基于MABC的中继系统资源分配策略

针对MABC的中继系统,对资源配置策略的优化问题进行研究,并将功率分配问题和中继选择问题相结合。基于此,提出了一种存在干扰情况MABC模型的多中继系统资源分配策略（IS-MABC-OPOR）。考虑双向中继系统干扰信号对其传输性能的影响,以通信链路信道容量最大化为优化目标,利用拉格朗日方法,对通信链路上的各节点进行了最优功率分配和最佳中继选择,给出了通信链路信道容量最大意义下的资源分配数学表达式。仿真结果表明,该链路的信道容量受中继节点位置以及链路总功率的制约,相比于等功率分配方案（EPA）、随机功率分配方案（RPA）和在所提方案的基础上加大干扰信号的信道增益,IS-MABC-OPOR方案有效提高了系统的信道容量,从而实现资源能够满足更高速率的业务传输需求。     

Device-to-device resource allocation in LTE-advanced networks by hybrid particle swarm optimization and genetic algorithm

D2D (Device-to-Device) communication is one of the promising technologies in the LTE (Long Term Evolution)-Advanced communication network. From system perspective, this technology can increase frequency efficiency of the whole macro cell. From user perspective, however, a D2D pair which shares the same frequency resource of CUE (Cellular User Equipment) can cause interference with the CUE in the downlink. Thus, reducing interference for the cellular users and allocating frequency resource for the D2D pairs are important in D2D communication. In this paper, two resource allocation schemes which are based on PSO (Particle Swarm Optimization) and hybrid PSO-GA (Genetic Algorithm) are proposed to maximize the system throughput by allowing up to two D2D pairs to share the same frequency resource with one CUE. Moreover, a scheme has been considered to mitigate the interference caused to CUEs. The simulation results show that service of CUEs and system performance can be effectively guaranteed by using the proposed schemes.     

增强现实场景下移动边缘计算资源分配优化方法

针对高速数据传输及计算所带来时延和终端设备能耗问题，提出了一种在上行链路采用等功率分配的传输方案。首先，依据增强现实（AR）业务的协作属性建立了针对AR特性的系统模型；其次，详细分析了系统帧结构，建立以最小化系统消耗总能量为优化目标的约束条件；最后，在保障延迟和功耗满足约束的条件下，建立了基于凸优化的移动边缘计算（MEC）资源优化求解数学模型，从而获得最优的通信和计算资源分配方案。与独立传输相比，该方案在最大延迟时间分别为0.1 s和0.15 s时的总能耗降幅均为14.6%。仿真结果表明，在相同条件下，与基于用户独立传输的优化方案相比，考虑用户间协作传输的等功率MEC优化方案能显著减少系统消耗的总能量。     

基于多对一Gale-Shapley算法的D2D通信资源分配

针对D2D通信复用异构蜂窝网络上行信道产生的干扰问题和频谱资源优化问题进行研究,提出一种基于多对一Gale-Shapley算法的D2D通信资源分配方案。本方案允许多个D2D用户共享一个蜂窝用户信道资源,通过设置信干噪比（SINR）门限保证用户的通信服务质量（QOS）。根据信道分配情况,构建D2D用户和信道的偏好列表,最大化系统总容量。仿真结果表明,该方案收敛较快,复杂度较低,能够有效保证用户的通信服务质量,系统总容量接近最优解。本研究为实现D2D用户和蜂窝用户的频谱资源共享,提高频谱利用率提供了一种有效方案。     

A uplink radio resource allocation scheme for localized SC-FDMA transmission in LTE network

The radio resource allocation is one of the most important issues to achieve effective wireless communication. In Long Term Evolution (LTE) network, single carrier frequency division multiple access (SC-FDMA) is applied as the transmission technology for uplink traffic. Most researches focus on maximizing the system throughput of SC-FDMA under changeable channel condition. However, users may require different quality of services (QoS) for different applications. This paper studies radio resource allocation for QoS users in localized SC-FDMA system. The proposed scheme divides allocation process into matching algorithm and radio resource assignment algorithm. The Gale–Shapley algorithm is applied to find the optimal matching between resource blocks (RB) and user equipment (UE) by considering channel conditions and the desired QoS. Then the resource assignment algorithm heuristically allocates bandwidth to UE by referring the matched RB under the constraint of carrier continuity. This paper modified the Recursive Maximum Expansion (RME) algorithm to effectively assign radio resource for UEs with different bandwidth demands. The performance of our proposed scheme is compared with the modified RME scheme through exhaustive simulations. The video streaming, VoIP, and FTP traffic types were adopted for simulations. Our simulation results show that the proposed scheme achieves better QoS satisfaction and system throughput than the RME-modified scheme.     

OFDMA 协同网络中基于QoS 保证的资源分配算法

针对正交频分多址(OFDMA)协作通信系统,为了解决数据传输速率最大化并提供尽力而为业务与实时业务的服务质量(QoS)保证问题,提出新型资源分配算法.通过以总功率受限为约束条件,定义基于数据速率、时延和丢包率的效用函数,并以在协作传输中以最大化效用函数为目标进行中继选择和子载波分配.通过中继和用户上的子载波和功率分配方案的设计,从而最大化网络数据传输速率并最小化时延与丢包率,为多种业务提供服务质量保证.采用最优化理论与方法求解效用函数,得到了资源分配结果.仿真结果验证了算法收敛,并给出功率分配结果,以及网络吞吐量和时延性能指标情况,验证了算法的有效性.     

An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

Cloud computing distributes task-parallel among the various resources. Applications with self-service supported and on-demand service have rapid growth. For these applications, cloud computing allocates the resources dynamically via the internet according to user requirements. Proper resource allocation is vital for fulfilling user requirements. In contrast, improper resource allocations result to load imbalance, which leads to severe service issues. The cloud resources implement internet-connected devices using the protocols for storing, communicating, and computations. The extensive needs and lack of optimal resource allocating scheme make cloud computing more complex. This paper proposes an NMDS (Network Manager based Dynamic Scheduling) for achieving a prominent resource allocation scheme for the users. The proposed system mainly focuses on dimensionality problems, where the conventional methods fail to address them. The proposed system introduced three –threshold mode of task based on its size STT, MTT, LTT (small, medium, large task thresholding). Along with it, task merging enables minimum energy consumption and response time. The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme (EDS) and Decentralized Virtual Machine Migration (DVM). With a Network Manager-based Dynamic Scheduling, the proposed model achieves excellence in resource allocation compared to the other existing models. The obtained results shows the proposed system effectively allocate the resources and achieves about 94% of energy efficient than the other models. The evaluation metrics taken for comparison are energy consumption, mean response time, percentage of resource utilization, and migration.     

An interference avoidance MAC protocol design in mobile ad hoc networks

The multi-rate IEEE 802.11 DCF MAC protocol can transmit control signals at a basic transmission rate and data signals at various transmission rates. When the transmission rates of the control signals and the data signals differ, the transmission range of the lower transmission rate is larger than the transmission range of the higher transmission rate. Since a lower transmission rate increases the transmission range, it also increases the nodes in the Network Allocation Vector (NAV) status and decreases the network throughput. However, if a neighbor receiving the control signal of the ongoing communication pair communicates with another node, it may occur signal interference. This study proposes a Space Overlapping MAC (SO-MAC) protocol to increase the communication pairs and avoid interferences in single radio for multi-rate wireless network. The SO-MAC protocol uses a channel division mechanism to avoid interference between the data and control signals. This study also proposes a bandwidth allocation strategy for the sub-channels to maximize the utilization of the bandwidth of the divided sub-channels. To solve the interference between the data signals, SO-MAC allows a neighbor of the sender and the receiver to use the received signal strength to determine whether it can send or receive the data signal to increase the communication pairs. Simulation results show that, compared to the IEEE 802.11 DCF protocol and DCA protocol, the proposed SO-MAC protocol with the bandwidth allocation strategy can increase the communication pairs, achieve better throughput, reduce the number of handshake failures, and decrease the delay of transmitting a packet.     

电力通信系统的宽带调节技术

当前利用分层网络结构对电力通信系统数字信道进行调节的方式,存在吞吐量性能差、宽带分配不均衡以及电力信息传输丢失严重等问题。提出利用遗传算法对电力通信系统宽带进行调节。对电力通信系统宽带中,每个OFDM符号内多个用户同时进行电力数据传输中的多种参数进行定义并计算,对最大的传输比特数以及最大功率进行计算,在电力通信资源分配速率的自适应和功率自适应的目标几乎一致理论基础上,建立电力通信多用户速率自适应宽带资源分配的数学模型,针对多用户的多目标特点,利用遗传算法中优良基因的注入,跨世代精英的保留以及迭代次数等,满足电力通信系统的多用户宽带资源分配需求。并通过实验证明,所提方法可实现电力通信系统宽带的有效调节。     

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QMix的车辆云计算资源动态分配方法

城市交通智能化和通信技术的进步会产生大量基于车辆的应用,但目前车辆有限的计算资源无法满足车辆应用的计算需求与延迟性约束。车辆云（VC）可以高效地调度资源,从而显著降低任务请求的延迟与传输成本。针对VC环境下任务卸载与计算资源分配问题,提出一个考虑异质车辆和异质任务的计计资源分配算法。对到达的任务构建M/M/1队列模型与计算模型,并定义一个效用函数以最大化系统整体效用。针对环境中车辆地理分布的高度动态系统变化,提出基于双时间尺度的二次资源分配机制（SRA）,使用两个不同时间尺度的资源分配决策动作,对其分别构建部分可观测马尔可夫决策过程。两个决策动作通过执行各自的策略获得的奖励进行连接,将问题建模为两层计算资源分配问题。在此基础上提出基于二次资源分配机制的多智能体算法SRA-QMix求解最优策略。仿真结果表明,与深度确定性策略梯度算法对比,该算法的整体效用值和任务完成率分别提高了70%、6%,对于QMix和MADDPG算法分别应用SRA后的任务完成率分别提高了13%与15%,可适用于动态的计算资源分配环境。     

一种联合D2D资源分配的中继选择算法的研究

为提高蜂窝网络中边缘用户的QoS,可在蜂窝网络中引入了D2D辅助中继技术,然而由于D2D链路复用蜂窝资源会与蜂窝链路之间产生同频干扰,为了降低这种干扰,提出了一种联合资源分配的中继选择算法,该算法首先使用一种资源分配策略,使得D2D链路对蜂窝链路产生的干扰最小,然后结合资源分配结果提出了一个中继选择方案,该方案不仅考虑了D2D链路和回程链路的信道质量,而且还同时考虑到了对蜂窝链路的干扰问题。通过仿真验证,本文所提出的算法不仅能有效提升边缘用户吞吐量,同时保证了中心用户的QoS。     

异构网络中基于用户划分的资源分配与功率控制方案

部署异构网络是提高移动通信系统容量至关重要的举措。然而宏基站的高发射功率会使得微蜂窝边缘用户受到严重的干扰;同时,微蜂窝周围的宏蜂窝用户由于靠近微基站将同样受到其所造成的干扰,因此这些用户应该受到保护。通过研究异构网络下行传输中的资源分配与功率控制策略,提出了一种基于用户划分的资源分配与功率控制方案。首先根据用户的信干噪比对用户进行分类,同时为受干扰严重的边缘用户划分保护频带。此外,通过动态地调整每个基站在保护频带上的传输功率,在不过度牺牲系统性能情况下减轻边缘用户的干扰。仿真结果表明,与传统方案相比,该方案能够有效抑制宏蜂窝和微蜂窝边缘用户的干扰并提高整个系统的吞吐量。     

Underlaid-D2D-assisted cooperative multicast based on social networks

In this paper, device-to-device (D2D) communication is integrated into cooperative multicast to elevate the multicast transmission rate from base station to users. To this end, a new optimization problem, aiming to maximize the overall cooperative multicast rate, is formulated with two essential features 1) D2D communication is exploited to accomplish the data forwarding in the second phase of cooperative multicast, which overcomes the performance bottleneck that time resource has to be bisected for original relay forwarding in conventional cooperative multicast. 2) Social tiers are also taken into account for relay user selection, which can be viewed as a kind of incentive mechanism to enable D2D communication. To solve the problem efficiently, a two-step scheme is presented, in which relay user selection by joint leveraging channel gains and social connections, and power allocation by quantum-behaved particle swarm optimization (QPSO), are carried out sequentially. Numerical simulations show that the proposed social network-based D2D-assisted cooperative multicast scheme performs well, and the performance gap between the proposed power allocation algorithm and upper bound could be limited within 5 % when the system is not heavily loaded. The performance gain over traditional cooperative multicast can reach up to 85 %.     

An Energy-Efficient Resource Allocation Scheme for Mobile Ad Hoc Computational Grids

Due to recent advancements in mobile computing and communication technologies, mobile ad hoc computational Grids are emerging as a new computing paradigm, enabling innovative applications through sharing of computing resources among mobile devices without any pre-existing network infrastructure. Energy-efficient resource allocation is one of the key issues in mobile ad hoc computational Grids due to limited battery life of mobile nodes. To reduce energy consumption, we propose a hybrid power-based resource allocation scheme for allocation of interdependent tasks to nodes within mobile ad hoc computational Grid. The basic idea is to exploit dependencies and task type, and allocate interdependent tasks to nodes accessible at minimum transmission power. We also propose a power-based algorithm to search a group of closest nodes to allocate a set of interdependent tasks. Compared to traditional algorithms, complexity of proposed algorithm depends on number of transmission power levels rather than number of nodes within a Grid. The scheme is validated in a simulation environment using various workloads and parameters.     

User satisfaction aware routing and energy modeling of polymorphic network on chip architecture

In mobile devices, multiple applications contend for limited resources in the underlying embedded system framework. Application resource requirements in mobile systems vary by computation needs, energy consumption and user interaction frequency. Quality of service (QoS) is the predominant metric of choice to manage resources among contending applications. Resource allocation policies to support static QoS for applications do not reflect the changing demands of the user in contemporary network on chip (NoC) based embedded architectures. User satisfaction with the user interactions and user interface design ought to be the primary design driver. Some recent research has integrated a saturating, non-linear user satisfaction function in the application thread scheduler. The application and operating system level user satisfaction research assumes that the throughput of inter-thread edges is limited only by the computational constraints of the nodes. With NoC, however, NoC resource allocation policies play an important role in determining the inter-thread communication flow’s throughput and the resulting application level user satisfaction. In this paper, we filter down the user satisfaction from an application layer attribute to a router level attribute to improve the resource and energy utilization for routing in order to leverage the user satisfaction at the application and system level. We demonstrate that this technique improves the user satisfaction of audio (MP3) application by 10% while maintaining the user satisfaction of video (MPEG-2) application. Experiments also show that a fixed energy source can be extended for an average of 18% of the time using the NoC user satisfaction based energy optimization proposed in this research.     

任务分解控制及人员柔性的车间集成调度

针对复杂产品制造环境下制造任务分解与资源配置脱节的问题,提出了制造任务分解与多目标人员柔性车间资源配置优化方法。在对复杂制造任务特点进行分析的基础上,建立了任务分解粒度控制模型和考虑人员柔性的制造单元资源模型,利用自适应非支配排序遗传算法进行求解,得到了较为满意的任务分解和车间资源调度方案。     

认知网络中基于博弈论的联合功率控制与速率分配算法

针对认知无线网络上行链路中的资源分配问题,提出了一种适应于多小区认知无线网络的基于功率控制与速率分配的博弈算法。为了更加合理地控制用户的功率和速率,减小各次用户间的干扰,首先,在效用函数中分别给功率和速率设置了不同的代价因子,使其能够更加合理地控制用户,避免用户过度增加发射功率。其次,从理论上证明了该算法纳什均衡的存在性、唯一性以及算法的收敛性。最后,为了解决发射功率和传输速率的最优化问题,给出了联合功率控制和速率分配的迭代更新算法流程图。理论分析及仿真结果表明,与同类博弈算法相比,在保证通信质量的前提下,所提算法可以使得用户以较小的发射功率获得较大的传输速率和较高的信干噪比（SINR）,并且减小了用户间的干扰,提高了次用户系统容量。     

基于能效的OFDMA系统资源分配研究*

在正交频分多址（OFDMA）系统中,合理的资源分配对于提升系统的能效具有重要的意义。针对多用户OFDMA系统,对最大化系统能效为目标的资源分配算法进行研究,提出了基于智能水滴算法的全连通图资源分配模型,该模型以全连通图的顶点和边来表示用户与子载波之间的对应关系。在此基础上,进一步对智能水滴算法进行改进。仿真结果表明,在满足用户QoS的条件下,所提出的改进的智能水滴算法的全连通图资源分配方案能够有效提高系统能效,提升系统性能。     

JCCA resource allocation for video transmission in relay-enhanced OFDMA system

Resulting from the rapid growth of wireless video services in the recent years, radio resource is often inadequate in the high data rate communication environment. Under the circumstance, it is important to make full use of limited resource for efficient video transmission. Hence, we propose a joint content and channel aware (JCCA) resource allocation algorithm in relay-enhanced OFDMA system. It can maximize accumulation of allocated subcarriers’ contribution for transmitting video data so as to improve overall quality of users’ received video under resource constrained conditions. As it is described in this work, the algorithm is implemented by packet scheduling, path selection and subcarrier allocation, in which time-varying data content and channel state are jointly considered at the same time. And the two implementation schemes, JCCAG and JCCAL, are designed for JCCA resource allocation algorithm. JCCAG is executed for allocating subcarriers through global search among all subcarriers’ contribution. For reducing complexity of JCCAG, subcarriers are allocated according to the corresponding contribution towards each user on the basis of local search in JCCAL. In the simulation, we make comparison between the JCCA resource allocation algorithm and others for video transmission in relay-enhanced OFDMA system. And the comparison results are evaluated through objective and subjective criterion, respectively. It is demonstrated that the proposed algorithm can perform better in improving overall quality of users’ received video.