基于NOMA技术的PLC-VLC用户配对和子载波分配方法

为了提高室内电力线通信和可见光通信(PLC-VLC)系统的吞吐量和用户体验的公平性,该文提出一种改进遗传算法优化用户配对联合子载波分配(IGA-JUPSA)方法.在IGA-JUPSA的用户配对阶段,设计了最优的非正交多址技术(NOMA)用户配对方法,提高PLC-VLC系统的吞吐量;在IGA-JUPSA的子载波分配阶段,设计NOMA与正交多址技术结合的子载波分配策略,设计改进的遗传算法优化不同NOMA组的子载波分配,提高系统的吞吐量和用户体验的公平性.仿真结果表明,所提的用户配对和子载波联合方法可以提高PLC-VLC系统的吞吐量和用户体验的公平性.     

基于MADDPG的无人机辅助通信功率分配算法

针对多无人机(unmanned aerial vehicle, UAV)作为空中基站辅助通信的吞吐量和公平性问题,提出了一种基于多智能体深度确定性策略梯度算法(multi-agent deep deterministic policy gradient algorithms, MADDPG)的功率分配算法,该算法通过联合优化UAV基站的功率分配和用户接入以提高系统吞吐量和公平性。本文首先构建了UAV基站为地面建立通信服务的三维场景,然后通过联合功率、用户关联和UAV位置约束,构建了吞吐量和公平性最大化的问题模型。考虑到该问题的复杂性,本文将所构建的优化问题建模为马尔科夫决策过程(Markov decision process, MDP),通过引入深度确定性策略梯度算法(deep deterministic policy gradient algorithm, DDPG)解决该问题。仿真结果表明,本文提出的基于MADDPG的UAV基站功率分配算法与其他算法相比,可以有效地提升系统的吞吐量和用户的公平性,提高通信的服务质量。     

移动WiMAX网络下行链路TCP吞吐量公平性的跨层优化

该文提出了一种移动WiMAX网络中的TCP公平性跨层优化模型,设计了基于此模型的cross-layer TCP改进协议。利用端节点链路层的速率信息和在基站BS(Base Station)处预分配ACK分组所需带宽的策略改善了下行终端的QoS (Quality of Service)指标,保证了下行终端和上行终端的吞吐量公平性。仿真结果表明cross- layer-TCP改进协议能够在保持原有系统吞吐量的前提下改善下行终端的公平性。     

基于中继辅助的不等发射功率调度算法的公平性研究

在多用户分集系统中，基于最大化吞吐量的调度算法可使系统获得较高的吞吐量性能，有效的调度可提高系统的用户服务率，提高特定用户的数据传输率，降低单位数据的通信开销。但是，采用最大化系统吞吐量的调度方式没有直接考虑每个用户的需求，故调度算法的公平性问题值得探讨。研究了基于中继辅助调度的中断调度策略和随机选择调度策略的公平性问题，并对结果进行了详细的比较。     

一种提高IEEE 802.11吞吐量和公平性的自适应优化算法

该文提出了一种针对IEEE 802.11 DCF网络增强其吞吐量和公平性性能的自适应优化算法,算法基于网络节点侦听信道得到的网络状态信息进行竞争发送的自适应调整以获得最优的网络性能,称之为CSCC(Channel Sensing Contention Control)算法。算法采用了对节点的信道接入请求以概率参数P_T进行过滤的方法控制节点竞争接入信道的激烈程度,其主要特点在于在优化调整过程中不需要进行计算复杂的网络节点数量估计,并且可以在不同网络状态下围绕始终确定的优化目标进行参数优化调整。仿真实验结果表明,算法能够适应不同节点数量和不同数据大小的网络进行自适应的网络优化调整,并获得了系统吞吐量、碰撞概率、延迟、延迟抖动、公平性等多方面的性能改善。     

基于分类的预取感知缓存分区机制

在多核处理器中,硬件预取技术是解决存储墙问题的主要技术之一,是对高速缓冲寄存器的优化.但是现有的预取技术大多只考虑内存密集型程序的性能优化,而忽视了非内存密集型程序因预取而受到的干扰.针对这个问题,本文提出基于分类的预取感知缓存分区机制,利用自适应预取控制和缓存分区技术,可以动态调整预取的激进程度和合理分配共享缓存,该机制使用Champsim进行仿真实验.实验结果表明该机制可以有效提高非内存密集型程序的吞吐量,减少核间干扰,提高系统的性能和公平性.     

太赫兹无线个域网公平高效的MAC层优化机制

针对太赫兹无线个域网双信道媒体访问控制(Medium Access Control, MAC)协议中存在的信道资源分配公平性差、数据帧重传效率低和控制开销较高的问题,提出了一种公平高效的MAC层优化机制。新机制采用基于历史时隙申请信息的信道资源分配策略优化时隙分配公平性,根据信道质量自适应选择数据帧重传机制,控制节点从申请时隙的节点发送的控制帧中的“duration”字段提取时隙申请信息,省略时隙申请帧的发送,从而降低数据帧排队时延和控制开销,提高吞吐量。仿真结果表明,与TAB-MAC和MDP-MAC协议的机制相比,所提机制的数据帧平均排队时延降低了15%,吞吐量提高了5%。     

基于比例公平原则的多用户MIMO-OFDM系统资源分配

提出了一种基于比例公平原则提高MIMO-OFDM系统吞吐量的子载波和功率分配算法.它在总功率和误比特率的约束下,以获取最大系统吞吐量为目标,同时为兼顾用户间资源享用的公平性,根据用户速率成比例推导出了子载波分配限制准则.仿真结果表明,本算法不仅可满足不同用户的速率要求,而且平衡了容量最大化和用户间公平性的矛盾,同时计算...     

面向船联网的NOMA系统用户公平性研究

非正交多址技术(Non-orthogonal Multiple Access, NOMA)有助于提升船联网的连接数量和频谱效率。通过研究公平性约束下船联网NOMA系统的能量效率优化问题,设计基于速率方差的用户公平性因子,并提出了一种公平性约束下NOMA系统的能量效率优化策略。首先,假设基站到各个簇的发射功率相同且簇内用户数固定,以最大化能量效率作为用户分簇的目标,利用遗传算法对用户分簇方案进行优化;其次,在满足最小公平因子的条件下对簇内用户间的功率分配进行优化。仿真结果表明该优化策略能提高能量效率且用户公平性得到了保证。     

基于射频能量收集的无线中继网络资源分配

针对现有射频能量收集网络资源分配研究局限于单个数据源场景,无法适配于多数据源网络的问题,提出了一种适用于多数据源场景的射频能量收集中继网络传输协议框架,在该框架内节点可作为源节点或中继节点传输自身数据或转发数据,并在其他节点的数据传输过程中完成射频能量收集。以协议框架为基础,分别以系统吞吐量及用户公平性为优化目标设计两种资源分配方案。仿真表明,两种方案可有效改善网络吞吐量及资源分配公平性。     

基于多个取指优先级的同时多线程处理器取指策略

同时多线程(SMT,Simultaneous Multithreading)处理器中,同时运行的线程在共享资源的同时也在竞争资源.如果一个发生L2 cache失效的线程长时间占用共享资源,那么会导致其他线程运行速度减慢,甚至会因为缺少资源而停顿下来,从而降低了SMT处理器的总体性能.本文提出了一种基于多个取指优先级的同时多线程取指策略MFP(Multiple Fetch Priorities),用于减少L2 cache失效给处理器性能带来的负面影响.模拟结果表明,无论使用IPC作为度量标准还是使用Hmean作为度量标准,对于所有类型的工作负载,尤其是存储器访问密集的工作负载,MFP都要优于现有的其他取指策略.此外,对于不同的取指策略,MFP表现出不同程度的提升.相对于PDG的提升最明显,平均IPC以及平均Hmean分别提高了19.2%和27.7%.     

An optimization framework for balancing throughput and fairness in wireless networks with QoS support

Quality-of-service (QoS) provisioning, high system throughput, and fairness assurance are indispensable for heterogeneous traffic in future wireless broadband networks. With limited radio resources, increasing system throughput and maintaining fairness are conflicting performance metrics, leading to a natural tradeoff between these two measures. Balancing system throughput and fairness is desired. In this paper, we consider an interference-limited wireless network, and derive a generic optimization framework to obtain an optimal relationship of system throughput and fairness with QoS support and efficient resource utilization, by introducing the bargaining floor. From the relationship curve, different degrees of performance tradeoff between throughput and fairness can be obtained by choosing different bargaining floors. In addition, our framework facilitates call admission control to effectively guarantee QoS of. multimedia traffic. The solutions of resource allocation obtained from the optimization framework achieve the pareto optimality, demonstrating efficient use of network resources.     

A probabilistic approach for fair-efficient call admission control in wireless multiservice networks

The efficiency of call admission control (CAC) schemes in multiclass wireless networks should be evaluated not only with regard to the call blocking probability (CBP) achieved for every service class (SC) supported but also with regard to quality of service (QoS) and network efficiency criteria. In this article, four CAC schemes offering priority to SCs of advanced QoS requirements, based on guard channel policy, are studied and evaluated taking into account fairness and throughput criteria in addition to CBP. For the performance evaluation of the proposed CAC schemes and to examine fairness issues, two fairness indices are introduced along with a throughput metric. The analytical results, validated through extensive simulations, indicate that by appropriate selection of the CAC parameters satisfactory fairness and throughput are achieved while achieving low CBP.     

Counter-intuitive throughput behaviors in networks under end-to-end control

It has been shown that as long as traffic sources adapt their rates to aggregate congestion measure in their paths, they implicitly maximize certain utility. In this paper we study some counter-intuitive throughput behaviors in such networks, pertaining to whether a fair allocation is always inefficient and whether increasing capacity always raises aggregate throughput. A bandwidth allocation policy can be defined in terms of a class of utility functions parameterized by a scalar a that can be interpreted as a quantitative measure of fairness. An allocation is fair if /spl alpha/ is large and efficient if aggregate throughput is large. All examples in the literature suggest that a fair allocation is necessarily inefficient. We characterize exactly the tradeoff between fairness and throughput in general networks. The characterization allows us both to produce the first counter-example and trivially explain all the previous supporting examples. Surprisingly, our counter-example has the property that a fairer allocation is always more efficient. In particular it implies that maxmin fairness can achieve a higher throughput than proportional fairness. Intuitively, we might expect that increasing link capacities always raises aggregate throughput. We show that not only can throughput be reduced when some link increases its capacity, more strikingly, it can also be reduced when all links increase their capacities by the same amount. If all links increase their capacities proportionally, however, throughput will indeed increase. These examples demonstrate the intricate interactions among sources in a network setting that are missing in a single-link topology.     

Traffic‐aware downlink power allocation in multiuser OFDM system

Multiuser orthogonal frequency division multiplexing (OFDM) is a promising technology to achieve high uplink/downlink (DL) capacities in the next generation broadband wireless networks such as WiMAX (Worldwide Interoperability for Microwave Access). In this paper, we investigate the DL adaptive power allocation (APA) in multiuser OFDM system from the perspective of cross‐layer design. Specifically, we formulate APA as an optimization problem with the traffic profile of each user asit a priori knowledge. To solve the optimization problem, we develop a fairness‐constrained optimal prioritized effective throughput (PET) strategy and the corresponding iterative algorithms, aiming at balancing the prioritized effective throughput and the linear or logarithmic user satisfaction‐based fairness. Simulation results show that our proposed APA optimization approach can achieve satisfying performance. Copyright © 2011 John Wiley & Sons, Ltd.     

Prediction‐based MAC‐layer sensing in cognitive radio networks

We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimization of energy-constrained wireless powered communication networks with heterogeneous nodes

In this paper, we generalize conventional time division multiple access (TDMA) wireless networks to a new type of wireless networks coined generalized wireless powered communication networks (g-WPCNs). Our prime objective is to optimize the design of g-WPCNs where nodes are equipped with radio frequency (RF) energy harvesting circuitries along with constant energy supplies. This constitutes an important step towards a generalized optimization framework for more realistic systems, beyond prior studies where nodes are solely powered by the inherently limited RF energy harvesting. Towards this objective, we formulate two optimization problems with different objective functions, namely, maximizing the sum throughput and maximizing the minimum throughput (maxmin) to address fairness. First, we study the sum throughput maximization problem, investigate its complexity and solve it efficiently using an algorithm based on alternating optimization approach. Afterwards, we shift our attention to the maxmin optimization problem to improve the fairness limitations associated with the sum throughput maximization problem. The proposed problem is generalized, compared to prior work, as it seemlessly lends itself to prior formulations in the literature as special cases representing extreme scenarios, namely, conventional TDMA wireless networks (no RF energy harvesting) and standard WPCNs, with only RF energy harvesting nodes. In addition, the generalized formulation encompasses a scenario of practical interest we introduce, namely, WPCNs with two types of nodes (with and without RF energy harvesting capability) where legacy nodes without RF energy harvesting can be utilized to enhance the system sum throughput, even beyond WPCNs with all RF energy harvesting nodes studied earlier in the literature. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show that conventional TDMA wireless networks and WPCNs with only RF energy harvesting nodes are considered as lower bounds on the performance of the generalized problem setting in terms of the maximum sum throughput and maxmin throughput. Moreover, the results reveal valuable insights and throughput-fairness trade-offs unique to our new problem setting.

     

PUMA: From Simultaneous to Parallel for Shared Memory System in Multi-core

In contemporary multi-core systems, memory is shared among a number of concurrent threads. Memory contention and interference are becoming increasingly severe incurring such problems as performance degradation, unfair resource sharing and priority inversion. In this paper, we aim at the challenge of improving performance and fairness for concurrent threads while minimizing energy consumption in main memory. Therefore, we propose PUMA, a novel solution that reduces memory contention and interference by judiciously partitioning threads among cores and allocating each core exclusive memory banks and bandwidth based on thread’s characteristics. Our results demonstrate that PUMA is able to improve both performance and fairness while reducing energy consumption significantly compared to existing memory management approaches.