基于云雾混合计算的车联网联合资源分配算法

针对车联网业务的低时延、低功耗需求及海量设备计算卸载引起的网络拥塞问题,该文提出一种在云雾混合网络架构下的联合计算卸载、计算资源和无线资源分配算法(JODRAA)。首先,该算法考虑将云计算与雾计算结合,以最大时延作为约束,建立最小化系统能耗和资源成本的资源优化模型。其次,将原问题转化为标准二次约束二次规划(QCQP)问题,并设计一种低复杂度的联合卸载决策和计算资源分配算法。进一步,针对海量设备计算卸载引起的网络拥塞问题,建立卸载用户接入请求队列的上溢概率估计模型,提出一种基于在线测量的雾节点时频资源配置算法。最后,借助分式规划理论和拉格朗日对偶分解方法得到迭代的带宽和功率分配策略。仿真结果表明,该文算法可以在满足时延需求的前提下,最小化系统能耗和资源成本。     

Ant-Colony-Optimization-Based Scheduling Algorithm for Uplink CDMA Nonreal-Time Data

Scheduling plays an important role in determining the overall performance of code-division multiple-access (CDMA) systems. This paper is focused on the uplink scheduling of CDMA nonreal-time data. In practical CDMA systems, data can only be transmitted with a few fixed transmission rates. Moreover, to guarantee receiving accuracy, the actual received signal-power-to-interference-plus-noise-power ratio (SINR) is expected to be no less than the target SINR value. Using Heaviside unit step functions, the relationship between the actual SINR value and the actual available maximum transmission rate is described in the proposed system model. Based on the proposed system model, an integer optimization problem is formulated to simultaneously maximize the throughput and the scheduling efficiency. Particularly, an ant-colony-optimization (ACO)-based scheduling algorithm is proposed to solve the proposed optimization problem. The computational complexity analysis indicates that the proposed ACO-based scheduling algorithm is computationally efficient in terms of both running time and storage space. In addition, the numerical results show that the proposed optimization problem is more efficient at guiding the development of scheduling algorithms for uplink CDMA nonreal-time data. Moreover, the proposed ACO-based scheduling algorithm performs quite well in terms of quality, running time, and stability.     

Multiple Antenna Enhancements for a High Rate CDMA Packet Data System

A High Data Rate (HDR) system has been proposed for providing downlink wireless packet service by using a channel-aware scheduling algorithm to transmit to users in a time-division multiplexed manner. In this paper, we propose using multiple antennas at the transmitter and/or at the receiver to improve performance of an HDR system. We consider the design tradeoffs between scheduling and multi-antenna transmission/detection strategies and investigate the average Shannon capacity throughput as a function of the number of antennas assuming ideal channel estimates and rate feedback. The highest capacities are achieved using multiple antennas at both the transmitter and receiver. For such systems, the best performance is achieved using a multi-input multi-output capacity-achieving transmission scheme such as BLAST (Bell Labs Layered Space-Time) in which the transmitted signal is coded in space and time, and the receive antennas are used to resolve the spatial interference. In the second part of the paper, we discuss practical transmitter and receiver architectures using BLAST for approaching the theoretical gains promised by the capacity analysis. Because the terminal receivers will be portable devices with limited computational and battery power, we perform a computational complexity analysis of the receiver and make high-level assessments on its feasibility. We conclude that the overall computational requirements are within the reach of current hardware technology.     

基于双连接和回传带宽配置的系统吞吐量效用和最大化算法

针对双连接可行的异构无线网络中关于用户关联和回传带宽配置的联合优化问题,构建了一个新的网络吞吐量效用和最大化框架。将该联合优化问题建模为一个非凸的混合整数分式优化问题。为了便于求解,首先将原建模问题进行去分式化转换,然后针对转换后依旧非凸的混合整数非线性优化问题,将其分解为两个优化子问题分别求解。通过固定用户关联变量,得到了最优的回传带宽配置机制;通过固定回传带宽配置因子变量,提出一个有效的迭代算法求解双连接可行的用户关联子问题。相比固定的回传带宽配置机制,所提算法可以获得最优的回传单位带宽配置因子值,同时拥有最优的系统吞吐量和系统吞吐量效用和性能。     

Link scheduling with power control for throughput enhancement in multihop wireless networks

Joint scheduling and power control schemes have previously been proposed to reduce power dissipation in wireless ad hoc networks. However, instead of power consumption, throughput is a more important performance concern for some emerging multihop wireless networks, such as wireless mesh networks. This paper examines joint link scheduling and power control with the objective of throughput improvement. The MAximum THroughput link Scheduling with Power Control (MATH-SPC) problem is first formulated and then a mixed integer linear programming (MILP) formulation is presented to provide optimal solutions. However, simply maximizing the throughput may lead to a severe bias on bandwidth allocation among links. To achieve a good tradeoff between throughput and fairness, a new parameter called the demand satisfaction factor (DSF) to characterize the fairness of bandwidth allocation and formulate the MAximum Throughput fAir link Scheduling with Power Control (MATA-SPC) problem is defined. An MILP formulation and an effective polynomial-time heuristic algorithm, namely, the serial linear programming rounding (SLPR) heuristic, to solve the MATA-SPC problem are also presented. Numerical results show that bandwidth can be fairly allocated among all links/flows by solving the MILP formulation or by using the heuristic algorithm at the cost of a minor reduction of network throughput. In addition, extensions to end-to-end throughput and fairness and multiradio wireless multihop networks are discussed.     

基于效用最大化的IEEE802.16带宽分配算法

由于IEEE802.16无线城域网协议并未给出网络带宽分配算法或建议,该文提出将802.16服务流带宽分配纳入统一的对数效用函数模型,使问题转化为效用最优化下的非线性规划(NP)求解。同时针对实际应用的实时性要求,提出了适用于对数效用函数的快速解法,使NP问题可以用线性运算解决,大大降低了计算复杂度。仿真结果表明,效用最优化算法比max-min公平算法在吞吐量和效用上均具有明显的优势,还可以灵活地改变效用函数参数,在不同服务质量(QoS)要求下高效地做出分配。     

Predictive channel scheduling algorithm between macro base station and micro base station group

A novel predictive channel scheduling algorithm was proposed for non-real-time traffic transmission between macro-base stations and micro-base stations in 5G ultra-cellular networks.First,based on the stochastic stationary process characteristics of wireless channels between stationary communication agents,a discrete channel state probability space was established for the scheduling process from the perspective of classical probability theory,and the event domain was segmented.Then,the efficient scheduling of multi-user,multi-non-real-time services was realized by probability numerical calculation of each event domain.The theoretical analysis and simulation results show that the algorithm has low computational complexity.Compared with other classical scheduling algorithms,the new algorithm can optimize traffic transmission in a longer time dimension,approximate the maximum signal-to-noise ratio algorithm in throughput performance,and increase system throughput by about 14% under heavy load.At the same time,the new algorithm is accurate.Quantitative computation achieves a self-adaption match between the expected traffic rate and the actual scheduling rate.     

OKRA: optimal task and resource allocation for energy minimization in mobile edge computing systems

To cope with the computational and energy constraints of mobile devices, Mobile Edge Computing (MEC) has recently emerged as a new paradigm that provides IT and cloud-computing services at mobile network edge in close proximity to mobile devices. This paper investigates the energy consumption problem for mobile devices in a multi-user MEC system with different types of computation tasks, random task arrivals, and unpredictable channel conditions. By jointly considering computation task scheduling, CPU frequency scaling, transmit power allocation and subcarrier bandwidth assignment, we formulate it as a stochastic optimization problem aiming at minimizing the power consumption of mobile devices and to maintain the long-term stability of task queues. By leveraging the Lyapunov optimization technique, we propose an online control algorithm (OKRA) to solve the formulation. We prove that this algorithm is able to provide deterministic worst-case latency guarantee for latency-sensitive computation tasks, and balance a desirable tradeoff between power consumption and system stability by appropriately tuning the control parameter. Extensive simulations are carried out to verify the theoretical analysis, and illustrate the impacts of critical parameters to algorithm performance.

     

Energy‐efficient resource allocation in D2D communications for energy harvesting–enabled NOMA‐based cellular networks

In this paper, we propose an energy‐efficient power control and harvesting time scheduling scheme for resource allocation of the subchannels in a nonorthogonal multiple access (NOMA)–based device‐to‐device (D2D) communications in cellular networks. In these networks, D2D users can communicate by sharing the radio resources assigned to cellular users (CUs). Device‐to‐device users harvest energy from the base station (BS) in the downlink and transmit information to their receivers. Using NOMA, more than one user can access the same frequency‐time resource simultaneously, and the signals of the multiusers can be separated successfully using successive interference cancellation (SIC). In fact, NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time by implementing adaptive power allocation. Our aim is to maximize the energy efficiency of the D2D pairs, which is the ratio of the achievable throughput of the D2D pairs to their energy consumption by allocating the proper subchannel of each cell to each device user equipment (DUE), managing their transmission power, and setting the harvesting and transmission time. The constraints of the problem are the quality of service of the CUs, minimum required throughput of the subchannels, and energy harvesting of DUEs. We formulate the problem and propose a low‐complexity iterative algorithm on the basis of the convex optimization method and Karush‐Kuhn‐Tucker conditions to obtain the optimal solution of the problem. Simulation results validate the performance of our proposed algorithm for different values of the system parameters.     

EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.     

Fair and efficient scheduling for UMTS forward link

This paper presents two scheduling algorithms, MWF²Q+ and MDRR, for multiple classes of service over the same spectrum in the forward link of the UMTS network. These scheduling algorithms can allocate bandwidth in proportion to weights of flows sharing the channel, and assign OVSF code to backlogged flows on a frame‐by‐frame basis. The MWF²Q+ algorithm has better fairness properties while the MDRR algorithm requires less computational complexity and space complexity. The fairness properties of these scheduling algorithms are analysed in this paper. Our simulation results show that these two algorithms support multiple traffic sources with heterogeneous rate guarantees while fully utilizing the system bandwidth. The impact of self‐similar traffic is also addressed in our simulations. Copyright © 2005 John Wiley & Sons, Ltd.     

Energy-Optimal Uplink Scheduling in Mobile Cloud Systems

This paper investigates the energy-optimal uplink scheduling in mobile cloud systems. We establish a framework to optimize the energy consumption of the terminal using OFDM technology. We first consider the fixed overhead of RRC state promotion, then, we optimize the energy consumption in slow-start stage and normal transmission stage respectively. In normal data transmission stage, we consider both single-channel transmission scenario and multi-channel transmission scenario. In single-channel transmission scenario, we present an uplink scheduling algorithm which uses dynamic programming method to adjust the transmission rate in accordance with the fluctuating multi-states channel gain. In multi-channel transmission scenario, we propose four different algorithms respectively. Two algorithms of them allocate the transmission rate only among sub-channels and the other two allocate the transmission rate among both time slots and sub-channels. The numerical results show that when the average transmission rate is low, significant amount of energy can be saved by the presented algorithms. The results provide a method for mobile terminals to save energy, i.e., uploading applications to the cloud when data size is small or when the terminal is allocated with wide spectral bandwidth.     

Cuckoo: flexible compute‐intensive task offloading in mobile cloud computing

Mobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.     

中继增强的无线蜂窝多小区系统的联合调度与功率控制算法

针对采用全局频率复用的中继增强的无线蜂窝多小区系统,该文考虑多种通信模式并存的混合场景,提出了一种干扰感知的联合资源分配策略。以最大化系统总吞吐量为目标,同时考虑小区间干扰对中继节点与移动站点的影响,以及基站与中继节点各自的发射功率约束。为了降低计算复杂度,针对用户与中继节点配对问题提出了一种基于小区间干扰的调度算法;针对功率控制问题分别提出了一种基于符号规划的最优功率分配算法和一种次优的最小能耗功率分配算法。仿真结果表明,该文所提算法逼近最优资源分配,在系统吞吐量与能量效率等性能方面具有显著优势。     

GEO卫星LTE跨层资源调度算法

地球同步轨道(GEO)卫星通信系统具有通信时延长的特点,适用于GEO卫星长期演进(LTE)通信的资源调度算法非常重要。为提升星上资源分配的高效性,基于可变最大加权时延优先(M-LWDF)算法,提出了一种综合考虑媒体接入控制(MAC)层参数和应用层参数的跨层调度算法。该算法在MAC层从数学角度推导,提出权重更大的时延判决因子及在应用层根据业务优先级不同引入Q因子。仿真结果表明,与M-LWDF算法相比,不同业务情况下,跨层资源调度算法减小了通信时延,提高了系统吞吐量,但公平性能略有下降并增加了复杂度。     

Performance evaluation of the RQMA-CDMA scheme for multimedia traffic with QoS guarantees

The rapid growth of cellular mobile technology in recent years, coupled with the explosive growth of the Internet, has significantly increased the demand for wireless data services. Traffic on mobile devices is expected to be a mix of real-time traffic such as video, voice, and data, with users requiring diverse quality of service (QoS) guarantees for different types of traffic (video, voice and data). One of the primary challenges of providing QoS is how to prioritize and allocate network resources among contending applications. In order to achieve these goals, a scheduling scheme that can provide equitable and effective packet routing is required. This paper proposes a scheduling scheme called remote queuing multiple access-code division multiple access (RQMA-CDMA), whose purpose is to equitably assign bandwidth resources with QoS guarantees to different mobile devices. RQMA-CDMA is a rate scheduling scheme that can be used to assign bandwidth resources in conjunction with GPS (generalized processor sharing). Additionally, we analyze an admission control that is based on signal to interference plus noise ratio (SINR) for multimedia traffic. Finally, the performance of RQMA-CDMA is evaluated and compared to schemes based on CDMA-GPS according to dropped packets, delay, and throughput.     

一种适用于B3G移动通信系统下行共享信道的调度算法

本文针对后三代(B3G)移动通信系统中的宽带无线信道特性和流媒体业务特征,分析了可用于高速下行共享信道的各种传统分组调度算法,提出面向流媒体业务能够提高系统吞吐量的基于优先级公平调度(Priority-Based Fairness Scheduling,PBFS)算法.该算法根据各移动用户收发信道质量和业务传输的QoS要求动态调整各用户的业务传输优先级,确定下行共享信道的调度方案.并给出该算法的简化形式S-PBFS.仿真结果表明,与传统调度算法相比,S-PBFS算法在数据包传输时延受限的条件下具有无线信道利用率高、实现复杂度低等特点.     

移动网络中基于两级缓存的流媒体调度算法

提出一种适用于移动通信网的两级缓存流媒体系统结构2CMSA（two—level cache mobile streaming architecture），它突破了移动流媒体系统中终端缓存空间小、无线接入网带宽窄的局限；针对2CMSA结构设计了基于两级缓存的移动流媒体调度算法2CMSS（two—level cache based mobile streaming scheduling algorithm），建立数学模型分析了其性能；仿真实验证明，与原有的移动流媒体系统相比，使用2CMSS调度算法能够有效地节省网络传输开销，降低用户启动时延。     

A Real‐Time Algorithm for Timeslot Assignment in ISM System with DVB‐RCS

This paper considers a timeslot assignment problem in an interactive satellite multimedia (ISM) system with digital video broadcast‐return channel via satellite (DVB‐RCS). The timeslot assignment problem is formulated as a binary integer programming to maximize the overall weighted throughput and is shown to be NP‐hard. Thus, three real‐time heuristic algorithms including ratio‐based, packet‐size (PS)‐based, and transmission gain (TG)‐based are derived, and some computational experiments are made. Considering the results, the ratio‐based heuristic algorithm is demonstrated to be the most effective and efficient. We propose adapting the ratio‐based heuristic algorithm to the timeslot assignment problem to greatly improve the ISM system utilization.     

Multi-user Fair Scheduling in the Downlink of CDMA Packet Systems

A fair scheduling algorithm is proposed to improve the system throughput while maintaining the fairness in the downlink of a code-division multiple access (CDMA) system employing AMC and multicodes. R. Kwan and C. Leung suggested an optimal scheduling to maximize the total throughput based on simultaneous transmissions strategy. In this letter, we extend the optimal scheduling so that some degree of fairness can be maintained. We formulate a mixed-integer nonlinear programming problem to assign the radio resources such as the transmit power and codes to several users effectively. The result shows that the proposed algorithm provides a significant throughput gain over PF scheduling with one-by-one transmission at the same level of fairness