异构网载波聚合系统基于干扰对齐的联合算法研究*

为了满足异构网络热点区域覆盖,同时解决由于低功率基站在高功率基站覆盖区域随意分布而带来的小区间干扰及系统容量速率受限问题,提出了一种载波聚合系统的联合小区选择、载波选择和干扰对齐算法。该方法是在载波公平比例准则下,动态的选择成员载波。为了进一步降低小区间干扰,设计了新的基于干扰对齐的用户小区选择算法。理论分析表明,联合优化算法问题可以分解成三个子问题进行优化求解处理。仿真结果表明,与调度复用因子为1的参考算法相比,设计的方案由于联合了小区选择和载波选择使得用户选择低功率节点作为服务小区的机会增多,提高了低功率节点小区容量,使系统的吞吐量得到提高。     

基于混合网络的小区间干扰协调研究

在蜂窝与Ad hoc混合网络中,小区边缘用户会受到邻近小区边缘用户的同频干扰.针对该问题,提出一种基于子载波分配的小区间干扰协调方案.该方案中的用户子载波分配选择算法具有信道增益选择性,且能对最小化子载波进行重利用,可提高小区边缘区域用户的通信性能.仿真结果证明,与软频率复用和部分频率复用方案相比,该方案能更有效地增加小区边缘用户的吞吐量.     

5G超密异构网络中联合虚拟波束形成的小区选择算法

随着移动通信技术的发展,移动用户设备呈爆炸性增长趋势,使异构网中小区密集,造成小区间严重干扰,严重影响系统吞吐量.基于5G超密异构网络结构,研究了以用户为中心,基于动态小区选择与联合优化,提出了联合虚拟波束形成的小区选择算法.仿真结果表明:该算法协调了小区间干扰和频谱效率的矛盾,提高了边缘用户的性能和小区的平均吞吐量.     

LTE系统自适应软频率复用技术研究

提高小区边缘用户性能是蜂窝移动通信系统的经典难题.下一代蜂窝移动通信系统3GPP LTE通过有效的小区间干扰协调以及频率资源分配来改善小区边缘用户性能.提出一种自适应软频率复用算法,根据系统负载情况自适应地为各小区分配主、副载波及其发射功率,在实现系统吞吐量优化的同时保证小区中心和边缘区域速率需求.算法首先通过穷尽搜索和贪婪递减策略,获得单小区最优资源分配,然后在不同小区间迭代执行单小区算法直到系统吞吐量不变为止.仿真结果表明,算法通过多次迭代后,系统吞吐量保持不变并输出一种优化的资源分配方案.与同类频率分配算法相比,可以有效提升小区边缘用户的吞吐量,同时获得更高的系统容量,更适用于高速率的LTE系统.     

异构网络中基于干扰负载比的小区选择策略

现有的小区选择算法在追求负载平衡的同时,往往没有考虑算法本身对用户干扰的影响,在一定程度上影响了小区性能指标。针对此类问题,从负载均衡和干扰管理两方面考虑,提出一种折中两项性能指标的小区选择算法。该算法首先将小区选择问题建模为负载和干扰的联合优化问题,然后对该最优化问题通过一种启发式算法进行求解,得出最佳用户小区选择因子矩阵,从而进一步提升异构小区的系统性能。仿真表明,该方案较其他传统小区选择方案在用户接入公平性和用户平均吞吐量上均有较大的提升。     

基于改进HSARSA(λ)算法的功率控制研究

在MBS-PBS两层异构网络中,微微基站采用小区范围扩展技术对网络进行负载均衡时,pico小区边缘用户的通信受到MBS基站较大干扰。为此,提出一种基于启发函数的改进HSARSA(λ)算法。采用缩减功率的RP-ABS子帧技术,在保证宏基站自身通信性能的同时减小MBS基站对pico边缘用户的干扰,并运用基于启发函数的改进HSARSA(λ)算法与环境进行交互,以配置RP-ABS子帧密度与功率大小,达到干扰协调的目的。仿真结果表明,改进HSARSA算法与原始SARSA和Q-Learning等算法相比,pico边缘用户吞吐量分别提升12%和40%,系统用户吞吐量分别提升10.3%和20.2%,有效提高了pico边缘用户的通信性能。     

超密集网络中小小区分簇和子载波分配算法

超密集网络中,严重的小区间干扰制约了终端用户尤其是边缘用户的数据速率。有效地对干扰进行管理、提升边缘用户的数据速率是超密集网络中的研究难点。在超密集网络架构的基础上,提出一种基于图论的不完全染色算法,对网络架构中的基站划分簇,同簇内的不同基站共享频带资源。同时,提出一种子载波分配算法,优先为边缘用户分配信道增益较优的子载波。通过仿真表明,不完全染色算法能够有效地减小小区间干扰,提升系统吞吐量,子载波分配算法在不影响系统吞吐量的基础上能够优化边缘用户的吞吐量。     

基于SLNR准则的多点协作联合处理算法

多小区单用户联合传输能显著改善小区边缘用户的链路性能, 但降低了协作小区的平均吞吐量; 多小区多用户联合传输在改善小区边缘用户性能的同时, 通过协作小区内多个移动终端的复用, 提升协作小区的平均吞吐量。根据SLNR准则提出了一种单用户和多用户自适应切换的多小区联合处理算法, 结合协作集合选择、选择协作集合内的配对用户、MU-MIMO处理等形成一个完整的CoMP处理过程。该算法的单用户和多用户MIMO算法处理相似, 可简化系统处理过程, 并通过自适应选择单/多用户模式提高算法鲁棒性, 有利于提高CoMP的系统增益。根据LTE-Advanced系统的参数进行了系统仿真, 结果证明, 该算法性能优于多小区单用户联合处理和单小区多用户处理的性能, 可提高协作小区吞吐量25%~40%。     

基于用户多业务模型的OFDM蜂窝网下行链路自适应小区选择算法

当前有基于用户接收信噪比的选择算法和基于平均用户阻塞率的选择算法两种蜂窝网络的自适应小区选择算法。但是这两种算法都不适合应用于多业务OFDM蜂窝系统。无法保证不同速率、不同实时性要求的用户接入系统的公平性,同时系统总体的阻塞率性能和吞吐量性能也无法尽可能最优。于是本文提出了一种基于多业务共存模型的自适应小区选择算法。仿真结果表明,本文所提出算法显著提高了实时性要求较高的高优先级用户的接入性能,同时又通过优先级浮动制度使得最终系统总体接入性能尽可能接近于最优。本算法特别适用于多业务共存的新一代蜂窝移动系统。     

考虑频谱感知错误的多载波认知无线电资源分配算法

认知无线电(CR)资源分配中二级用户对主用户造成的干扰源于两方面,即带外频谱泄露和频谱感知错误。滤波器组多载波(FBMC)技术和正交频分复用(OFDM)技术相比,FBMC带外泄露较小,频谱利用率较高。FBMC技术考虑干扰来源,可以降低二级用户对主用户的干扰,提高CR系统吞吐量。为此,提出考虑频谱感知错误的CR资源分配算法,建立干扰模型,将资源分配分步简化为载波分配和功率分配,在干扰约束和功率约束条件下对二级用户进行功率分配。基于FBMC和OFDM系统的仿真结果表明,该算法对主用户造成的干扰更小,CR系统可以获得更大的吞吐量,FBMC的干扰和吞吐量性能均优于OFDM。     

基于环境选择和配对选择的多目标粒子群算法

为了使多目标粒子群算法中种群粒子能够快速地收敛于怕累托最优边界,针对标准多目标粒子群算法中缺乏粒子评价标准以及种群个体历史最优值位置和全局最优值位置选择问题,提出了一种基于环境选择和配对选择策略的多目标粒子群算法.该算法在每次迭代时,采用SPEA2中的环境选择和配对选择策略及适应度值计算方法,以此来提高种群粒子之间的信息交换力度,减少标准多目标粒子群算法中大量的随机性,使种群粒子能够更快速地收敛于怕累托最优边界.经典测试函数的仿真实验结果表明,在标准多目标粒子群算法中运用SPEA2的环境选择、配对选择策略和适应度值计算方法,能够使种群粒子更快速地收敛于帕累托最优边界,验证了算法改进的可行性和有效性.     

选择性聚类融合研究进展

传统的聚类融合方法通常是将所有产生的聚类成员融合以获得最终的聚类结果。在监督学习中,选择分类融合方法会获得更好的结果,从选择分类融合中得到启示,在聚类融合中应用这种方法被定义为选择性聚类融合。对选择性聚类融合关键技术进行了综述,讨论了未来的研究方向。     

Hybrid clustering solution selection strategy

Cluster ensemble approaches make use of a set of clustering solutions which are derived from different data sources to gain a more comprehensive and significant clustering result over conventional single clustering approaches. Unfortunately, not all the clustering solutions in the ensemble contribute to the final result. In this paper, we focus on the clustering solution selection strategy in the cluster ensemble, and propose to view clustering solutions as features such that suitable feature selection techniques can be used to perform clustering solution selection. Furthermore, a hybrid clustering solution selection strategy (HCSS) is designed based on a proposed weighting function, which combines several feature selection techniques for the refinement of clustering solutions in the ensemble. Finally, a new measure is designed to evaluate the effectiveness of clustering solution selection strategies. The experimental results on both UCI machine learning datasets and cancer gene expression profiles demonstrate that HCSS works well on most of the datasets, obtains more desirable final results, and outperforms most of the state-of-the-art clustering solution selection strategies.     

遗传算法选择策略比较

以遗传算法中的轮盘赌选择策略和锦标赛选择策略作为研究对象,通过在13个基准测试函数上的测试,对不同选择策略的性能进行了比较和分析.实验结果表明,锦标赛选择策略比轮盘赌选择策略具有更好的通用性,而且性能更优.在锦标赛选择策略中,组规模为种群规模的60%至80%的锦标赛选择策略效果较好.该实验结果为设计更加合理高效的选择策略提供了有用的参考.     

Non-parametric classifier-independent feature selection

Feature selection is used for finding a feature subset that has the most discriminative information from the original feature set. In practice, since we do not know the classifier to be used after feature selection, it is desirable to find a feature subset that is universally effective for any classifier. Such a trial is called classifier-independent feature selection. In this study, we propose a novel classifier-independent feature selection method on the basis of the estimation of Bayes discrimination boundary. The experimental results on 12 real-world datasets showed the fundamental effectiveness of the proposed method.     

遗传算法选择方法的比较分析

本文基于改进的基本遗传算法实验,对选择方法进行了比较分析的研究,测试了四种不同选择方法：轮盘赌选择法、锦标赛选择法、随机遍历选择法以及一种新的基于种群交流的选择方法,分析比较这四种不同选择方法封种群发展及最佳适应值的影响。结果表明各种选择方法各有特点。最後为了防止陷入局部收敛,而对轮盘赌选择方法进行了改进,并比较了改进前後的结果,发现改进后的结果要好一些。     

Gene selection with guided regularized random forest

The regularized random forest (RRF) was recently proposed for feature selection by building only one ensemble. In RRF the features are evaluated on a part of the training data at each tree node. We derive an upper bound for the number of distinct Gini information gain values in a node, and show that many features can share the same information gain at a node with a small number of instances and a large number of features. Therefore, in a node with a small number of instances, RRF is likely to select a feature not strongly relevant.Here an enhanced RRF, referred to as the guided RRF (GRRF), is proposed. In GRRF, the importance scores from an ordinary random forest (RF) are used to guide the feature selection process in RRF. Experiments on 10 gene data sets show that the accuracy performance of GRRF is, in general, more robust than RRF when their parameters change. GRRF is computationally efficient, can select compact feature subsets, and has competitive accuracy performance, compared to RRF, varSelRF and LASSO logistic regression (with evaluations from an RF classifier). Also, RF applied to the features selected by RRF with the minimal regularization outperforms RF applied to all the features for most of the data sets considered here. Therefore, if accuracy is considered more important than the size of the feature subset, RRF with the minimal regularization may be considered. We use the accuracy performance of RF, a strong classifier, to evaluate feature selection methods, and illustrate that weak classifiers are less capable of capturing the information contained in a feature subset. Both RRF and GRRF were implemented in the “RRF” R package available at CRAN, the official R package archive.     

Determine the optimal carrier selection for a logistics network based on multi-commodity reliability criterion

From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.     

Vendor selection in outsourcing

In any large organization, millions of dollars are spent on outsourcing. Most large organizations are outsourcing those activities that are either not cost efficient if done in-house or not core to their businesses. One of the most critical steps in outsourcing is vendor selection, which is a strategic decision. We model the vendor selection problem as a multi-objective optimization problem, where one or more buyers order multiple products from different vendors in a multiple sourcing network. Price, lead-time and rejects (quality) are explicitly considered as three conflicting criteria that have to be minimized simultaneously. A pricing model under quantity discounts is used to represent the purchasing cost. We present and compare several multi-objective optimization methods for solving the vendor selection problem. The methods include weighted objective, goal programming and compromise programming. The multicriteria models and the methods are illustrated using a realistic example. Value path approach is used to compare the results of different models.