Joint optimization of spectral and energy efficiency for multi-pair full-duplex two-way relay networks with imperfect channel state information

An iterative algorithm is proposed for jointly optimizing spectral and energy efficiency in a multipair full-duplex (FD) two-way relaying (TWR) system with imperfect channel state information (CSI). Based on Dinkelbach method, a Taylor expansion based approximation method and the Generalized Lagrange Multiplier Method have been applied iteratively to obtain the near optimal relay amplified matrix and power allocation, respectively. And the simulation results illustrate the effectiveness of the proposed algorithm and the algorithm can converge quickly.     

安全双向中继通信中友好干扰节点的功率分配

研究了具有一组友好干扰节点辅助的双向中继网络中的安全传输问题,考虑了中继节点不可信为窃听节点和中继节点可信而存在其他窃听节点的两种情况下,友好干扰节点的功率分配问题.目的是设计友好干扰的最优功率分配方案,在友好干扰节点的功率消耗最小的情况下,达到双向中继系统的通信质量和通信安全性要求.本文为两种情形下的友好干扰节点功率分配问题分别建立了最优化模型,并证明了两个问题均属于线性规划问题.仿真结果表明,本文所设计友好干扰节点功率分配方案,能够在保证双向中继系统的通信质量和通信安全性前提下,使得友好干扰节点的总功率最小.     

大规模路网中分布式车辆群体协同决策方法

为解决车路协同环境下大规模路网中车辆群体协同决策问题,提出了分布式车辆群体协同决策方法;在深入分析交通控制特性的基础上,构建了路网分解模型,将大规模协同决策问题分解成若干个同质小规模子问题,每个子问题覆盖了上游路段、路口和下游路段这3类不同交通区域;基于虚拟车辆映射技术构建了车辆群体协同决策模型,将路口区域二维车辆群体协同决策问题转化为一维问题;与路段区域内车辆群体协同决策方式相同,在路口区域内通过控制虚拟车队中车辆的等效车头时距来完成车辆之间的交互和冲突消解,进而采用统一的协同决策参数来解决各子问题中不同区域内车辆群体的协同决策问题;基于不同区域内车辆群体协同决策参数的统一化,设计了上、下游区域之间的协作机制来保证上游车辆在充分考虑下游交通状态的基础上做出合适的驾驶决策。仿真结果表明：在不同的交通需求设置下,采用提出的方法后,车辆在通过冲突区的过程中均具有平滑的时空轨迹,避免了车辆时空轨迹出现剧烈波动;相对于纯分布式方法,提出的方法在给定的仿真条件下可使车辆燃油消耗最大降低14%;因此,在大规模路网中实施提出的分布式车辆群体协同决策方法可有效降低冲突区对车流连续性的影响,从而保证了车辆安全、平稳、环保地行驶。     

Performance analysis of single relay selection in cooperative communication over Nakagami-m fading channels

We derive closed-form expressions for outage probability and symbol error rate(SER) of opportunistic relaying(OR) and selection cooperation(SC).For the amplify-and-forward relay selection scheme,comparison between two selection strategies over independent non-identically distributed Nakagami-m fading channels via analytical and simulation results is given in terms of the outage probability and SER.Simulation results show that OR performs better than SC.     

场面航空器滑行时空协同优化模型

引入双层规划方法, 研究了场面航空器在滑行道系统中的滑行调度问题; 考虑了成本与冲突对场面航空器运行效率和安全的影响, 以航空器推出延迟时间与滑行路径作为决策变量, 以航空器在滑行道系统中滑行过程无冲突与场面航空器的总滑行距离最短为目标函数, 构建了场面航空器滑行时空协同优化模型; 针对航空器滑行道调度问题的特点, 设计了适用于航空器滑行时空协同优化模型的双层规划算法, 以降低场面航空器滑行距离和等待时间; 为了验证航空器滑行时空协同优化模型及算法的有效性, 对比了先到先服务调度方案的计算结果, 分析了滑行等待时间与滑行距离对场面航空器运行效率的影响。研究结果表明: 场面航空器滑行时空协同优化模型与先到先服务的航空器调度方案相比, 保证了航空器滑行过程无冲突, 将16架次航空器的总滑行距离从40 690 m降至37 700 m, 降低了8%;航空器平均运行时间为254 s, 提升了滑行道系统的整体运行效率; 在复制组数为100与变异概率为0.4的条件下, 采用场面航空器滑行时空协同优化模型能够在412 s内获得最优解, 求解效率与收敛性显著。可见, 采用场面航空器时空协同优化模型在保障航空器滑行安全的前提下, 能有效提高场面航空器滑行调度效率, 降低航空器运行成本, 能够为繁忙机场滑行道调度提供决策支持。     

轨道交通中异构智融车载网络发展综述

从蜂窝无线接入技术、非蜂窝无线接入技术、异构智融车载网络接入技术三方面,分析了国内外轨道交通车载网络的研究现状;针对非蜂窝无线接入技术和蜂窝无线接入技术的问题,阐述了协同利用轨道交通周边异构无线资源进行网络融合、协同通信的优越性;从网络模型、网络架构两方面论述了异构智融车载网络的融合方案;结合智能轨道交通业务需求,从可靠性和资源利用率两方面对现有的异构智融车载网络研究进行了系统性的归类梳理;从人工智能、安全性和云边结合三方面提出未来异构智融车载网络的发展趋势。研究结果表明：异构智融车载网络可靠性分为网络架构的可靠性和数据传输的可靠性,其中在网络架构可靠性方面,主要研究了通过冗余网络架构、车云传输架构、软件定义网络构架和智慧协同网络架构4种方式提升可靠性,在数据传输可靠性方面,主要研究了通过多路径传输、网络编码和切换算法降低传输过程中的丢包率;异构智融车载网络资源利用率分为无线接入的资源利用率和链路调度的资源利用率,其中在无线接入资源利用率方面,主要通过信道状态预测、频谱划分、频移补偿3种方式增加网络吞吐量,提高资源利用率,在链路调度的资源利用率方面,主要通过调度算法、接收缓存算法和拥塞控制算法来减少异构链路对数据传输的影响,降低数据重传次数,提高网络资源利用率。     

Network lifetime global optimization for multi-source and single-sink topology in wireless sensor networks

The multi-source and single-sink (MSSS) topology in wireless sensor networks (WSNs) is defined as a network topology, where all of nodes can gather, receive and transmit data to the sink. In energy-constrained WSNs with such a topology, the joint optimal design in the physical, medium access control (MAC) and network layers is considered for network lifetime maximization (NLM). The problem of integrating multi-layer information to compute NLM, which involves routing flow, link schedule and transmission power, is formulated as a non-linear optimization problem. Specially under time division multiple access (TDMA) scheme, this problem can be transformed into a convex optimization problem. To solve it analytically we make use of the property that local optimization is global optimization in convex problem. This allows us to exploit the Karush-Kuhn-Tucker (KKT) optimality conditions to solve it and obtain analytical solution expression, i.e., the globally optimal network lifetime (NL). NL is derived as a function of number of nodes, their initial energy and data rate arrived at them.Based on the analysis of analytical approach, it takes the influence of data rates, link access and routing method over NLM into account. Moreover, the globally optimal transmission schemes are achieved by solution set during analytical approach and applied to algorithms in TDMA-based WSNs aiming at NLM on OMNeT<++> to compare with other suboptimal schemes.     

无人售票票款流失分析

无人售票作为公交的一种主要运营模式,目前在全国大部分城市都已实施多年.无人售票减少了企业运营成本,规范了公交运营秩序,但也暴露出一些问题,给公交企业带来了不少烦恼,这其中主要是票款流失现象.票款流失现象严重制约了公交企业效益的提高.     

车路协同环境下车辆群体协同决策研究综述

从车路协同环境下车辆群体协同决策机制、协同决策方法与典型应用场景方面分析了国内外车辆群体协同决策的研究现状;考虑车辆群体协同决策机制的不同,系统梳理了集中式和分布式2种决策机制的相关研究;针对车辆群体协同决策方法的多样性,以基于优化和基于启发式2类决策方法为主线,对比分析了不同决策方法的优劣;考虑车辆群体协同决策应用场景的不同,全面分析了匝道、路口、路段和路网等多个应用场景下车辆群体协同决策的相关理论与研究;考虑国内外车辆协同决策典型项目进展,分别梳理了中国、美国、日本和欧洲代表性车辆群体协同决策项目任务、建设与实施情况;从系统结构、普适模型和示范场景3个方面提出了未来车路协同环境下车辆群体协同决策的发展趋势。研究结果表明：集中式车辆群体协同决策机制有助于提升局部区域内的车辆通行性能,分布式车辆群体协同决策机制有助于提升全局范围内的交通运行状态;基于优化的车辆群体协同决策方法在特定场景下可最大程度提升决策效果,基于启发式的车辆群体协同决策方法在大多数场景下可获得可行的决策效果;由于不同场景下车辆群体协同决策问题的复杂性有所不同,需要在统一框架下做针对性建模。研究结果可为车路协同环境下新型混合交通系统的管理与控制提供参考。     

车路协同环境下群体车辆诱导与协同运行方法

为解决城市发展带来的交通拥堵问题,发掘道路交通的潜力,提高车路协同环境下车辆在路网中的行驶效率,面向群体车辆提出了一种诱导优化方法和协同控制策略;在车辆诱导分配方面,在起始点和目的地之间的可达路径中,以交通效率最优、车辆排放最小为目标,设计了基于道路饱和度、车辆行程时间和延误的群体车辆分配规则,建立了群体车辆诱导分配优化模型,并用多目标非支配排序遗传算法-Ⅱ(NSGA-Ⅱ)和多目标粒子群优化算法进行求解;在车辆协同运行控制策略方面,基于引力场思想建立了多车协同运行模型,并提出了多车协同加减速策略;通过仿真验证比较了不同网联自动驾驶车辆(CAV)渗透率下的车辆诱导优化结果,同时仿真了车辆协同加减速策略,并将诱导优化方法和协同控制策略进行了联合仿真。仿真结果表明：多目标诱导分配方法可以提升车辆速度和环境效益,且群体车辆平均速度与CAV渗透率正相关;在四车组队行驶环境中,车辆协同加减速策略能够将车辆在加速和减速时的初始平均加速度分别提高15.0%和8.2%,让车辆快速达到目标速度,保障行车安全;在联合仿真环境中,路网群体车辆的加速度平均提高了11.6%,速度平均提高了1.6%,碳氧化合物排放量减少约4.9%。由此可见,提出的方法能够提高路网通行效率,降低车辆能源消耗,减少对环境造成的不良影响。     

Optimization for PID controller of cryogenic ground support equipment based on cooperative random learning particle swarm optimization

Cryogenic ground support equipment (CGSE) is an important part of a famous particle physics experiment — AMS-02. In this paper a design method which optimizes PID parameters of CGSE control system via the particle swarm optimization (PSO) algorithm is presented. Firstly, an improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. Secondly, the way of finding PID coefficient will be studied by using this algorithm. Finally, the experimental results and practical works demonstrate that the CRPSO-PID controller achieves a good performance.     

Outage performance of amplify-and-forward multiple-input multiple-output relay networks with outdated channel state information and co-channel interference

In this paper, we investigate the outage performance of a dual-hop amplify-and-forward(AF) multipleinput multiple-output(MIMO) relay network, considering beamforming transmission with outdated channel state information(CSI) and an arbitrary number of co-channel interferers at both the relay and the destination. The exact closed-form expression for the outage probability is derived, and the asymptotic behavior of the outage probability is also presented to provide further insights into the system performance. Our results show that the system suffers a diversity gain loss caused by the outdated CSI, and the interference only degrade the coding gain of the system. Moreover, increasing antennas can achieve more performance gains, however, the gains decrease as the delay of the CSI increases.     

航空收益管理中多航段舱位控制模型

为了优化多航段舱位控制, 提高航空客运收益, 将旅客始-终点流按票价由高到低排序, 定义旅客始-终点流为决策变量, 以各始-终点流需求为约束条件, 以航线期望总收益为目标函数, 建立了多航段舱位控制问题的随机规划模型, 设计了模型求解遗传算法。算例分析结果表明, 收益计算结果最大误差为0.34%, 结果稳定, 模型可行。     

税收筹划在企业实践中的应用

论述了如何利用国家税法的有关条款进行税收筹划 ,合理、合法地实现企业税负的最小化 ,以提高企业经济效益和市场竞争力     

Price-based power control algorithm in cognitive radio networks based on monotone optimization

This paper considers a price-based power control problem in the cognitive radio networks(CRNs).The primary user(PU) can admit secondary users(SUs) to access if their interference powers are all under the interference power constraint. In order to access the spectrum, the SUs need to pay for their interference power.The PU first decides the price for each SU to maximize its revenue. Then, each SU controls its transmit power to maximize its revenue based on a non-cooperative game. The interaction between the PU and the SUs is modeled as a Stackelberg game. Using the backward induction, a revenue function of the PU is expressed as a non-convex function of the transmit power of the SUs. To find the optimal price for the PU, we rewrite the revenue maximization problem of the PU as a monotone optimization by variable substitution. Based on the monotone optimization, a novel price-based power control algorithm is proposed. Simulation results show the convergence and the effectiveness of the proposed algorithm compared to the non-uniform pricing algorithm.     

Joint Tomlinson-Harashima source and linear relay precoder design in amplify-and-forward multiple-input multiple-output two-way relay systems

Existing minimum-mean-squared-error (MMSE) transceiver designs in amplified-and-forward (AF) multiple-input multiple-output (MIMO) two-way relay systems all assume a linear precoder at the sources. Nonlinear source precoders in such a system have not been considered yet. In this paper, we study the joint design of source Tomlinson-Harashima precoders (THPs), relay linear precoder and MMSE receivers in two-way relay systems. This joint design problem is a highly nonconvex optimization problem. By dividing the original problem into three sub-problems, we propose an iterative algorithm to optimize precoders and receivers. The convergence of the algorithm is ensured since the updated solution is optimal to each sub-problem. Numerical simulation results show that the proposed iterative algorithm outperforms other algorithms in the high signal-to-noise ratio (SNR) region.     

Stability Analysis for Stochastic Optimization Problems

Stochastic optimization offers a means of considering the objectives and constrains with stochastic parameters. However, it is generally difficult to solve the stochastic optimization problem by employing conventional methods for nonlinear programming when the number of random variables involved is very large. Neural network models and algorithms were applied to solve the stochastic optimization problem on the basis of the stability theory. Stability for stochastic programs was discussed. If random vector sequence converges to the random vector in the original problem in distribution, the optimal value of the corresponding approximation problems converges to the optimal value of the original stochastic optimization problem.     

Research and implementation of cooperative cache for PVFS

At present, there are many effective ways to achieve high performance in cluster system storage management, including server-end disk, server-end caching, local caching and cooperative caching. The cooperative caching mechanism shares caches among different clients so as to avoid expensive disk access costs and to improve overall throughput of cluster system. In this paper, a Single Copy Cooperative Cache model is proposed together with block lookup algorithm, block replacement algorithm and the consistency algorithm based on the model. Meanwhile, the prototype system of the model is implemented in PVFS file system. Finally, the performance of this system is tested in InfiniBand Framework, the result of which shows that in contrast to the original PVFS system, read performance of PVFS file system is improved by about two times, while write performance is reduced by nearly ten percent.     

几种常见分布的随机不等式的确定性等价式

讨论了将随机线性不等式组转化为相应的确定性不等式组的问题．对含有几种常见分布的随机变量的随机线性不等式组，就其左端系数矩阵和右端向量含有随机变量的不同情形分别进行了讨论，给出了相应的确定性不等式组．     

随机交通网络连通可靠度改进算法

连通可靠度作为网络可靠性的基础指标是指导交通事故预防、灾后重建和日 常维护等活动的重要理论,但其计算是经典的NP难问题.为了提高大规模网络应用的求 解精度和效率,提出了基于k-最短路径和状态排序的改进算法--Target_Order 算法,集 中考察影响网络连通性的关键节点及其状态,有效减少了无关网络连通性的节点组合产 生的冗余网络状态,大幅降低了计算复杂度.最后,以成都规划年地铁网为例,通过与传统 算法（ORDER算法）比较,分析了算法关键参数的影响,验证了改进算法在精度与效率方 面的显著优势.研究结果同样适用于其他随机交通网络的连通可靠度计算与统计.