fairness of rate,which makes users more satisfied.     

基于选择性子载波的OFDM中继信道无线资源分配算法

针对OFDM中继信道,本文提出了一种基于选择性子载波的无线资源分配算法.文中通过选择信道条件最好的部分子载波进行平均功率分配,并对中继子载波进行优化分配.仿真结果表明,该算法不仅能够提高系统可获取的传输速率,而且可以降低运算复杂度.     

Research on the interference control and resource allocation in D2D communication

In order to solve the problem of interference caused by D2D users reuse cellular resources in cellular network,an algorithm of interference control and resource allocation of D2D communication in single cell cellular system was proposed.Firstly,an interference graph was constructed according to the interference between users in the system to find the reusable channel resource for D2D users.Then,the channel resource pre-allocation and the channel switching policy were performed for D2D users in sequence according to the preset D2D user priority.The simulation results show that the proposed algorithm can significantly improve the system throughput and the access rate of D2D users so that the system can achieve better performance in the presence of small or large number of D2D users.     

机器类通信中集中式与分布式Q学习的资源分配算法研究

针对海量机器类通信（massive machine type communication,mMTC）场景,以最大化系统吞吐量为目标,且在保证部分机器类通信设备（machine type communication device,MTCD）的服务质量（quality of service,QoS）要求前提下,提出两种基于Q学习的资源分配算法：集中式Q学习算法（team-Q）和分布式Q学习算法（dis-Q）。首先基于余弦相似度（cosine similarity,CS）聚类算法,考虑到MTCD地理位置和多级别QoS要求,构造代表MTCD和数据聚合器（data aggregator,DA）的多维向量,根据向量间CS值完成分组。然后分别利用team-Q学习算法和dis-Q学习算法为MTCD分配资源块（resource block,RB）和功率。吞吐量性能上,team-Q 和 dis-Q 算法相较于动态资源分配算法、贪婪算法分别平均提高了 16%、23%;复杂度性能上,dis-Q算法仅为team-Q算法的25%及以下,收敛速度则提高了近40%。     

大规模MIMO OFDMA下行系统能效资源分配算法

针对大规模多输入多输出（MIMO）正交频分多址(OFDMA)下行移动通信系统,提出了一种基于能效最优的资源分配算法。所提算法在采用迫零(ZF)预编码的情况下,以最大化系统能效的下界为准则,同时考虑每个用户的最低速率要求,通过调整带宽分配、功率分配和基站天线数分配来优化能效函数。首先根据优化条件提出了一种迭代算法确定每个用户的带宽分配,然后利用分数规划的性质并采用凸优化方法,通过联合调整基站端的发射天线数和用户的发射功率来优化能效函数。仿真结果表明,所提算法在较少迭代次数的同时能够取得较好的系统能效性能和吞吐量性能。     

An elastic resource allocation algorithm enabling wireless network virtualization

Following the wired network virtualization, virtualization of wireless networks becomes the next step aiming to provide network or infrastructure providers with the ability to manage and control their networks in a more dynamic fashion. The benefit of the wireless mobile network virtualization is a more agile business model where virtual mobile network operators (MNOs) can request and thus pay physical MNOs in a more pay‐as‐you‐use manner. This paper presents some resource allocation algorithms for joint network virtualization and resource allocation of wireless networks. The overall algorithm involves the following two major processes: firstly, to virtualize a physical wireless network into multiple slices, each representing a virtual network, and secondly, to carry out physical resource allocation within each virtual network (or slice). In particular, the paper adopts orthogonal frequency division multiplexing (OFDM) as its physical layer to achieve more efficient resource utilization. Therefore, the resource allocation is conducted in terms of sub‐carriers. Although the motivation and algorithm design are based on IEEE 802.16 or WiMAX networks, the principle and algorithmic essence are also applicable to other OFDM access‐based wireless networks. The aim was to achieve the following design goals: virtual network isolation and resource efficiency. The latter is measured in terms of network throughput and packet delivery ratio. The simulation results show that the aforementioned goals have been achieved. Copyright © 2012 John Wiley & Sons, Ltd.     

Radio resource allocation using genetic algorithm in heterogeneous networks

Radio resource management to enable user association and resource block (RB) allocation is crucial for enhancing the performance of heterogeneous networks (HetNets), which are required for fifth generation (5G) mobile networks. This paper proposed a resource allocation technique based on a genetic algorithm (GA) for use in HetNets. We aimed to optimize user association and RB allocation simultaneously to fulfill multiple objectives, such as throughput and fairness measure. In addition to the four primary phases used in GA process, namely initialization, crossover, mutation, and selection, a further operator was provided for managing illegal offspring generated during a GA process. We performed a simulation to compare the proposed GA‐based approach with best channel quality indicator (CQI) algorithm and integer linear programming (ILP) approach in terms of total throughput and fairness measure. The simulation results revealed that the total throughput obtained using the proposed approach is 32.7% and 37.6% better than that obtained using the ILP and best CQI approaches, respectively. Moreover, the fairness measure obtained using the proposed GA‐based approach was 31.8% and 33.2% higher than that obtained using ILP and best CQI approaches, respectively.     

基于无线供能反向散射通信系统安全性的稳健机会约束优化算法设计

针对无线供能反向散射通信系统中信道不确定性以及安全性问题,提出一种考虑系统安全性的稳健机会约束优化算法.首先,在考虑窃听数据传输速率约束、最小能量收集约束和设备反射率约束条件下,建立一个基于信道不确定性的最大化系统传输速率最小值的优化问题.为求解该问题,应用基于伯恩斯坦型不等式的安全近似方法,将原不确定性问题转换为确定...     

基于社会福利最大化的柔性业务资源分配研究

对经济学方法在无线资源管理中的应用进行了研究,考虑业务、用户、资源等多个域,将无线资源分配看作生产–消费模型,兼顾用户公平性原则,针对不同业务的QoS（quality of service）要求采用不同的资源分配方法,建立了基于社会福利最大化的资源分配模型。采用基于用户柔性业务的调度算法优化所提模型,综合考虑用户效用、网络效益以及运营商收益,实现了基于社会福利最大化的柔性业务资源分配。仿真结果验证了所提算法的优越性。     

一种改进的多维计算资源任务分配算法研究

当今时代,互联网技术发展迅速,人们的社交需求日益增长,网络爬虫技术已被成熟地应用于各大搜索引擎和检索领域。文章针对分布式爬虫系统中的任务分配问题,提出了具体的爬行任务分配算法。本算法建立了多维度计算机资源模型,采用优先匹配启发式算法进行爬行任务的静态分配,通过求解目标函数,使整个系统的费用开销最小化。实验证明该算法能在满足系统需求的前提下,当系统需求确定时,使得总费用最小。     

User-centric energy efficiency fairness in backscatter-assisted wireless powered communication network

In order to address the unfair user-centric energy efficiency (EE) problem caused by channel difference in the backscatter-assisted wireless powered communication network,a resource allocation scheme was proposed.Firstly,a mixed integer nonconvex fractional programming problem was formulated to maximize the minimum user-centric EE,subject to the quality of service and energy-causality constraints.Based on the generalized fractional programming theory,the original problem was transformed into a mixed integer nonconvex subtraction problem.With the aid of the slack variable,the proof by contradiction,the auxiliary variable and the mixed integer nonconvex subtraction problem were further transformed into an equivalent convex problem.Finally,an iterative algorithm was proposed to obtain the optimal solutions.Computer simulations validated the quick convergence of the proposed iterative algorithm,and that the developed resource allocation scheme efficiently guarantees the fairness among users in terms of EE.     

用于资源动态预留的航天测控资源配置优化算法

针对航天测控资源配置优化问题这类约束条件繁杂且数量众多的组合优化问题,提出了可用于资源动态预留的航天测控资源配置优化算法。具体来讲,考虑测控设备和航天器执行任务的唯一性约束以及时间窗口冲突约束,建立了基于原子型任务调度的0-1整数规划模型;设计了能将实际需求和求解算法进行解耦的求解框架,并基于最大化利用测控资源的思想获得了可回溯的并行最佳优先搜索算法。仿真结果表明,所提算法达到了能在国内东部、西部、南部和北部四大测控区域中更加均衡地动态预留出更多、更重要测控设备的资源配置优化效果。     

无线网络虚拟化中资源共享的功率分配算法

针对传统无线网络中功率不能动态分配共享的问题,采用无线网络虚拟化,设计了一种基于博弈的两阶段功率分配方法(G2SPA, game theory based two steps power allocation scheme for wireless network virtualization),首先利用买卖博弈模拟了服务提供商(SP, service provide)和移动用户(MUE, mobile user equipment)之间的相互影响,提出了基于斯坦博格均衡(SE, stackelberg equilibrium)的报价策略。然后,利用拍卖理论对空闲下行功率资源进行再分配,采取McAfee机制保证拍卖的诚实性。通过仿真实验证明G2SPA算法的正确性和有效性。