茅洲河流域再生水的综合优化配置

对深圳市茅洲河流域工业生产、农业灌溉、城市绿地浇灌、道路广场浇洒以及居民冲厕等方面的再生水需求进行分析,建立包括4个再生水厂、35个再生水用水片区的再生水优化配置模型,并运用LINGO求解其全局最优解。结果表明:①茅洲河流域再生水厂的生产能力为59.4万m3/d,仅能满足该流域污水处理需求的51.7%;②农业灌溉、工业回用、城市绿地浇灌、道路广场浇洒、居民冲厕等行业使用再生水的边际收益分别为3.7元/m3、21.6元/m3、4.1元/m32、.1元/m3、1.3元/m3。     

以水功能区为基本单元的流域水资源配置方法探讨

以水功能区为基本单元,以水功能区管理办法和分水协议为基础,以滦河流域为研究对象,综合考虑上下游工农业用水和生态环境用水,对各水功能区的水资源潜在利用量和实际可利用量确定方法进行分析,探讨滦河流域水资源配置和合理利用方案,为水资源的合理开发利用提供参考依据。     

Uplink resource allocation in OFDMA system using distributed antennas

In this article, we investigate uplink resource allocation in orthogonal frequency division multiple access （OFDMA） systems that use distributed antennas. The multiple distributed receiving antennas motivate us to exploit the spatial diversity to maximize the achievable rate of the system. Given constraints on the subcarrier allocation and the power limit of each user equipment （UE）, a mixed-integer optimization problem is formulated. We first propose a centralized greedy algorithm to find the maximum achievable rate of the system. Then a heuristic algorithm is proposed that can be implemented in a distributed manner. Simulation results show that the heuristic algorithm can approximately achieve the maximum achievable rate of the system and result in significant system performance improvement over the approach assigning one user to each subcarrier.     

Joint relay selection and power allocation for amplify-and-forward two-path relaying networks

This paper presents a joint relay selection and power allocation scheme for amplify-and-forward two-path relaying networks,in which diferent relay nodes forward information symbols alternatively in adjacent time slots.Our approach is based on the maximization of the received signal-to-noise ratio under total power consumption by the transmission of the symbol.We show that in spite of inter-relay interferences,the maximization problem has a closed-form solution.Simulation results explicitly indicate that the performance of proposed approach outmatches the existing methods including equal power allocation and one-path relaying.     

Dynamically reconfigurable radio-over-fiber network with medium access control protocol to provide network access to train passengers

An innovative dynamically reconfgurable radio-over-fber(RoF)network equipped with an intelligent medium access control(MAC)protocol is proposed to provide broadband access to train passengers in railway high-speed mobile applications.The proposed RoF network architecture is based on a reconfgurable control station and remote access unit(RAU)that is equipped with a fxed flter and tunable flter.The proposed hybrid frequency-division multiplexing/time division multiple access(FDM/TDMA)based MAC protocol realizes failure detection/recovery and dynamic wavelength allocation to remote access units.Simulation result shows that with the proposed MAC protocol,the control station can detect failures and recover and dynamic wavelength allocation can increase the wavelength resource utilization to maintain network performance.     

A suboptimal joint bandwidth and power allocation for cooperative relay networks＂ a cooperative game theoretic approach

In commercial networks, user nodes operating on batteries are assumed to be selfish to consume their resources (i.e., bandwidth and power) solely maximizing their own benefits (e.g., the received signal-to-noise ratios (SNRs) and datarates). In this paper, a cooperative game theoretical framework is proposed to jointly perform the bandwidth and power allocation for selfish cooperative relay networks. To ensure a fair and efficient resource sharing between two selfish user nodes, we assume that either node can act as a source as well as a potential relay for each other and either node is willing to seek cooperative relaying only if the datarate achieved through cooperation is not lower than that achieved through noncooperation (i.e., direct transmission) by consuming the same amount of bandwidth and power resource. Define the cooperative strategy of a node as the number of bandwidth and power that it is willing to contribute for relaying purpose. The two node joint bandwidth and power allocation (JBPA) problem can then be formulated as a cooperative game. Since the Nash bargaining solution (NBS) to the JBPA game (JBPAG) is computationally difficult to obtain, we divide it into two subgames, i.e., the bandwidth allocation game (BAG) and the power allocation game (PAG). We prove that both the subgames have unique NBS. And then the suboptimal NBS to the JBPAG can be achieved by solving the BAG and PAG sequentially. Simulation results show that the proposed cooperative game scheme is efficient in that the performance loss of the NBS result to that of the maximal overall data-rate scheme is small while the maximal-rate scheme is unfair. The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.     

四网协同资源配置模型战略分析

四网协同是中国移动3大战略之一。如何通过科学的资源配置,真正实现四网协同发展,是中国移动集团、省市公司和地市公司共同关注的核心议题。在四网大协同的战略背景下,通过研究搭建了一个基于四网协同的闭环、联动、可获取、可评估的资源配置分析模型。一方面,通过体系化的四网资源配置模型,能够有效地支撑中国移动四网战略的落地,保障四网协同战略的有效实施;另一方面,通过闭环的投资策略方案比较分析,支撑中国移动集团公司与各省公司四网投资的精细化管理,提升投资管理效益。同时,还根据大协同分析思路和端管云策略体系,达到提升中国移动的企业综合效益,促进中国移动可持续发展的目标。     

层次分析法在太湖典型区域污染物总量分配中的应用

在公平性、有效性、可持续性等原则的指导下,考虑水污染物总量分配的社会、经济、环境等整体效益,兼顾各区域间的实际差异,以及各项评价指标的相对重要性,用层次分析法构造水污染物排放总量分配的层次结构模型,把求得的各分区相对于区域允许排污总量这一总目标的权重之比,作为各分区允许排污量之比,然后按此比例在各分区间进行排污总量分配。将该方法应用于太湖流域典型区域(常州市武进区)水污染物排放总量分配。结果表明:本文提出的分配方法,可以克服等比例分配的不公平性,并且兼顾各分区间的实际差异,是一种较理想的分配方法,为制定科学合理的总量方案提供了一种新思路。     

Locally linear reconstruction for instance-based learning

Instance-based learning (IBL), so called memory-based reasoning (MBR), is a commonly used non-parametric learning algorithm. k-nearest neighbor (k-NN) learning is the most popular realization of IBL. Due to its usability and adaptability, k-NN has been successfully applied to a wide range of applications. However, in practice, one has to set important model parameters only empirically: the number of neighbors (k) and weights to those neighbors. In this paper, we propose structured ways to set these parameters, based on locally linear reconstruction (LLR). We then employed sequential minimal optimization (SMO) for solving quadratic programming step involved in LLR for classification to reduce the computational complexity. Experimental results from 11 classification and eight regression tasks were promising enough to merit further investigation: not only did LLR outperform the conventional weight allocation methods without much additional computational cost, but also LLR was found to be robust to the change of k.     

Solving a real-time allocation problem with constraint programming

In this paper, we present an original approach (CPRTA for “Constraint Programming for solving Real-Time Allocation”) based on constraint programming to solve a static allocation problem of hard real-time tasks. This problem consists in assigning periodic tasks to distributed processors in the context of fixed priority preemptive scheduling. CPRTA is built on dynamic constraint programming together with a learning method to find a feasible processor allocation under constraints. Two efficient new approaches are proposed and validated with experimental results. Moreover, CPRTA exhibits very interesting properties. It is complete (if a problem has no solution, the algorithm is able to prove it); it is non-parametric (it does not require specific tuning) thus allowing a large diversity of models to be easily considered. Finally, thanks to its capacity to explain failures, it offers attractive perspectives for guiding the architectural design process.