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蓝牙网络接入点切换问题是构建蓝牙网络必须考虑的关键问题,而蓝牙特点给接入点切换的实现带来了极大挑战。提出了一种基于灰预测模型的蓝牙网络切换算法,该算法对链路质量信息的数量和分布特征没有苛刻要求,仅用少量样本数据就可以预测出下一时间链路质量,当预测值低于阈值时,接入点搜索网络拓扑结构找出最佳接入点集合,并实时监测切换终端与集合中接入点之间链路质量,预测出下一时刻链路质量,当切换终端链路质量预测值低于另一阈值时,接入点选择最佳接入集合中预测值最佳的接入点完成切换。该算法预测过程可以动态地调整参数,实现自适应预测,提高了预测的精度。仿真结果表明,使用该算法减少了终端切换时延以及误切换概率。 相似文献
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车辆异构网络中预测垂直切换算法 总被引:3,自引:0,他引:3
在车辆异构网络中,针对垂直切换决策时刻之后网络状态的动态变化,提高切换性能问题,提出一种基于马尔可夫过程的预测垂直切换(M-VHO)算法。算法考虑了切换决策后网络状态的动态变化对车辆终端服务质量(QoS)的影响。其基本思路是:在需要垂直切换时,利用马尔可夫过程的转移概率预测未来网络状态的变化;另外,采用模糊逻辑方法确定评价属性参数权重;最后,比较切换决策、切换执行和切换之后时刻的总收益来优化选择最佳切换网络。仿真结果证明,该算法在确保较高负载均衡的情况下,可有效改进车辆终端的平均阻塞率及丢包率,降低乒乓效应,确保了车辆终端的QoS。 相似文献
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随着5G技术的广泛应用,网络超密集化部署已成为必然趋势。超密集异构无线网络在实现网络高流量密度、高峰值速率性能的同时,给传统的网络切换算法带来了挑战,处于变速移动的终端会面临更频繁的切换问题,这将导致乒乓效应频率的显著提高,进而影响用户在网体验。针对上述问题,该文提出一种基于终端移动轨迹预测的网络切换算法,适用于各类型用户在高密度无线网络中的垂直切换和水平切换问题。首先,为了更高精度的移动轨迹预测,提出一种基于模糊核聚类和长短期记忆(LSTM)神经网络的预测方法,可以有效预测不同移动模式下用户终端的短时移动轨迹;之后,基于用户当前和预测位置,获取候选网络集合,通过候选集交运算法和指标阈值判断网络切换时机;当切换触发时,使用帝企鹅算法最优化网络选择。仿真结果表明,相比于其他类型的时间序列预测算法,该文提出的轨迹预测算法精度较高;同时相较对比算法,该文所提网络切换算法的切换次数适中,有效避免了乒乓效应,且提高了用户连接的网络质量。 相似文献
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基于位置信息的IP层切换判决机制及性能分析 总被引:1,自引:0,他引:1
基于IP的移动性管理技术使得移动终端可以在互联网间漫游,并且不会改变自己的IP地址.当同时存在多种类型无线网络的时候,如何选择最佳的候选网络,何时发起切换等切换判决问题也是提高基于IP的移动性管理性能的重要课题.提出了一种基于位置信息的切换判决算法,使得终端在对自身移动模型预测的基础上,做出正确的切换目标和切换时间的选择,从理论分析和仿真结果可以看出,该算法明显减小了切换次数和丢包率,提高了切换的性能. 相似文献
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在异构5G网络中,针对切换算法稳健性差引起的切换准确率低的问题,提出一种基于区间标记判决的稳健垂直切换算法.首先,引入中位值平均滤波法,获取更准确的网络参数.其次,基于参数波动特征分析,提出区间标记判决算法,保证候选网络筛选通过率和准确率的同时,提高区间标记判决算法的稳健性.再次,根据移动终端传输层需求,结合终端运动趋势,分别使用不同权值的效用函数获得最佳目标网络.最后,仿真结果表明,该算法能够有效提升切换触发和网络筛选的准确率,降低切换失败率和乒乓效应,提高系统吞吐量,并能够根据移动终端的需求选择最佳目标网络. 相似文献
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Luís F. R. Lucas Nuno M. M. Rodrigues Carla L. Pagliari Eduardo A. B. da Silva Sérgio M. M. de Faria 《Multidimensional Systems and Signal Processing》2017,28(4):1393-1416
The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based generic image encoding solution which has been investigated earlier for the compression of stereo images with successful results. While first MMP-based proposals for stereo image coding employed dictionary-based techniques for disparity compensation, posterior developments have demonstrated the advantage of using predictive methods. In this paper, we focus on recent investigations on the use of predictive methods in the MMP algorithm and propose a new prediction framework for efficient stereo image coding. This framework comprises an advanced intra directional prediction model and a new linear predictive scheme for efficient disparity compensation. The linear prediction model is the main novelty of this work, combining adaptive linear models estimated by least-squares algorithm with fixed linear models provided by the block-matching algorithm. The performance of the proposed intra prediction and disparity compensation methods when applied in an MMP encoder has been evaluated experimentally. Comparisons with the current stereo image coding standards showed that the proposed MMP algorithm significantly outperforms the Stereo High Profile of H.264/AVC standard. In addition, it presents a competitive performance relative to the MV-HEVC standard. These results also suggest that current stereo image coding standards may benefit from the proposed linear prediction scheme for disparity compensation, as an extension to the omnipresent block-matching solution. 相似文献
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在射频识别(Radio Frequency Identification,RFID)系统中,针对EPC C1G2协议的Q算法中Q值调整的不灵活性及对空闲时隙和碰撞时隙处理上的缺点,提出了一种基于连续时隙预测的帧时隙Aloha防碰撞算法.通过马尔可夫时隙状态模型,分析不同连续时隙状态下帧长与标签数的关系,提出连续时隙预测机制和自适应散列方案.有效地减少了无效时隙的出现,实现了读取阶段的时隙多数为成功时隙.仿真结果表明,本文提出的算法能够灵活地调整帧长,有效提高吞吐率,降低传输延时和开销,为物联网(Internet of Things,IoT)的海量数据信息完整性问题提供了合理的解决方案. 相似文献
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Chow T.W.S. Wang B.-Y. Ng K.T. 《Vision, Image and Signal Processing, IEE Proceedings -》2002,149(4):225-230
An adaptive algorithm for blind identification of single-input multiple-output (SIMO) FIR systems is proposed. It is based on the one-step forward linear prediction (LP) technique and can be implemented by an RLS adaptation. Unlike most second-order statistics (SOS)-based approaches, the proposed solution does not require the computation of the correlation matrix or its inverse explicitly. The obtained results demonstrate that the proposed approach is able to deliver better performance compared with the typical batch algorithm. It is also observed that the proposed algorithm can tolerate the appearance of near common zeros among the subchannels 相似文献
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Two-dimensional orthogonal lattice filters are developed as a natural extension of the 1-D lattice parameter theory. The method offers a complete solution for the Levinson-type algorithm to compute the prediction error filter coefficients using lattice parameters from the given 2-D augmented normal equations. The proposed theory can be used for the quarter-plane and asymmetric half-plane models. Depending on the indexing scheme in the prediction region, it is shown that the final order backward prediction error may correspond to different quarter-plane models. In addition to developing the basic theory, the article includes several properties of this lattice model. Conditions for lattice model stability and an efficient method for factoring the 2-D correlation matrix are given. It is shown that the unended forward and backward prediction errors form orthogonal bases. A simple procedure for reduced complexity 2-D orthogonal lattice filters is presented. The proposed 2-D lattice method is compared with other alternative structures both in terms of conceptual background and complexity. Examples are considered for the given covariance case 相似文献
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The polycepstra and prediction equalization algorithm (POPREA) is proposed for blind equalization of nonminimum phase channels. The algorithm equalizes the amplitude and phase of the channel independently by employing linear prediction and tricepstrum principles, respectively. It guarantees convergence to a global solution. The tracking and cancellation of phase due to carrier frequency offset is carried out independently of equalization. It is demonstrated, by means of computer simulations, that the proposed POPREA is able to open the eye pattern of QAM signal constellations faster than existing polyspectra-based equalizers. The complexity of the algorithm is high but comparable to that of polyspectra equalizers 相似文献
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NO2 是主要的大气污染气体之一, 在大气光化学过程中起着重要作用。研究 NO2 浓度的时空演变, 预测其浓
度变化趋势, 对政府出台改善环境措施具有重要意义。提出利用粒子群算法 (PSO) 的反向传播 (BP) 神经网络对大气
NO2 浓度进行预测。以合肥地区 2017 年 1 月 1 日至 2019 年 12 月 31 日的大气污染数据和气象数据为基础, 结合逐步
回归方法筛选出与 NO2 浓度相关性较大的影响因子作为输入样本。构建 PSO-BP 神经网络预测模型, 利用 PSO 找出
BP 神经网络最优的初始权值和阈值。对比 BP 神经网络、遗传算法改进的 BP 神经网络和 PSO 改进的 BP 神经网络
三种模型的预测结果, 发现 PSO-BP 模型能够较为准确地预测出 NO2 浓度的动态变化规律, 并且预测精度高、模式简
单, 有望广泛应用于大气污染物浓度预测等方面的研究。 相似文献
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微电网负荷随机性强、波动大,负荷单点预测已经难以满足微电网稳定运行需要.提出一种考虑概率区间的微电网短期负荷多目标预测方法,以循环神经网络为预测模型,以逼近理想解排序策略、网格筛选策略对基本多目标人工蜂群算法进行改进,优化循环神经网络的权值和阈值,避免单目标区间预测中惩罚系数难以选择的问题,对历史负荷数据进行记忆并修正预测结果,有效提高微电网短期负荷区间预测准确性与可靠性.仿真结果表明,本文所构建的考虑概率区间的微电网短期负荷多目标预测方法,预测性能优越、结果准确,可为微电网安全经济调度提供决策依据. 相似文献
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In order to overcome the poor generalization ability and low accuracy of traditional network traffic prediction methods, a prediction method based on improved artificial bee colony (ABC) algorithm optimized error minimized extreme learning machine (EM-ELM) is proposed. EM-ELM has good generalization ability. But many useless neurons in EM-ELM have little influences on the final network output, and reduce the efficiency of the algorithm. Based on the EM-ELM, an improved ABC algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons. Network complexity is reduced. The efficiency of the algorithm is improved. The stability and convergence property of the proposed prediction method are proved. The proposed prediction method is used in the prediction of network traffic. In the simulation, the actual collected network traffic is used as the research object. Compared with other prediction methods, the simulation results show that the proposed prediction method reduces the training time of the prediction model, decreases the number of hidden layer nodes. The proposed prediction method has higher prediction accuracy and reliable performance. At the same time, the performance indicators are improved. 相似文献
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两步双向查找表预测的高光谱图像无损压缩 总被引:1,自引:1,他引:0
提出一种基于两步双向查找表预测的高光谱图像无损压缩算法。将谱段内预测和谱间预测有效地结合,去除了高光谱图像强的谱间相关性。根据高光谱图像特点,首先,在光谱线的第1谱段图像采用JPEG-LS中值预测器进行谱段内预测,其它谱段图像采用谱间预测。谱间预测采用两步双向预测算法,第1步预测,采用一种双向四阶预测器,利用该预测器得到参考预测值;第2步预测,采用一种8级查表(LUT)搜索预测算法,得出8个LUT预测值。然后,将参考预测值与其比较得出最终的预测值。最后,将预测差值进行熵编码。实验结果表明,本文算法的平均压缩比达到3.05bpp(bits per pixel),与传统高光谱图像无损压缩算法比较,平均压缩比提高了0.14~2.91bpp,有效提高了高光谱图像无损压缩比低的问题。 相似文献