Joint resource allocation for lifetime-aware cooperative multi-carrier DF system

In this paper,we study the problem of optimal resource allocation for lifetime maximization in an orthogonal-frequencydivision multiplexing(OFDM)system with decode-and-forward relay.The goal is to minimize total energy cost of the system by jointly optimizing power allocation,subcarrier pairing and relay selection.We present a heuristic solution that is composed of two parts.The first part is an optimal power allocation approach to allocate power to a subcarrier pair of the source and the relay.The second part is a modified Hungarian algorithm to make subcarrier pairing and relay selection.Evaluations show that the presented scheme outperforms other schemes in the total transmitted data and the network lifetime.     

Considering inter-task resource constraints in task allocation

This paper focuses on task allocation with single-task robots, multi-robot tasks and instantaneous assignment, which has been shown to be strongly NP-hard. Although this problem has been studied extensively, few efficient approximation algorithms have been provided due to its inherent complexity. In this paper, we first provide discussions and analyses for two natural greedy heuristics for solving this problem. Then, a new greedy heuristic is introduced, which considers inter-task resource constraints to approximate the influence between different assignments in task allocation. Instead of only looking at the utility of the assignment, our approach computes the expected loss of utility (due to the assigned robots and task) as an offset and uses the offset utility for making the greedy choice. A formal analysis is provided for the new heuristic, which reveals that the solution quality is bounded by two different factors. A new algorithm is then provided to approximate the new heuristic for performance improvement. Finally, for more complicated applications, we extend this problem to include general task dependencies and provide a result on the hardness of approximating this new formulation. Comparison results with the two natural heuristics in simulation are provided for both formulations, which show that the new approach achieves improved performance.     

中继OFDMA系统容量公平资源分配算法研究

针对OFMDA解码-转发中继系统的资源分配问题,提出了一种以系统总功率和用户间的数据速率比例公平为约束条件,以最大化系统总速率为目标的资源分配算法。该资源分配问题为非线性最优化问题,联合求解所有变量复杂度很高,通过次优化的方法降低计算复杂度。算法包括：子载波分配和功率分配。子载波分配是以功率平均分配为前提,对基站-中继站和中继站-用户链路的子载波按照信道条件进行配对,并根据比例公平约束将配对的子载波分配给相应的用户。功率分配是对每个用户利用Lagrange方法调整每个子载波的功率,进一步提高系统的数据速率。算法仿真分析表明,该算法既能同时满足多用户不同数据速率的要求,又能提高系统的数据速率。     

基于能量效率的OFDMA系统资源分配

研究了OFDMA(正交频分多址)系统优化能量效率的资源分配方法,提出了一种基于能量效率的速率比例公平资源分配算法。首先在各子载波等功率的假设条件下为各个用户进行子载波分配,然后求得满足速率比例公平条件下的各用户注水线间的函数关系,再将用户和系统速率要求作为约束条件,采用二分法对函数求解,得到近似最优能量效率下的用户注水线,最后根据用户注水线进行功率分配。仿真结果表明,与其他方案相比,该算法在保持用户速率比例公平性的同时能有效提高系统的能量效率,且复杂度较低。     

Single-machine scheduling to minimize total convex resource consumption with a constraint on total weighted flow time

In this paper, we consider single-machine scheduling problem in which processing time of a job is described by a convex decreasing resource consumption function. The objective is to minimize the total amount of resource consumed subject to a constraint on total weighted flow time. The optimal resource allocation is obtained for any arbitrary job sequence. The computational complexity of the general problem remains an open question, but we present and analyze some special cases that are solvable by using polynomial time algorithms. For the general problem, several dominance properties and some lower bounds are derived, which are used to speed up the elimination process of a branch-and-bound algorithm proposed to solve the problem. A heuristic algorithm is also proposed, which is shown by computational experiments to perform effectively and efficiently in obtaining near-optimal solutions. The results show that the average percentage error of the proposed heuristic algorithm from optimal solutions is less than 3%.     

云中心异构服务器能耗受限下的性能优化策略

为了解决云中心能耗受限下性能优化和异构服务器之间的能耗分配问题,提出了基于M/GI/1-PS排队系统的云中心能耗—性能优化模型,并通过拉格朗日乘数法进行求解,并给出计算云中心最快平均响应时间的OPT算法。同时通过大量数值仿真实验将OPT算法与常用的启发式能耗分配方法进行对比。数值仿真结果表明：本文提出的OPT算法在相同能耗下可获得最优的平均响应时间,同时通过能耗—性能变化曲线,可为云中心优化资源配置提供理论依据和参考数据。     

5G超密集异构网络带内无线回传资源分配方案

为实现5G超密集异构网络中无线回传链路和接入链路之间的最优资源分配,研究多用户场景下双层异构网络的联合用户调度和功率分配问题,在队列稳定和无线回传资源有限的情况下,综合考虑用户调度、功率分配和干扰控制等因素,对带内无线回传的最优资源分配问题进行数学建模并求解,基于李雅普诺夫优化理论提出联合用户调度和功率分配的优化算法。将优化问题解耦为网络内各个用户的调度以及宏基站和小基站的功率分配过程,采用MOSEK求解器和二分类方法获得用户调度向量,利用拉格朗日乘子法求解功率分配问题,并通过队列的时刻更新过程实现最优资源分配。仿真结果表明,在多用户场景下,该方案能够有效提升网络总吞吐量以及网络效用,并且毫米波频段的通信性能优于传统蜂窝网络频段。     

基于合作博弈的认知卫星网络信道分配与上行功率控制算法

随着通信业务需求的不断增长,频谱资源的有限性使得卫星通信网络和地面网络都面临着严重的频谱危机。认知无线电技术的出现,使得卫星网络与地面网络共用频率资源以提升网络效用成为可能。文中对认知接入分配给地面网络作为主用户的同一频谱资源的认知卫星网络的功率控制和信道分配问题进行了研究。根据卫星网络和地面网络的特性构建了合理的系统模型,并利用中断概率门限表征了信道估计误差对系统容量的影响。为了保护主基站的通信性能,在考虑信道估计误差、信道资源约束、认知卫星用户最大发射功率和微波基站干扰约束的条件下,根据议价博弈理论设计了优化函数。其次,根据凸优化理论推导了最优发射功率和信道分配的闭式解,并在此基础上设计了一种对偶迭代算法来求解该优化问题。最后,根据卫星网络的特性设置了合理的网络参数,并根据参数利用Matlab仿真平台对提出的算法进行了仿真实验。仿真结果表明:所提方法在不同到达速率的条件下均具备良好的收敛性;信道估计误差会降低网络的总容量;所提方法在波束数多于15个时,相比比例公平性算法容量提升超过50 bps/Hz,相比最大容量法公平性能提升超过一倍,因此,相较于这两种方法,该方法能在系统容量和用户间公平性之间获得较好的折中。     

能效最优准则下的无人机中继系统的功率分配算法

由于无人机（Unmanned aerial vehicle,UAV）机动性好且部署简单,基于无人机中继的传输技术受到了广泛关注。功率作为通信系统的重要资源,其分配问题直接影响各条链路的性能和整个通信系统的能量效率。本文以莱斯衰落信道为背景,提出了一种在系统能效准则下的无人机中继通信系统的功率分配算法。首先在双跳放大转发（Amplify-and-forward,AF）中继传输模型的基础上建立功率分配的优化模型,将功率分配问题转化为求解最大系统能效的优化问题。在最优功率分配的求解过程中,先固定发射信号功率,获得波束形成优化方案;然后通过大信噪比区间近似,将非凸优化问题转化为凸优化问题;最后利用KKT（Karush-Kuhn-Tucker）条件,计算得出功率分配方案的闭式解。仿真实验表明,本文算法相对于迭代算法降低了算法复杂度。     

Joint cell selection and resource allocation games with backhaul constraints

In this work we study the problem of user association and resource allocation to maximize the proportional fairness of a wireless network with limited backhaul capacity. The optimal solution of this problem requires solving a mixed integer non-linear programming problem which generally cannot be solved in real time. We propose instead to model the problem as a potential game, which decreases dramatically the computational complexity and obtains a user association and resource allocation close to the optimal solution. Additionally, the use of a game-theoretic approach allows an efficient distribution of the computational burden among the computational resources of the network.     

Parametric Transformation of Timed Weighted Marked Graphs: Applications in Optimal Resource Allocation

Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing systems. Due to the existence of multiplicities (i.e., weights) on edges, the performance analysis and resource optimization of such graphs represent a challenging problem. In this paper, we develop an approach to transform a timed weighted marked graph whose initial marking is not given, into an equivalent parametric timed marked graph where the edges have unitary weights. In order to explore an optimal resource allocation policy for a system, an analytical method is developed for the resource optimization of timed weighted marked graphs by studying an equivalent net. Finally, we apply the proposed method to a flexible manufacturing system and compare the results with a previous heuristic approach. Simulation analysis shows that the developed approach is superior to the heuristic approach.      

一种虚拟路由器资源映射算法研究

网络虚拟化技术的提出,为解决互联网"僵化"问题找到了新的思路,受到广泛的关注。在虚拟路由器平台中,若干台互联的网络服务器资源组成了底层物理网络,通过虚拟网络映射技术,将物理网络资源有效地映射到虚拟网络设备上,组成多个虚拟网络,满足用户对网络的多样化需求。虚拟路由器资源映射问题是虚拟网络映射问题的基础,虚拟路由器实例与物理资源的映射方法决定了虚拟网络平台资源的利用率和虚拟网络系统的性能。针对虚拟路由器平台资源分配的问题,提出了物理网络资源模型和虚拟路由器资源请求模型,设计了一种启发式虚拟路由资源分配算法,并对算法的复杂性和优化目标进行了分析。     

Resource allocation algorithm with limited feedback for multicast single frequency networks

The single frequency network (SFN) can provide a multimedia broadcast multicast service over a large coverage area. However, the application of SFN is still restricted by a large amount of feedback. Therefore, we propose a multicast resource allocation scheme based on limited feedback to maximize the total rate while guaranteeing the quality of service (QoS) requirement of real-time services. In this scheme, we design a user feedback control algorithm to effectively reduce feedback load. The algorithm determines to which base stations the users should report channel state information. We then formulate a joint subcarrier and power allocation issue and find that it has high complexity. Hence, we first distribute subcarriers under the assumption of equal power and develop a proportional allocation strategy to achieve a tradeoff between fairness and QoS. Next, an iterative water-filling power allocation is proposed to fully utilize the limited power. To further decrease complexity, a power iterative scheme is introduced. Simulation results show that the proposed scheme significantly improves system performance while reducing 68% of the feedback overhead. In addition, the power iterative strategy is suitable in practice due to low complexity.     

A Compiler-Directed Approach to Network Latency Reduction for Distributed Shared Memory Multiprocessors

In distributed shared memory multiprocessor systems, parallel tasks communicate through sharing memory data. As the system size increases, such communication cost becomes the main factor that limits the overall parallelism and performance. In this paper, we propose a new solution to the problem through judiciously managing the relevant resource, namely, the shared data and the interconnection network (IN) through which the sharing is carried out. In this approach, communication cost is minimized by means of data migration/allocation which is based on analyzing general layered task graphs, sharing behavior of parallel tasks, and network topology. Our method is not applicable for read only variables. Further, for the time being, the usefulness of the method is limited to multiprocessors where no cache coherence mechanism is implemented. Four typical interconnection topologies for multiprocessors are considered, namely, shared-bus, hierarchical-bus, 2-D mesh, and fat-tree structures. Efficient data allocation algorithms for each of the four network topologies are developed that make decision on data allocation/migration at the compile time. The complexity of one algorithm isO(np) for shared-bus andO(n²p) for the remaining three in a system withnprocessors executing ap-layer task graph for one shared variable. We have also given an algorithm to determine optimal allocation/migration scheme for multiple shared variables. However, the cost of the algorithm become prohibitive when the number of shared variables is high. Therefore, a heuristic of low complexity is suggested. The heuristic is optimal for some topologies.     

A bicriterion approach to time/cost trade-offs in scheduling with convex resource-dependent job processing times and release dates

This paper presents a bicriterion analysis of time/cost trade-offs for the single-machine scheduling problem where both job processing times and release dates are controllable by the allocation of a continuously nonrenewable resource. Using the bicriterion approach, we distinguish between our sequencing criterion, namely the makespan, and the cost criterion, the total resource consumed, in order to construct an efficient time/cost frontier. Although the computational complexity of the problem of constructing this frontier remains an open question, we show that the optimal job sequence is independent of the total resource being used; thereby we were able to reduce the problem to a sequencing one. We suggest an exact dynamic programming algorithm for solving small to medium sizes of the problem, while for large-scale problems we present some heuristic algorithms that turned out to be very efficient. Five different special cases that are solvable by using polynomial time algorithms are also presented.     

Sequence of linear programming for transmission of fine-scalable coded content in bandwidth-limited environments

In this paper, we propose an optimal peer assignment algorithm on peer-to-peer networks. This algorithm is designed to maximize the quality of transmitting fine-scalable coded content by exploiting the embedding property of scalable coding. To be more realistic, we assume that the requesting peer has a delay constraint to display the content within a certain delay bound, and it also has limited incoming bandwidth. We first use a simple example to illustrate the peer assignment problem, and then formulate this problem as a linear programming problem, followed by a nonlinear programming problem. To efficiently solve the second nonlinear problem, we transform it into a sequence of linear programming problems. Finally, we apply our proposed algorithm to both image and video transmissions in bandwidth-limited environments. Extensive experiments have been carried out to evaluate the complexity and performance of our approach by comparing it with both nonlinear formulation and two heuristic schemes. The results have verified the superior performance of our proposed algorithm.     

A Heuristic Genetic Algorithm for Solving Resource Allocation Problems

In the paper, a heuristic genetic algorithm for solving resource allocation problems is proposed. The resource allocation problems are to allocate resources to activities so that the fitness becomes as optimal as possible. The objective of this paper is to develop an efficient algorithm to solve resource allocation problems encountered in practice. Various genetic algorithms are studied and a heuristic genetic algorithm is proposed to ameliorate the rate of convergence for resource allocation problems. Simulation results show that the proposed algorithm gives the best performance.     

Resource allocation for physical-layer security in OFDMAdownlinkwith imperfect CSI

We investigate the problem of resource allocation in a downlink orthogonal frequency-division multiple access (OFDMA) broadband network with an eavesdropper under the condition that both legitimate users and the eavesdropper are with imperfect channel state information (CSI). We consider three kinds of imperfect CSI: (1) noise and channel estimation errors, (2) feedback delay and channel prediction, and (3) limited feedback channel capacity, where quantized CSI is studied using rate-distortion theory because it can be used to establish an informationtheoretic lower bound on the capacity of the feedback channel. The problem is formulated as joint power and subcarrier allocation to optimize the maximum-minimum (max-min) fairness criterion over the users’ secrecy rate. The problem considered is a mixed integer nonlinear programming problem. To reduce the complexity, we propose a two-step suboptimal algorithm that separately performs power and subcarrier allocation. For a given subcarrier assignment, optimal power allocation is achieved by developing an algorithm of polynomial computational complexity. Numerical results show that our proposed algorithm can approximate the optimal solution.     

Resource Allocation Based on User Pairing and Subcarrier Matching for Downlink Non-Orthogonal Multiple Access Networks

The traditional orthogonal multiple access (OMA) is unable to satisfy the needs of large number of smart devices. To increase the transmission rate in the limited spectrum resource, implementation of both non-orthogonal multiple access (NOMA) and successive interference cancelation (SIC) is essential. In this paper, an optimal resource allocation algorithm in NOMA is proposed to maximize the total system rate in a multi-sector multi-subcarrier relay-assisted communication network. Since the original problem is a non-convex problem with mixed integer programming which is non-deterministic polynomial-time (NP)-hard, a three-step solution is proposed to solve the primal problem. Firstly, we determine the optimal power allocation of the outer users by using the approach of monotonic discrimination, and then the optimal user pairing is determined. Secondly, the successive convex approximation (SCA) method is introduced to transform the non-convex problem involving central users into convex one, and the Lagrangian dual method is used to determine the optimal solution. Finally, the standard Hungarian algorithm is utilized to determine the optimal subcarrier matching. The simulation results show that resource allocation algorithm is able to meet the user performance requirements with NOMA, and the total system rate is improved compared to the existing algorithms.      

C-V2X无线资源管理算法研究

在蜂窝车联网（C-V2X）的underlay模式下,蜂窝用户（C-UEs）与车载用户（V-UEs）共享上行频谱。为解决C-V2X中的同频干扰问题,并确定不同数据包优先级（PPPP）用户的传输优先度,在保障V-UEs与C-UEs通信可靠性的同时,以最大化所有用户的信息值为目标,提出一种新的无线资源管理（RRM）算法。通过引入子用户的概念将RRM问题转换为功率分配问题与频率资源选择问题,在功率分配结果的基础上采用启发式算法实现低复杂度的频率资源选择,并对频率选择结果进行再分配得到最终发射功率。仿真结果表明,该算法相比正交分配算法能够有效提升所有用户的信息值,并保障系统可靠性。