Generalized quality-of-service routing with resource allocation

We present a general framework for the problem of quality-of-service (QoS) routing with resource allocation for data networks. The framework represents the QoS parameters as functions rather than static metrics. The formulation incorporates the hardware/software implementation and its relation to the allocated resources into a single framework. The proposed formulation allows intelligent adaptation of QoS parameters and allocated resources during a path search, rather than decoupling the path search process from resource allocation. We present a dynamic programming algorithm that, under certain conditions, finds an optimal path between a source and destination node and computes the amount of resources needed at each node so that the end-to-end QoS requirements are satisfied. We present jitter and data droppage analyzes of various rate-based service disciplines and use the dynamic programming algorithm to solve the problem of QoS routing with resource allocation for networks that employ these service disciplines.     

Graph‐based resource allocation algorithms for multiuser downlink MIMO‐OFDMA networks

Multiuser multiple‐input multiple‐output orthogonal frequency division multiple access (MIMO‐OFDMA) is considered as the practical method to attain the capacity promised by multiple antennas in the downlink direction. However, the joint calculation of precoding/beamforming and resource allocation required by the optimal algorithms is computationally prohibitive. This paper proposes computationally efficient resource allocation algorithms that can be invoked after the precoding and beamforming operations. To support stringent and diverse quality of service requirements, previous works have shown that the resource allocation algorithm must be able to guarantee a specific data rate to each user. The constraint matrix defined by the resource allocation problem with these data rate constraints provides a special structure that lends to efficient solution of the problem. On the basis of the standard graph theory and the Lagrangian relaxation, we develop an optimal resource allocation algorithm that exploits this structure to reduce the required execution time. Moreover, a lower‐complexity suboptimal algorithm is introduced. Extensive simulations are conducted to evaluate the computational and system‐level performance. It is shown that the proposed resource allocation algorithms attain the optimal solution at a much lower computational overhead compared with general‐purpose optimization algorithms used by previous MIMO‐OFDMA resource allocation approaches. Copyright © 2012 John Wiley & Sons, Ltd.     

基于地理位置的频谱共享系统功率分配

针对次用户地理位置分布的随机性,提出一种基于地理位置的多信道underlay频谱共享模型,并设计了一种基于凸优化的功率分配算法以提高频谱资源利用率。首先根据不同地理位置的次用户对主用户干扰的差异,设计一种基于产出投入比的公平性信道分配准则,将复杂的多信道资源分配问题转化为单信道功率分配问题;继而采用一种基于凸优化的迭代寻优方式进行功率分配,最终获得次用户的系统信道容量。仿真结果表明,相比按接入时间先后分配信道的方法,基于地理位置的多信道underlay频谱共享模型可以获得更高的信道容量。     

基于补偿式CSI的分布式跨层联合资源分配算法

为消除过时信道状态信息(CSI)对分布式无线多跳网络环境下跨层资源分配效率的影响,提高跨层联合资源分配的准确性,基于信道相关性提出了一种补偿式跨层联合资源分配算法。利用瞬时和过时信道状态信息之间的条件概率密度函数,基于瑞利衰落信道模型求得信噪比(SINR) 模型下条件容量的闭式解。为补偿部分网络性能的损失,提出了一种考虑过时信道状态信息的联合拥塞控制、信道分配和功率控制的算法,在此过程中网络被建模成一个NUM 问题,可变的链路数据率和功率等资源限制作为约束条件。运用拉格朗日对偶分解技术,NUM问题被分布式求解。实验对比分析表明：在确保较低复杂度的前提下,该算法有效改善了分布式多跳网络资源分配的合理性,使其网络总体效用得到提升,降低了能耗。     

Memory-efficient high-speed VLSI implementation of multi-level discrete wavelet transform

Memory requirements and critical path are essential for 2-D Discrete Wavelet Transform (DWT). In this paper, we address this problem and develop a memory-efficient high-speed architecture for multi-level two-dimensional DWT. First, dual data scanning technique is first adopted in 2-D 9/7 DWT processing unit to perform lifting operations, which doubles the throughputs per cycle. Second, for 2-D DWT architecture, the proposed Row Transform Unit and Column Transform Unit take advantage of input sample availabilities and provision computing resources accordingly to optimize the processing speed, in which the number of processors is further optimized to significantly reduce the hardware cost. Third, to address the problem of high cost of memory for the immediate computing results from each level and the computation time as resolution level increases, multiple proposed 2-D DWT units were combined to build a parallel multi-level architecture, which can perform up to six levels of 2-D DWT in a resolution level parallel way on any arbitrary image size at competitive hardware cost. Experimental results demonstrated that the proposed scheme achieves improved hardware performance with significantly reduced on-chip memory resource and computational time, which outperforms the-state-of-the-art schemes and makes it desirable in memory-constrained real-time application systems.     

Dynamic hybrid duplex for rate maximization in OFDMA

Orthogonal frequency division multiple access (OFDMA) is considered as one of the technologies for 4th generation (4G) due to its robustness against multi‐path environment. OFDMA also has the advantage of flexibility in resource allocation, which cannot be supported by existing duplex schemes. In this paper, we propose dynamic hybrid duplex (DHD) which will enhance the efficiency and flexibility of the OFDMA system. To establish the framework of resource allocation for the DHD, we formulate DHD resource allocation problem (DRAP) which maximizes the total users' data rate with power, rate, and subcarrier allocation constraints. A heuristic procedure is developed to solve the problem. The procedure finds the best mode and the best amount of resources allocated to downlink (DL) and uplink (UL). Simulation is performed with five scenarios to evaluate the DHD. Its results show that the proposed DHD outperforms other duplex schemes in various environments. Copyright © 2010 John Wiley & Sons, Ltd.     

基于用户画像与关联规则的全域电力资源调度算法

为解决应用调度算法进行全域电力资源调度,资源剩余率依旧较高的问题,提出结合用户画像与关联规则的新型调度算法,实现全域电力资源的合理分配。运用双聚类算法,对整个调度区域内所有用户用电数据进行分析,构建电力用户画像从而描述用户用电个性化需求;以用户画像为基础,建立以满足用户需求为核心的全域资源分配模式;总结全域内资源调度子任务,计算不同子任务之间的支持度和置信度,结合关联规则实现子任务的分组;根据子任务组进行资源分域,在每个分域中设置二级调度中心,再与全域一级调度中心相连接,实现全域资源集中调度。实验结果表明,所提调度算法应用后,电力测试系统每日的全域资源剩余率出现了大幅降低,仅保持在12%左右。该算法具有较好的实际应用价值。     

Distributed Scheduling and Dynamic Pricing in a Communication Network

This paper studies dynamic resource allocation in a decentralized communication network. The temporal aspect in the decentralized resource allocation problem presents new challenges, e.g., in optimizing the delay-throughput trade-off under user-specific delay costs. A dynamic bandwidth allocation game modelling an agent-based network is presented. The dynamic noncooperative game achieves Pareto-efficient bandwidth allocation that can be implemented by a greedy algorithm with pricing. Optimal dynamic pricing is discussed for the efficient sharing of network resources. An ad hoc wireless network is an example of such self-organizing decentralized system: the mobile nodes need not be directly connected to a base station. Another application of the model is to consider distributed uplink scheduling, based on local information, in a WCDMA network. The discretized control variable of a mobile node is either the received power/QoS-level or the binary decision on packet transmission.     

DMA在内存间数据拷贝中的应用及其性能分析

在嵌入式系统中,进行大批量的数据拷贝操作会占用很多CPU资源,降低了系统响应速度.直接存储器存取(DMA)是一种高效的I/O(输入输出)方式,具有传输速度快、CPU资源消耗低的特点.使用DMA方式进行数据传输可以占用较少的CPU资源,同时获得较快的系统的响应速度和数据拷贝速率.因此可以将DMA方式应用到需要许多数据拷贝的场合.本文分析了DMA的原理和嵌入式处理器中DMA控制器(DMAC)的特点,给出了DMA应用的一些实例和内存之间使用DMA方式进行数据拷贝的性能分析.     

基于速率约束的OFDM中继链路能效最优资源分配策略

该文研究解码转发(DF)模式的OFDM中继链路的能效最大化资源分配问题。与现有典型的固定速率最小化发射功率或无约束最大化能效算法不同,该文考虑电路功率消耗的前提下,将问题建模为以最大化系统能效为目标,同时考虑用户最小速率需求、源节点S和中继节点R各自总发射功率约束下的联合子载波配对和最优功率分配问题。证明了速率和功率联合约束条件下中继链路全局能效最优解的唯一性,在此基础上提出一种低复杂度联合最优资源分配策略。仿真结果表明,该文所提方案能够在最小速率和S/R节点最大发射功率约束下自适应分配功率资源,实现系统能效最优,并能够降低链路的中断概率。     

Proportional-fair power allocation with CDF-based scheduling for fair and efficient multiuser OFDM systems

In this paper we investigate adaptive resource allocation schemes in multiuser OFDM systems for fair share of resources and efficient operation. We employ the CDF-based scheduling (CS) algorithm for the subcarrier allocation, taking advantage of its distinctive feature of analyzability and multiuser diversity. Noting that conventional power allocation schemes do not exhibit efficient and fair operations in heterogeneous user channel environment, we present a new algorithm called proportional-fair power allocation (PFPA). This algorithm is designed to allocate transmission power in such a way that the resulting relative throughput-increment is identical for all subcarriers. The PFPA algorithm is shown to be equivalent to the power allocation of the asymptotically optimal algorithm, which exhibits the largest achievable region in the asymptotic case. Numerical results reveal that the combined CS-PFPA algorithm improves the overall system capacity in terms of time-average throughput and provides efficient estimation of user performances. Further, the CS-PFPA algorithm can meet each user's requirements using a minimum amount of resources, so it renders an efficient and fair means for resource allocation in multiuser OFDM systems.     

Resource allocation in MIMO‐OFDM‐based cooperative cognitive radio networks: optimal and suboptimal low complexity approaches

The problem of resources allocation in multiple‐input multiple‐output‐orthogonal frequency division multiplexing based cooperative cognitive radio networks is considered, in this paper. The cooperation strategy between the secondary users is decode‐and‐forward (DF) strategy. In order to obtain an optimal subcarrier pairing, relay selection and power allocation in the system, the dual decomposition technique is recruited. The optimal resource allocation is realized under the individual power constraints in source and relays so that the sum rate is maximized while the interference induced to the primary system is kept below a pre‐specified interference temperature limit. Moreover, because of the high computational complexity of the optimal approach, a suboptimal algorithm is further proposed. The jointly allocation of the resources in suboptimal algorithm is carried out taking into account the channel qualities, the DF cooperation strategy, the interference induced to the primary system and the individual power budgets. The performance of the different approaches and the impact of the constraint values and deploying multiple antennas at users are discussed through the numerical simulation results. Copyright © 2014 John Wiley & Sons, Ltd.     

基于蚁群优化算法的出租车调度方法研究

城市交通工具的合理调度能够有效缓解日益严峻的交通压力,出租车作为公共出行的交通工具满足了大量的出行需求。蚁群算法(ACO)作为仿生算法的代表,根据蚂蚁个体产生的信息素,通过不同策略和信息素更新等操作,逐步接近最优解,适合解决城市交通资源路径规划问题。文章给出一种改进的蚁群算法进行出租车调度,在不同时间段内,对非热点区域向热点区域以及热点区域向非热点区域转移进行研究,根据信息素差异化特征,首先建立了时间区域优化算法和区域调度模型,通过对数据样本的训练得到不同情况下的转移概率和行驶里程,从而确定最优的抑制因子和调节参数,提高出租车转移概率并减少空载行驶距离,实现对出租车资源的合理分配。     

Energy minimization resource allocation schemes for relay-enhanced OFDMA networks

This paper studies the problem of joint allocation of subchannel, transmission power, and phase duration in the relay-enhanced bidirectional orthogonal frequency-division multiple access time division duplex systems. The challenges of this resource allocation problem arise from the complication of multiple-phase assignments within a subchannel since the relay station can provide an additional signal path from the base station to the user equipments (UEs). Existing research work does not fully consider all the influential factors to achieve feasible resource allocation for the relay-based networks. Since energy consumption is one of the principal issues, the energy minimization resource allocation (EMRA) schemes are proposed in this paper to design the allocation of subchannel, power, and phase duration for the UEs with the consideration of UE’s quality-of-service (QoS) requirements. Both the four-phase and two-phase bidirectional relaying assignments and the network coding technique are adopted to obtain the suboptimal solutions for the proposed EMRA schemes. Different weights are designed for the UEs to achieve the minimization of weighted system energy for the relay-enhanced networks. Simulation results show that the proposed EMRA schemes can provide comparably better energy conservation and outage performance with QoS support.

     

The One-Shot Task Model for Robust Real-Time Embedded Control Systems

Embedded control systems are often implemented in small microprocessors enabled with real-time technology. In this context, control laws are often designed according to discrete-time control systems theory and implemented as hard real-time periodic tasks. Standard discrete-time control theory mandates to periodically sample (input) and actuate (output). Depending on how input/output (I/O) operations are performed within the hard real-time periodic task, different control task models can be distinguished. However, existing task models present important drawbacks. They generate task executions prone to violate the periodic control demands, a problem known as sampling and latency jitter, or they impose synchronized I/O operations at each task job execution that produce a constant but artificially long I/O latency.      

一种用于多目标定位的MIMO雷达快速功率分配算法

为了满足实际需求,该文提出一种用于多目标定位的功率分配算法,实现MIMO雷达有限功率的快速优化分配。该文首先给出了多目标定位误差的克拉美罗下界,并将其作为代价函数进行功率分配。而后,设计了一种可应用于多目标定位功率分配的交替全局优化算法,通过搜索Pareto解集来实现功率的快速分配。最后,仿真结果表明,所提的算法能快速实现MIMO雷达有限功率的优化分配,明显提升多目标定位精度。     

Throughput and resource optimization for adaptive coding‐based random access networks with correlated sources

Modern wireless communication networks frequently have lower application throughput due to higher number of collisions and subsequent retransmission of data packets. Moreover, these networks are characterized by restricted computational capacity due to limited node‐battery power. These challenges can be assessed for deploying fast, reliable network design with resource‐restrained operation by means of concurrent optimization of multiple performance parameters across different layers of the conventional protocol stack. This optimization can be efficiently accomplished via cross‐layer design with the aid of network coding technique and optimal allocation of limited resources to wireless links. In this paper, we evaluate and analyze intersession coding across several source–destination pairs in random access ad hoc networks with inherent power scarcity and variable capacity links. The proposed work addresses the problem of joint optimal coding, rate control, power control, contention, and flow control schemes for multi‐hop heterogeneous networks with correlated sources. For this, we employ cross‐layer design for multiple unicast sessions in the system with network coding and bandwidth constraints. This model is elucidated for global optimal solution using CVX software through disciplined convex programming technique to find the improved throughput and power allocation. Simulation results show that the proposed model effectively incorporates throughput and link power management while satisfying flow conservation, bit error rate, data compression, power outage, and capacity constraints of the challenged wireless networks. Finally, we compare our model with three previous algorithms to demonstrate its efficacy and superiority in terms of various performance metrics such as transmission success probability, throughput, power efficiency, and delay.     

Distributed opportunistic scheduling for MIMO underlay cognitive radio networks

Cognitive radio networks have emerged to improve the utilization of the scarce spectrum. In this paper, we propose a distributed resource allocation algorithm that allocates resources opportunistically to the secondary users in a multiple‐input multiple‐output environment. In order to reduce the complexity and cost, antenna selection schemes are employed to allow the secondary communication using a single radio frequency chain. The proposed algorithm is proved theoretically and using simulations, to give a performance very close to that of a centralized one with lower delay and overhead. Furthermore, we introduce two techniques for the proposed algorithm based on the allowable data rates referred to as limited and maximum rates. We derive closed‐form expression for the consumed power and tight upper bounds for the average throughput achieved by each technique. A comparison between the proposed techniques is also provided. Both simulations and analytical results show that the proposed algorithm achieves high throughput with low complexity. Moreover, the results show that the tightness of the bounds improves with the diversity order. Finally, the proposed techniques are compared with two suggested random schemes to investigate their effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.     

车联网中基于MEC的V2X协同缓存和资源分配

针对车联网的多媒体业务快速增长,大量的数据交换为移动网络带来沉重负担的问题,构建了一种车联网中基于移动边缘计算的V2X协同缓存和资源分配框架.提出了V2X协同缓存与资源分配机制,实现网络内计算、缓存和通信资源的有效分配;利用图着色模型为卸载用户分配信道;采用拉格朗日乘子法对功率与计算资源进行分配.仿真结果表明,在不同的...