一种OFDM系统动态子载波比特和功率分配联合算法

提出一种多用户OFDM(orthogonal frequency division multiplexing)系统下行链路,具有信道变化实时性的动态子载波、比特和功率分配联合算法(UA),在满足各个用户数据速率和BER要求的同时使总的发送功率最小。提出的算法与动态子载波分配算法(WSA)相比,计算复杂度相当,在移动信道环境下仿真结果表明性能有一定的改善。     

WCDMA系统中位置选择分集发射方式下的自适应小区分配算法研究

针对WCDMA系统软切换的位置选择分集发射（SSDT）方式,提出了一种自适应导频功率调整和主小区选择算法。采用这种算法,各基站能够根据各自小区负载和覆盖情况动态调整其导频功率,进行相邻小区间的负载均衡,另外,以系统效用最大化为目标,为每个用户选择最佳主小区。仿真结果表明,该算法能以可接受的计算复杂度换取系统性能的提高。     

提高中继辅助的MIMO系统通信可靠性的联合功率分配方法

为提高中继辅助的多输入多输出(MIMO)通信系统的通信可靠性,对系统的联合功率分配问题进行了研究。首先,提出了总均方误差最小化的优化准则,并给出了相应代价函数。其次,证明了该代价函数关于部分参数是凸函数而关于全局参数是非凸函数。最后,通过推导SMSE的上界,把原来的非凸问题转换为联合的凸优化问题,从而能够使用高效的凸优化方法获得全局最优的功率分配系数。仿真结果表明所提出的功率分配方案比现有方案有更好的性能。     

OFDM系统在时变信道下的迭代干扰抵消算法

为克服正交频分复用(OFDM)系统中因时变多径衰落信道破坏子载波的正交性引起载波间干扰(ICI),从而导致系统性能下降的问题,提出了一种基于最小均方误差(MMSE)估计和迭代算法相结合的迭代干扰抵消算法.为了消除一般MMSE均衡方法将载波间干扰当作加性高斯白噪声(AWGN)所带来的误差,该算法先使用MMSE方法对传输信号进行初值估计,再通过迭代干扰抵消和MMSE均衡相结合,来消除由时变信道所带来的载波间干扰.多次仿真表明,在时变多径衰落信道中使用本算法能够有效地消除载波间干扰,使系统性能得到很好的改善.     

基于改进粒子群算法的分布式天线系统跨层功率分配方案

结合数据链路层的队列状态信息和物理层的信道状态信息定义了系统的吞吐量系数和公平性系数,建立了分布式天线系统跨层功率分配的离散速率集优化模型。对粒子群优化算法的初始群体产生、粒子更新等步骤进行了改进,形成了改进粒子群算法(IMPSO)。利用IMPSO进行了动态功率分配和跨层优化。数值仿真结果表明,IMPSO能够取得很好的队列时延性能,选取不同的权重可对系统吞吐量性能和公平性性能产生重要影响,IMPSO获得的系统吞吐量性能和公平性性能较之功率固定分配算法(AP)均有较大幅度的提高。     

基于符号调整的OFDM系统载波间干扰抑制方法

基于组合优化理论,提出了一种通过调整子载波调制符号的正负来抑制正交频分复用(OFDM)系统载波间干扰(ICI)的方法.首先推导出了加性高斯白噪声(AWGN)信道下的最优符号选择算法,并给出了降低其复杂度的简化算法;然后给出了二进制相移键控(BPSK)调制下减少算法码率损失的改进算法,而且在频率选择性衰落信道下对以上算法进行了推广.仿真结果证实,提出方法的误码率低于基于选择映射的载波间干扰抑制方法.     

基于OFDMA子载波功率分配的FFR广义分析模型

基于正交频分多址接入(OFDMA)系统对分数阶频率复用(FFR)技术进行了研究。通过引入功率比值和软频率复用因子的概念,提出了FFR广义分析模型。目前现存的FFR方案均可作为特例统一在该广义分析模型的框架之下。该模型从OFDMA子载波功率分配的角度出发,为分析FFR提供了一种新的视角。理论分析表明,可以通过调整功率比值连续地调整软频率复用因子。对于基于FFR的OFDMA蜂窝系统,通过该模型可以有效地平衡小区容量和覆盖之间的矛盾。最后,基于全球互操作性微波接入(WiMAX)系统,从计算机仿真的角度证实了该结论,同时给出了功率比值的合理取值范围,这对于实际系统的设计颇具指导意义。     

OFDMA系统中支持混合业务的跨层调度算法

针对正交频分多址接入(OFDMA)系统下行链路的混合业务调度问题,提出了一种基于队列等待时间的跨层调度算法.该算法联合利用了MAC层的队列等待时间与物理层的信道状态信息作为调度参数,通过队列等待时间反映用户的服务质量要求,并利用多用户分集增益提高系统性能;针对实时和非实时用户的不同服务质量要求,在队列等待时间的计算上采取了不同的策略;在子载波的分配过程中根据分配状态及时更新队列等待时间,使资源的利用更为有效.仿真结果表明,提出的算法可显著降低实时用户的平均时延和最大延时违反概率,同时保证了非实时用户的吞吐量需求,能够有效地支持下一代网络中混合业务的多种服务质量要求.     

基于模糊理论的半导体制造系统时变多目标生产调度研究

针对半导体晶圆制造系统(SWFS)调度中的多目标优化和目标时变性问题,提出了一种时变多目标(TVMO)调度算法.基于模糊理论研究了时变模糊集合与多目标重要程度的复杂非线性量化关系,进而研究了SWDS的优化调度方法.所提出的方法兼顾最大产品交期率、最大生产移动量和瓶颈机台最大产能利用率三个目标,根据时变目标权重计算出在制品的加工优先权序列号.大量的仿真实验数据证明,该调度算法能够改善系统多个绩效目标,可为大规模复杂重入型制造系统的科学生产控制与调度提供有效支持.     

基于多代理系统的微电网多目标优化调度

微电网在并网情况下的多目标稳定运行是微电网运营和建设的基本要求。从并网型微电网的经济目标和环保目标出发,建立微电网多目标优化模型,其中经济目标考虑分布式电源的燃料费用、运行维护费用、启停费用和与大电网的能量交互费用,环保目标考虑污染气体的排放量。提出一种基于多代理系统（multi-agent system,MAS）的分时电价机制下储能装置的调度策略,并且采用改进的NSGA-Ⅱ算法优化可控微源和储能装置的出力。以某地区微电网单元为例进行仿真,验证了所建模型和改进算法的有效性。试验结果表明,改进的NSGA-Ⅱ算法性能更优,所提出的策略可以控制储能装置一个周期内的充放电次数,延长使用寿命。     

Decoding methods for the flow shop scheduling with peak power consumption constraints

The permutation flow shop scheduling problem (PFSP) is of wide application backgrounds and plays an important role in the manufacturing systems. With the serious energy concerns in manufacturing enterprises, peak power consumption is considered one of the significant issues. For the PFSP with peak power consumption constraints (PFSPP), the real-time power consumption cannot exceed a given peak power at any time. Since the classical first-come first-serve scheduling method is not suitable for the PFSPP, this paper addresses the decoding methods to obtain feasible schedules based on the permutation encoding scheme. First, an earliest processing rule (EPR) is designed to determine the starting time of each operation, satisfying the power consumption constraints. Then, five decoding methods based on EPR are proposed to determine the suitable priority between the operations to yield feasible schedules with high quality. After analysing the complexity of the proposed decoding methods and comparing the performances via extensive numerical tests, some suggestions are provided for solving the PFSPP with different scales and power constraints.     

分布式实时系统任务调度算法的设计和实现

针对分布式实时任务中容易引起的任务分配不合理及系统负载不平衡的情况,根据分布式实时任务多机执行的特点及分布式实时系统在同一结点上有多任务执行,提出了两级分布式系统结构下实时任务的调度策略：任务分配和任务调度。实验表明,在任务分配阶段提出的算法可以更合理地平衡任务,在任务调度阶段采用的调度算法能够更好地完成任务的执行。     

Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results

This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power.     

A hybrid scheduling approach for automated flowshops with material handling and time constraints

Flowshop scheduling problems have been extensively studied by several authors using different approaches. A typical flowshop process consists of successive manufacturing stages arranged in a single production line where different jobs have to be processed following a predefined production recipe. In this work, the scheduling of a complex flowshop process involving automated wet-etch station from semiconductor manufacturing systems requires a proper synchronisation of processing and transport operations, due to stringent storage policies and fixed transfer times between stages. Robust hybrid solution strategies based on mixed integer linear programming formulations and heuristic-based approaches, such as aggregation and decomposition methods, are proposed and illustrated on industrial-scale problems. The results show significant improvements in solution quality coupled with a reduced computational effort compared to other existing methodologies.     

基于MPSO的有限缓冲区多产品厂间歇调度问题的研究

研究了以最小化最大完工时间为目标的有限缓冲区多产品厂间歇调度问题,提出了一种基于多种群粒子群优化(MPSO)的间歇调度算法.该算法采用多种群,增加了种群初始粒子的多样性,在每一代子种群并行进化的过程中引入移民粒子,使子种群之间相互影响和促进,避免算法过早地陷入局部最优,提高了算法的全局搜索能力;每代进化后选出子种群中的优秀粒子作为精华种群,并对其进行变邻域搜索(VNS),进一步提高了算法的收敛精度.通过对不同规模调度问题的仿真,以及与其它算法的对比,证明了该算法解决有限缓冲区多产品厂间歇调度问题的有效性和优越性.     

平面波超声功率测量的互易法与辐射力法实验比较研究

本文分别用互易法[1]和辐射力法对两个不同的平面超声换能器声功率进行实验测量。实验数据对比研究 表明:在实验所用的激励电压范围内,两种方法测量结果具有很好的一致性,互易法综合不确定度为±4.4%,辐射 力法综合不确定度为±6.9%。由于互易法具有使用设备简单、信噪比高以及受气泡和测量环境的影响小等优点, 有利于提高测量精度,适合于小功率测量,是超声功率测量的一种简单有效的新途径。     

Improved mixed-integer linear programming model and heuristics for bi-objective single-machine batch scheduling with energy cost consideration

This article addresses bi-objective single-machine batch scheduling under time-of-use electricity prices to minimize the total energy cost and the makespan. The lower and upper bounds on the number of formed batches are first derived and a continuous-time mixed-integer linear programming model is proposed, which improves an existing discrete-time model in the literature. Two improved heuristics are proposed based on the improved model. Computational experiments demonstrate that the improved model and heuristics can run hundreds of times faster than the existing ones for large-size instances.