一个新型的毫米波无线信号光纤传输系统及本振信号远程传送结构

摘要提出一个新型的毫米波光纤传输系统及本振信号远程传送的结构.此技术能为将来使用皮蜂窝网络提供宽带无线接入服务给出一个简单的基站接入点解决方案.计算和仿真结果表明,在激光器输出功率为－6.5 dBm,光放大增益为6 dB,激光线宽为1 MHz,75 MHz或150 MHz,误码率不超过10－9的情形下,622 Mbit/s的下行相移键控信号能够在传统单模光纤上传输超过30 km的距离.在具有相对大的激光器线宽150 MHz和光纤距离为30 km时,由激光器相位噪音和光纤色散所引起的（通过远程传送的）毫米波本振信号的额外相位误差的方差仅为1.74°.     

不稳定分层海洋湍流对厄米-高斯光通信系统的影响

推导了不稳定分层海洋湍流下厄米-高斯光束闪烁指数的理论公式,以及考虑海洋湍流和瞄准误差综合影响下UWOC系统信道系数的概率分布函数,进一步推导了系统主要性能参数即误码率、信道容量和中断概率的理论计算公式,并采用高斯-厄米正交积分近似方法求得3个性能参数的闭合表达式,仿真分析了不稳定分层和稳定分层湍流情况下,光束模数、传输距离、海洋湍流参数和瞄准误差对系统的平均误码率、平均信道容量和中断概率的影响。结果表明：相比于稳定分层,不稳定分层的系统计算误差更小;当盐度波动占主导时,系统性能更好;随着均方温度耗散率增大、湍流动能耗散率减小、瞄准误差增大,系统信道容量减小,中断概率增大。本研究结果可为厄米-高斯光在水下光通信领域的应用提供参考。     

相干光脉冲位置调制信道容量及传信率最大化研究

分析了相干光脉冲位置调制的信道容量及传信率最大化条件.通过分析光外差接收的脉冲位置调制信号具有的概率分布特性,推导出采用最大似然检测的相干脉冲位置调制信道具有的转移概率矩阵和信道容量,证明在检测器散粒噪声极限假设下,信道容量只取决于接收脉冲能量和脉冲位置调制阶数.利用Jensen不等式化简信道容量得到其下限,该下限能够近似实际通信链路的信道容量.基于此估计出达到最高传信率时的脉冲位置调制阶数同粒子数反转的重建时间之间的关系,给出了二者在一定范围内的一般映射.数值仿真表明,在接收能量使信道误码率优于10-2时,估箅出的脉冲位置调制阶数能够使传信率接近最大值.     

基于质心理念的无线传感网络路由协议的研究与改进*

在无线传感器网络中存在诸如节点能量损耗以及数据传输时所存在的时延等问题,因此在深入研究PEGASIS以及相关改进算法的基础上,并结合力学中质心的概念而提出一种基于质心理念的链式算法CMC-PEGASIS(Center of mass concept-PEGASIS)。该算法将整个传感器区域分成等宽的五个子区域,通过计算每个子区域内节点至基站的距离并按照距离与基站的远近来成链。其次引入质心的概念找到每个区域节点的能量中心,同时结合节点自身的能耗以及每个节点与基站的距离关系,得到每个子区域中的最佳的簇头节点,最后每个区域的簇头节点直接与基站完成信息的传输。经过理论分析和仿真结果得知：CMC-PEGASIS算法降低了全局能耗、减少了信息延迟率同时延长了网络的生命周期。     

一类基于随机行走机理的优化路由改进策略

在对随机行走过程的研究中发现:单个粒子通过某条特定路径的时间正比于该路径上所有节点度的连乘积.据此,文章提出基于随机行走机理的优化路由改进策略.该策略以节点度连乘积最小化为原则,通过调节可变参数,建立节点处理能力均匀分布的情况下最佳路由策略.通过分析比较不同路由策略条件下平均路由介数中心度,网络的临界负载量,平均路径长度以及平均搜索信息量等性能指标,研究结果表明,此改进路由策略在保证网络平均路径长度较少增加的前提下,使网络的传输能力获得最大幅度的提升. 关键词： 复杂网络 路由策略 负载传输     

无背景噪声时光多输入多输出系统的信道容量

信道容量是通信系统能够达到的最大传输速率,是衡量通信系统通信能力的重要指标之一。在建立了光多输入多输出(MIMO)的信道模型之后,结合脉冲位置调制(PPM)分析了光MIMO系统的平均容量和中断容量;在无背景辐射的条件下,分别推导出了有无衰落时光MIMO平均容量封闭形式的近似表达式。在相同发射功率和传输带宽条件下,得到了光MIMO技术能成倍提高现有系统信道容量的结论。通过Monte Carlo仿真实验进一步证明了理论的正确性。     

医院网环境数据中心机房网络设计

为了确保医院环境下数据中心机房网络的安全、稳定的运行,需要对数据中心机房网络进行优化设计;采用当前方法进行数据中心机房网络设计时,未考虑到数据中心机房网络路径传输能耗、路径传输距离等影响,会造成网络寿命减少,影响数据中心机房网络的生存时间;为此,提出一种基于数据传输速度优化的医院网环境数据中心机房网络设计方法;该方法对数据中心机房网络供配电系统、机房监控系统和VPN虚拟系统等三大子系统进行设计优化,在医疗数据传输过程中,考虑医院网络节点能量优化理论对数据节点进行分簇,以需要传输的医疗数据节点为初始节点,汇聚节点作为医疗数据接收目标节点,采用蚁群优化方法对数据中心机房网络进行多路径搜索,搜索过程中考虑了数据中心机房网络路径传输能耗、路径传输距离以及医院网带宽等因素,最终选择满足多种条件的数据中心机房网络数据传输最优路径,完成医院网环境数据中心机房网络设计;实验结果表明,所提方法设计的医院网环境下数据中心机房网络具有耗能低、扩展灵活、周期短等优点。     

大气湍流中部分相干光束上行和下行传输偏振特性的比较

以部分相干的电磁高斯-谢尔模型(electromagnetic Gaussian-Schell model, EGSM) 光束为研究对象, 根据相干和偏振的统一理论以及随机光束的Stokes参量, 推导出EGSM光束在大气湍流中斜程传输时的偏振度(degree of polarization, DoP)和偏振方向角的表达式, 研究了大气湍流中上行和下行传输时EGSM光束偏振特性的不同. 研究结果表明: 在相同条件下, EGSM 光束下行传输时整个光场DoP的分布比上行传输要集中; 下行传输时轴上点的DoP达到最大值所对应的传输距离长于上行传输. 可以看出, EGSM光束沿下行路径传输时, 探测器可以接收更远距离处的波束传输信息.     

部分相干涡旋光束通过大气湍流上行和下行传输的比较研究

基于广义惠更斯-菲涅耳原理,以高斯-谢尔模型(GSM)涡旋光束作为典型的部分相干涡旋光束,推导出GSM涡旋光束通过大气湍流斜程传输的平均光强、均方根束宽和交叉谱密度函数的解析表达式,并用以研究了大气湍流中上行和下行对GSM涡旋光束传输和对相干涡旋的影响.结果表明,在相同条件下,GSM涡旋光束下行传输受大气湍流的影响要小于上行传输,下行传输时相干涡旋拓扑电荷守恒距离要长于上行传输.对所得结果做了物理解释. 关键词： 部分相干涡旋光束 相干涡旋 大气湍流 上行和下行传输     

一种面向监测区域的链路质量和覆盖保证的节点调度算法

为了降低监测区域能耗总开销和减少网络传输时延,保证监测区域网络链路质量、实现网络的全面覆盖和延长网络生命周期,设计了一种基于扫描线和节点自适应调整苏醒时隙的节点调度方案。首先,定义了系统模型即网络假设和调度目标;然后判断网络是否实现当完全覆盖,当不能全面覆盖时,通过调整部分节点的感知半径来实现网络的全面覆盖;当链路质量过差导致传输延迟过大时,通过设计从源节点到目标节点的增加节点苏醒时隙,并根据节点的剩余能量和传输延迟阈值来减少数据传输次数以降低传输延迟。在NS2环境下进行实验,结果表明：文中方法能有效地实现传感器网络监测区域的全面覆盖,降低网络的传输延迟和提高网络的生命周期,与其他节点调度相比,具有很强的优越性和实用性。     

Scheduling Strategy Design Framework for Cyber–Physical System with Non-Negligible Propagation Delay

Cyber–physical systems (CPS) have been widely employed as wireless control networks. There is a special type of CPS which is developed from the wireless networked control systems (WNCS). They usually include two communication links: Uplink transmission and downlink transmission. Those two links form a closed-loop. When such CPS are deployed for time-sensitive applications such as remote control, the uplink and downlink propagation delay are non-negligible. However, existing studies on CPS/WNCS usually ignore the propagation delay of the uplink and downlink channels. In order to achieve the best balance between uplink and downlink transmissions under such circumstances, we propose a heuristic framework to obtain the optimal scheduling strategy that can minimize the long-term average control cost. We model the optimization problem as a Markov decision process (MDP), and then give the sufficient conditions for the existence of the optimal scheduling strategy. We propose the semi-predictive framework to eliminate the impact of the coupling characteristic between the uplink and downlink data packets. Then we obtain the lookup table-based optimal offline strategy and the neural network-based suboptimal online strategy. Numerical simulation shows that the scheduling strategies obtained by this framework can bring significant performance improvements over the existing strategies.     

An energy efficient scheme by exploiting multi-hop D2D communications for 5G networks

The multi-hop Device-to-Device (M-D2D) communication has a potential to serve as a promising technology for upcoming 5G networks. The prominent reason is that the M-D2D communication has the potential to improve coverage, enhanced spectrum efficiency, better link quality, and energy-efficient communication. One of the major challenges for M-D2D communication is the mitigation of interference between the cellular user (CUs) and M-D2D users. Considering this mutual interference constraint, this work investigates the problem of optimal matching of M-D2D links and CUs to form spectrum-sharing partners to maximize overall sum rates of the cell under QoS and energy efficiency (EE) constraints. In this paper, we investigate the interference management for multi-hop (more than one-hop) D2D communication scenarios where we propose a channel assignment scheme along with a power allocation scheme. The proposed channel assignment scheme is based on the Hungarian method in which the channel assignment for M-D2D pairs is done by minimum interference value. The power allocation scheme is based on Binary Particle swarm optimization (BPSO). This scheme calculates the specific power values for all the individual M-D2D links. We have done a comprehensive simulation and the result portrays that our proposed scheme performs better compared to the previous work mentioned in the literature. The results clearly indicate that the proposed scheme enhances the EE of up to 13% by producing the optimal assignment of channels and power for the CUs and M-D2D users.     

A matching-theoretic approach to resource allocation in D2D-enabled downlink NOMA cellular networks

This paper investigates the resource allocation problem in non-orthogonal multiple-access (NOMA) cellular networks underlaid with OMA-based device-to-device (D2D) communication. This network architecture enjoys the intrinsic features of NOMA and D2D communications; namely, spectral efficiency, massive connectivity, and low-latency. Despite these indispensable features, the combination of NOMA and D2D communications exacerbates the resource allocation problem in cellular networks due to the tight coupling among their constraints and conflict over access to shared resources. The aim of our work is to maximize the downlink network sum-rate, while meeting the minimum rate requirements of the cellular tier and underlay D2D communication, and incorporating interference management as well as other practical constraints. To this end, many-to-many matching and difference-of-convex programming are employed to develop a holistic sub-channels and power allocation algorithmic solution. In addition to analyzing the properties of the proposed solution, its performance is benchmarked against an existing solution and the traditional OMA-based algorithm. The proposed solution demonstrates superiority in terms of network sum-rate, users’ connectivity, minimum rate satisfaction, fairness, and interference management, while maintaining acceptable computational complexity.     

Synchronization optimal networks obtained using local structure information

In this paper, the networks with optimal synchronizability are obtained using the local structure information. In scale-free networks, a node will be coupled by its neighbors with maximal degree among the neighbors if and only if the maximal degree is larger than its own degree. If the obtained coupled networks are connected, they are synchronization optimal networks. The connection probability of coupled networks is greatly affected by the average degree which usually increases with the average degree. This method could be further generalized by taking into account the degree of next-nearest neighbors, which will sharply increase the connection probability. Compared to the other proposed methods that obtain synchronization optimal networks, our method uses only local structure information and can hold the structure properties of the original scale-free networks to some extent. Our method may present a useful way to manipulate the synchronizability of real-world scale-free networks.     

Joint uplink–downlink resource allocation for energy efficient D2D underlaying cellular networks with many-to-one matching

Energy efficiency (EE) is an important parameter for the next generation cellular communications which is not limited to voice and text messages only. Device-to-Device (D2D) communication is being viewed as a promising technology to support heterogeneous applications involved in future cellular networks. Due to its short range communication, less amount of power is sufficient to make a successful transmission. By exploiting this feature of D2D, this paper proposes an energy-efficient resource allocation scheme for joint uplink/downlink (UL/DL) D2D considering many-to-one matching criterion for channel reuse among users. In this paper, total EE of D2D pairs (DPs) is taken as a performance metric to be optimized subject to quality of service (QoS) satisfaction for cellular users (CUs) within the power budgets of all the users. An iterative scheme is designed for joint channel and power optimization problem. Simulation results show the convergence of joint iterative algorithm and verify significant performance improvement over other schemes.     

Deep Q-learning based optimal resource allocation method for energy harvested cognitive radio networks

In this article, we propose a deep Q-learning based algorithm for optimal resource allocation in energy harvested cognitive radio networks (EH-CRN). In EH-CRN, channel resources of primary users (PU) networks are shared with secondary users (SU) and energy harvesting allows nodes of the CRN to acquire energy from the environment for operation sustainability. However, amount of energy harvested from the environment is not fixed and requires dynamic allocation of resources for obtaining optimum network and throughput capacity. In this work, we overcome the limitations of existing Q-learning based resource allocation schemes which are constrained by large state-space systems and have slow convergence. Proposed deep Q-learning based algorithm improves the resource allocation in EH-CRN, while considering quality of service (QoS), energy and interference constraints. Simulation results show that proposed algorithm provide improved convergence and better resource utilization compared to other techniques in literature.     

Performance enhancement in the cell-free massive MIMO SWIPT system with active eavesdropping

This paper investigates the secure transmission for simultaneous wireless information and power transfer (SWIPT) in the cell-free massive multiple-input multiple-output (MIMO) system. To develop green communication, legitimate users harvest energy by the hybrid time switching (TS) and power splitting (PS) strategy in the downlink phase, and the harvested energy can provide power to send uplink pilot sequences for the next time slot. By in-built batteries, the active eavesdropper can send the same pilots with the wiretapped user, which results in undesirable correlations between the channel estimates. Under these scenarios, we derive the closed-form expressions of average harvested energy and achievable rates, and propose an iterative power control (PC) scheme based on max–min fairness algorithm with energy and secrecy constraints (MMF-ESC). This scheme can ensure the uniform good services for all users preserving the distributed architecture advantage of cell-free networks, while meeting the requirements of energy harvested by legitimate users and network security against active eavesdroppers. Besides, continuous approximation, bisection and path tracking are jointly applied to cope with the high-complexity and non-convex optimization. Numerical results demonstrate that MMF-ESC PC scheme can effectively increase the achievable rate and the average harvested energy of each user, and decrease the eavesdropping rate below the threshold. Moreover, the results also reveal that PS strategy is superior in harvesting energy in terms of more stringent network requirements for average achievable rates or security.     

Optimal network structure to induce the maximal small-world effect

In this paper, the general efficiency, which is the average of the global efficiency and the local efficiency, is defined to measure the communication efficiency of a network. The increasing ratio of the general efficiency of a small-world network relative to that of the corresponding regular network is used to measure the small-world effect quantitatively. The more considerable the small-world effect, the higher the general efficiency of a network with a certain cost is. It is shown that the small-world effect increases monotonically with the increase of the vertex number. The optimal rewiring probability to induce the best small-world effect is approximately 0.02 and the optimal average connection probability decreases monotonically with the increase of the vertex number. Therefore, the optimal network structure to induce the maximal small-world effect is the structure with the large vertex number （〉 500）, the small rewiring probability （≈0.02） and the small average connection probability （〈 0.1）. Many previous research results support our results.     

A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks

One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in a tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network, but most of the proposed methods are computationally demanding, which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that THESIS significantly reduces the energy consumption of the UDHN and can reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.     

上下行传输孔径接收光强起伏特性分析

为了研究地星上行和星地下行激光链路孔径内接收光强信号的典型特征,基于大气湍流理论和位相屏方法,计算了短波和中波红外激光在特定大气相干长度条件下,孔径内接收激光功率与总功率的比值及起伏情况。根据统计结果讨论了上下行大气通道传播特性的差异,结果表明相同外界条件下,直径50 cm孔径内接收的下行激光信号强度大于上行激光信号强度1个数量级以上,其信号的起伏程度也低于上行激光;中等湍流下,下行激光孔径接收光强的概率分布函数服从对数正态分布,最大概率接收功率比与无湍流条件下的功率比值一致,分别为0.42%(1.315 m)和 0.26%(3.8 m)。