一种基于有限信道的能量高效节点调度机制

在低负载、低功耗无线传感器网络中,节点状态切换的能量消耗因为用于数据传输的能量较小而变得不可忽略。针对此问题,提出了结合多信道技术与时分多路访问( TDMA)技术的节点调度算法。该算法设计了基于接收端的连续时隙分配策略以减少节点状态切换次数,并且在可用无线信道有限的约束条件下,提出了信道分配与时隙调整机制,实现了时隙重用并最小化有限信道约束对优化节点状态切换次数的影响。仿真实验结果表明,当可用无线信道数为3~5时,算法能够有效地改善节点能量效率。当可用无线信道数大于3之后,算法能够获得优化的数据汇聚时间。     

面向5 G无线通信系统的关键技术综述

对未来无线通信系统的几种潜在的关键通信技术,如异构网络、大规模多输入多输出(MIMO)通信、绿色通信和毫米波通信,给出了较详尽的论述与讨论。首先简要介绍了未来无线通信网络结构的异构化变化所引起的复杂干扰信号的出现及能源消耗增加问题。介绍了大规模MIMO通信技术的优点,大规模MIMO通信的研究现状与研究难点。详尽叙述了分别以频谱效率、能源效率和资源效率最大化为目标的绿色无线通信传输优化问题的解决方案。最后,给出了进一步解决频谱稀缺问题的毫米波无线通信系统的混合波束成形的方案。     

传感器网络基于动态密钥池的密钥建立方案

针对网络在整个生命周期内的可持续安全性问题,基于动态密钥池技术提出了一个新的对密钥建立方案(KES-D).在此方案中,节点分n次部署,密钥池根据节点部署的次数分代.第i代密钥池由第i-1阶段的密钥池去除m条使用时间最长的二维反向密钥链再加上m条新二维反向密钥链组成.理论分析及模拟结果表明:与适用于多次部署的传感器网络密钥管理方案相比,该方案的抗毁密钥连通率显著提高.     

基于簇头分级的改进非均匀分簇算法

针对无线传感器网络非均匀成簇路由中频繁的簇头轮换带来的簇内以及簇间广播开销对传感器网络生存周期的缩短,提出了一种基于簇头分级的改进的非均匀成簇算法（CHCI）,利用簇内节点能量构建了节点的分级模型,将节点分为主要簇头（PCH）,次要簇头（SCH）及簇内成员节点（CM）,为PCH设置了重选因子。结合二次规划问题为SCH选择了最佳中继路径降低节点能耗,延长PCH的重选时间。仿真结果表明,CHCI算法比经典LEACH算法以及非均匀成簇的EEUC算法,延长了网络的生存时间。     

基于观测器的空间机器人神经自适应鲁棒控制

研究无速度反馈的不确定性自由漂浮空间机器人轨迹跟踪控制问题,为了提高控制性能,采用一种观测器神经网络自适应鲁棒控制方法。利用神经网络设计观测器对系统的速度信息进行估计;其次,利用神经网络对系统模型的非线性进行逼近,设计神经网络自适应鲁棒控制器对系统进行控制,无需建立复杂的数学模型,并且所设计的自适应律能够进行在线学习;最后,根据H∞理论设计的鲁棒控制器保证了系统稳定,并使系统的L2增益小于给定指标。仿真结果表明,上述方法在无速度信息反馈下仍能进行有效的跟踪控制。     

基于免疫克隆选择机制的WSN节点调度算法

由于无线传感器网络节点分布不均匀,监测环境复杂等特点,远离Sink的节点由于能耗较大,并且容易导致网络覆盖面积不足.提出一种启发式的利用人工免疫克隆选择机制的节点调度优化算法(AICSO),将网络生命期划分为具体数量的迭代周期并生成中心节点的覆盖位图,利用节点间冗余进行有效地拓扑控制合理调度节点,以获得网络的最优连通性和最大面积的覆盖.仿真结果表明,上述算法能够有效利用网络节点的能量满足感知覆盖和连通性要求,延长了网络生命周期,降低了网络整体能耗,为网络优化节点调试提供了依据.     

High‐precision formation control of nonlinear multi‐agent systems with switching topologies: A learning approach

Arbitrary high precision is considered one of the most desirable control objectives in the relative formation for many networked industrial applications, such as flying spacecrafts and mobile robots. The main purpose of this paper is to present design guidelines of applying the iterative schemes to develop distributed formation algorithms in order to achieve this control objective. If certain conditions are met, then the control input signals can be learned by the developed algorithms to accomplish the desired formations with arbitrary high precision. The systems under consideration are a class of multi‐agent systems under directed networks with switching topologies. The agents have discrete‐time affine nonlinear dynamics, but their state functions do not need to be identical. It is shown that the learning processes resulting from the relative output formation of multi‐agent systems can converge exponentially fast with the increase of the iteration number. In particular, this work induces a distributed algorithm that can simultaneously achieve the desired relative output formation between agents and regulate the movement of multi‐agent formations as desired along the time axis. The illustrative numerical simulations are finally performed to demonstrate the effectiveness and performance of the proposed distributed formation algorithms. Copyright © 2014 John Wiley & Sons, Ltd.     

Novel neural network modeling method and applications

Neural networks play an important role for designing the parametric model of electromagnetic structures. The current neural network methods are unfit for a circuit model with many input variables because it is costly to extract a large number of the training data and test data to complete the highly nonlinear mapping approximation. This article proposes a new neural network modeling method—the multidimensional neural network model, which can be used to solve the issue of multivariable radiofrequency and microwave passive device modeling. The entire multidimensional neural network modeling problem is simplified into a set of neural network submodels through decomposition method. Then the submodels are combined into an equivalent model, and the final entire model is produced through the neural‐network mapping model developed with the submodels and equivalent model. A microstrip hairpin filter model is developed using the proposed method. The simulation results show the correctness and the effectivity of the proposed method. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:769–779, 2015.     

Recurrent neural network technique for behavioral modeling of power amplifier with memory effects

A new technique for behavioral modeling of power amplifier (PA) with short‐ and long‐term memory effects is presented here using recurrent neural networks (RNNs). RNN can be trained directly with only the input–output data without having to know the internal details of the circuit. The trained models can reflect the behavior of nonlinear circuits. In our proposed technique, we extract slow‐changing signals from the inputs and outputs of the PA and use these signals as extra inputs of RNN model to effectively represent long‐term memory effects. The methodology using the proposed RNN for modeling short‐term and long‐term memory effects is discussed. Examples of behavioral modeling of PAs with short‐ and long‐term memory using both the existing dynamic neural networks and the proposed RNNs techniques are shown. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:289–298, 2015.     

Modeling of microstrip antennas using neural networks techniques: A review

Artificial neural networks modeling have recently acquired enormous importance in microwave community especially in analyzing and synthesizing of microstrip antennas (MSAs) due to their generalization and adaptability features. A trained neural model estimates response very fast, which is nearly equal to its measured and/or simulated counterpart. Thus, it completely bypasses the repetitive use of conventional models as these models need rediscretization for every minor changes in the geometry, which itself is a time‐consuming exercise. The purpose of this article is to review this emerging area comprehensively for both analyzing and synthesizing of the MSAs. During reviewing process, some untouched cases are also observed, which are essentially required to be resolved for antenna designers. Unique and efficient neural networks‐based solutions are suggested for these cases. The proposed neural approaches are validated by fabricating and characterizing of the prototypes too. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:747–757, 2015.