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Reinforcement Learning for Minimizing Age of Information under Realistic Physical Dynamics 真实物理动态下最小化信息年龄的强化学习
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9322139
Sihua Wang, Mingzhe Chen, W. Saad, Changchuan Yin, Shuguang Cui, H. Poor
In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied. In particular, each IoT device monitors a physical process that follows nonlinear dynamics. As the dynamic of the physical process varies over time, each device must sample the real-time status of the physical system and send the status information to a base station (BS) so as to monitor the physical process. The dynamics of the realistic physical process will influence the sampling frequency and status update scheme of each device. In particular, as the physical process varies rapidly, the sampling frequency of each device must be increased to capture these physical dynamics. Meanwhile, changes in the sampling frequency will also impact the energy usage of the device. Thus, it is necessary to determine a subset of devices to sample the physical process at each time slot so as to accurately monitor the dynamics of the physical process using minimum energy. This problem is formulated as an optimization problem whose goal is to minimize the weighted sum of AoI and total device energy consumption. To solve this problem, a machine learning framework based on the repeated update Q-learning (RUQL) algorithm is proposed. The proposed method enables the BS to overcome the biased action selection problem (e.g., an agent always takes a subset of actions while ignoring other actions), and hence, dynamically and quickly finding a device sampling and status update policy so as to minimize the sum of AoI and energy consumption of all devices. Simulations with real data of PM 2.5 pollution in Beijing from the Center for Statistical Science at Peking University show that the proposed algorithm can reduce the sum of AoI by up to 26.9% compared to the conventional Q-learning method.
研究了物联网(IoT)设备的信息年龄(AoI)和总能耗加权和的最小化问题。特别是,每个物联网设备都监控遵循非线性动力学的物理过程。由于物理过程的动态性随时间的变化而变化,因此每个设备必须对物理系统的实时状态进行采样,并将状态信息发送给基站(BS),以便对物理过程进行监控。实际物理过程的动态性会影响到每个器件的采样频率和状态更新方案。特别是,由于物理过程变化迅速,必须增加每个设备的采样频率以捕获这些物理动态。同时,采样频率的变化也会影响设备的能耗。因此,有必要在每个时隙确定一个设备子集来对物理过程进行采样,以便使用最小的能量准确地监测物理过程的动态。将该问题表述为一个优化问题,其目标是使AoI和设备总能耗的加权和最小。为了解决这一问题,提出了一种基于重复更新q -学习(RUQL)算法的机器学习框架。提出的方法使BS能够克服有偏差的动作选择问题(例如,一个agent总是采取一组动作而忽略其他动作),从而动态快速地找到设备采样和状态更新策略,从而使所有设备的AoI和能耗之和最小。北京大学统计科学中心对北京pm2.5污染的真实数据进行了模拟,结果表明,与传统的Q-learning方法相比,该算法可将AoI的总和降低26.9%。
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引用次数: 4
Explainability Methods for Identifying Root-Cause of SLA Violation Prediction in 5G Network 5G网络SLA违规预测根源识别的可解释性方法
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9322496
Ahmad Terra, R. Inam, Sandhya Baskaran, Pedro Batista, Ian Burdick, E. Fersman
Artificial Intelligence (AI) is implemented in various applications of telecommunication domain, ranging from managing the network, controlling a specific hardware function, preventing a failure, or troubleshooting a problem till automating the network slice management in 5G. The greater levels of autonomy increase the need for explainability of the decisions made by AI so that humans can understand them (e.g. the underlying data evidence and causal reasoning) consequently enabling trust. This paper presents first, the application of multiple global and local explainability methods with the main purpose to analyze the root-cause of Service Level Agreement violation prediction in a 5G network slicing setup by identifying important features contributing to the decision. Second, it performs a comparative analysis of the applied methods to analyze explainability of the predicted violation. Further, the global explainability results are validated using statistical Causal Dataframe method in order to improve the identified cause of the problem and thus validating the explainability.
人工智能(AI)在电信领域的各种应用中实现,从管理网络,控制特定硬件功能,防止故障或排除问题,直到5G中的网络切片管理自动化。更高水平的自主性增加了对人工智能决策的可解释性的需求,以便人类能够理解它们(例如底层数据证据和因果推理),从而实现信任。本文首先提出了多种全局和局部可解释性方法的应用,主要目的是通过识别有助于决策的重要特征来分析5G网络切片设置中服务水平协议违反预测的根本原因。其次,对分析预测违章可解释性的常用方法进行了比较分析。进一步,使用统计因果数据框架方法对全局可解释性结果进行验证,以改进问题的原因识别,从而验证可解释性。
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引用次数: 18
High Frequency Impedance Matching for Twisted Pair Cables 双绞线高频阻抗匹配
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9322202
Tobias Schaich, Karthik Subramaniam, E. Acedo, A. A. Rawi
This paper re-examines the 100 Ohm characteristic impedance assumption used in the context of Digital Subscriber Line in the very to ultra high frequency range. A novel method of measuring the characteristic impedance of a transmission line is introduced which uses time-gated measurements from a vector network analyser. The results are utilised to synthesise impedance matching circuits for the excitation of a single twisted pair and a two pair underground cable at centre frequencies of 150 MHz and 500 MHz. These circuits achieved reflections as low as $Omega$40 dB. Furthermore, we report gains in transmission increasing with frequency, ranging from few decibels below 100 MHz, to several in 100-250 MHz and many above 400 MHz when compared to a commercial reference balun. In fact, compatibility tests beyond the target cable samples demonstrated higher than 10 dB gains in 20, 40 and 60 metre underground cables at around 500 MHz indicating that sample-based matching circuits are not entirely sample-specific. Preliminary tests showed no significant change in crosstalk when using differential matching circuits.
本文重新研究了数字用户线路在甚高频到超高频范围内使用的100欧姆特性阻抗假设。介绍了一种利用矢量网络分析仪进行时门控测量传输线特性阻抗的新方法。结果被用于合成在中心频率为150 MHz和500 MHz的单双绞线和双对地下电缆的激励阻抗匹配电路。这些电路实现了低至$ $ Omega$40 dB的反射。此外,与商业参考平衡器相比,传输增益随频率增加而增加,范围从低于100 MHz的几个分贝到100-250 MHz的几个分贝,以及高于400 MHz的许多分贝。事实上,超出目标电缆样本的兼容性测试表明,在500 MHz左右的20,40和60米地下电缆中,增益高于10 dB,这表明基于样本的匹配电路并不完全针对样本。初步测试表明,采用差分匹配电路时,串扰没有显著变化。
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引用次数: 3
Generative Adversarial Network and Auto Encoder based Anomaly Detection in Distributed IoT Networks 基于生成对抗网络和自动编码器的分布式物联网网络异常检测
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9348244
Tian Zixu, Kushan Sudheera Kalupahana Liyanage, G. Mohan
With the advances in modern communication technologies, the application scale of Internet of Things (IoT) has evolved at an unprecedented level, which on the other hand poses threats to the IoT ecosystem. As the intrusions and malicious actions are becoming more complex and unpredictable, developing an effective anomaly detection system, considering the distributed nature of IoT networks, remains a challenge. Moreover, the lack of sufficiently large amount of data samples of IoT traffic and data privacy pose further challenges in developing a behavior-based anomaly detection system. To address these issues, we present an unsupervised hierarchical approach for anomaly detection through cooperation between generative adversarial network (GAN) and auto-encoder (AE). The problems of data aggregation and privacy preservation are addressed by reconstructing a sampling pool at a centralized controller using a collection of generators from the individual IoT networks. Then, a centralized global AE is trained and passed to individual local networks for anomaly detection after a final adaptation with the local raw data from the IoT nodes. The performance is evaluated using the UNSW Bot-IoT dataset and the results demonstrate the effectiveness of our proposed approach which outperforms other approaches.
随着现代通信技术的进步,物联网的应用规模达到了前所未有的水平,同时也给物联网生态系统带来了威胁。随着入侵和恶意行为变得越来越复杂和不可预测,考虑到物联网网络的分布式特性,开发有效的异常检测系统仍然是一个挑战。此外,缺乏足够大的物联网流量数据样本和数据隐私,为开发基于行为的异常检测系统带来了进一步的挑战。为了解决这些问题,我们提出了一种通过生成对抗网络(GAN)和自动编码器(AE)之间的合作进行异常检测的无监督分层方法。数据聚合和隐私保护的问题是通过使用来自各个物联网网络的一组生成器在集中控制器上重建采样池来解决的。然后,在与来自物联网节点的本地原始数据进行最终适配后,训练集中的全局AE并将其传递到各个本地网络进行异常检测。使用UNSW Bot-IoT数据集对性能进行了评估,结果证明了我们提出的方法优于其他方法的有效性。
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引用次数: 16
Physical-Layer Security for Cognitive Radio Networks over Cascaded Rayleigh Fading Channels 级联瑞利衰落信道认知无线网络的物理层安全
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9348134
Deemah H. Tashman, W. Hamouda
In this paper, physical-layer security (PLS) for an underlay cognitive radio network (CRN) over cascaded Rayleigh fading channels is studied. The underlying cognitive radio system consists of a secondary source transmitting to a destination over a cascaded Rayleigh fading channel. An eavesdropper is attempting to intercept the confidential information of the secondary users (SUs) pair. The secrecy is studied in terms of three main security metrics, which are the secrecy outage probability (SOP), the probability of non-zero secrecy capacity (Prnzc), and the intercept probability (Pint). The effects of the path loss and the variation of the distances from the SU transmitter over the secrecy are also analyzed. Results reveal the great effect of the cascade level over the system secrecy. In addition, the effect of varying the interference threshold that the PU receiver can tolerate over the secrecy of the SUs pair is studied. The effect of the channel model parameters of both the main and the wiretap channels is investigated using both simulation and analytical results.
本文研究了级联瑞利衰落信道下底层认知无线网络(CRN)的物理层安全问题。底层认知无线电系统由一个通过级联瑞利衰落信道向目的地发射的辅助源组成。事件解释窃听者试图拦截从用户对的机密信息。本文从保密中断概率(SOP)、非零保密能力概率(Prnzc)和拦截概率(Pint)三个主要安全指标来研究保密问题。分析了路径损耗和距离变化对保密性的影响。结果表明级联级别对系统保密性有很大的影响。此外,还研究了改变PU接收器所能容忍的干扰阈值对SUs对保密性的影响。利用仿真和分析结果研究了主信道和窃听信道模型参数的影响。
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引用次数: 12
Continual Information Cascade Learning 持续信息级联学习
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9322124
Fan Zhou, Xin Jing, Xovee Xu, Ting Zhong, Goce Trajcevski, Jin Wu
Modeling the information diffusion process is an essential step towards understanding the mechanisms driving the success of information. Existing methods either exploit various features associated with cascades to study the underlying factors governing information propagation, or leverage graph representation techniques to model the diffusion process in an end-to-end manner. Current solutions are only valid for a static and fixed observation scenario and fail to handle increasing observations due to the challenge of catastrophic forgetting problems inherent in the machine learning approaches used for modeling and predicting cascades. To remedy this issue, we propose a novel dynamic information diffusion model CICP (Continual Information Cascades Prediction). CICP employs graph neural networks for modeling information diffusion and continually adapts to increasing observations. It is capable of capturing the correlations between successive observations while preserving the important parameters regarding cascade evolution and transition. Experiments conducted on real-world cascade datasets demonstrate that our method not only improves the prediction performance with accumulated data but also prevents the model from forgetting previously trained tasks.
对信息扩散过程进行建模是理解驱动信息成功的机制的必要步骤。现有的方法要么利用与级联相关的各种特征来研究控制信息传播的潜在因素,要么利用图表示技术以端到端方式对扩散过程进行建模。目前的解决方案仅适用于静态和固定的观测场景,并且由于用于建模和预测级联的机器学习方法固有的灾难性遗忘问题的挑战,无法处理越来越多的观测。为了解决这个问题,我们提出了一种新的动态信息扩散模型CICP(连续信息级联预测)。CICP采用图神经网络对信息扩散进行建模,并不断适应不断增加的观测值。它能够捕获连续观测之间的相关性,同时保留有关级联演化和过渡的重要参数。在真实的级联数据集上进行的实验表明,我们的方法不仅可以提高累积数据的预测性能,而且可以防止模型忘记先前训练过的任务。
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引用次数: 5
Multi-Constraint Virtual Network Embedding Algorithm For Satellite Networks 卫星网络多约束虚拟网络嵌入算法
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9347993
Dan-dan Yang, Jiang Liu, Ran Zhang, Tao Huang
Satellite network constellation is promising in providing efficient global Internet access. While the constellation scale, the user population, and service variety in satellite networks are too large, requiring efficient resource allocation and network management. Network virtualization is an efficient solution to achieve preceding objectives, but conventional schemes on terrestrial networks are not well adapted to satellite networks. Therefore, in this work, we establish a network virtualization model considering topology dynamics, quality of service requirement, and resource constraint. Then we formulate Virtual Network Embedding (VNE) into optimization problems, and we propose a multi-constraint virtual network embedding algorithm to solve the problem. Finally, we evaluate the proposed scheme and prove its adaptability to satellite networks.
卫星网络星座在提供高效的全球互联网接入方面前景广阔。而卫星网络星座规模大、用户数量多、业务种类多,需要高效的资源分配和网络管理。网络虚拟化是实现上述目标的有效解决方案,但传统的地面网络虚拟化方案不适应卫星网络。因此,在本工作中,我们建立了一个考虑拓扑动态、服务质量需求和资源约束的网络虚拟化模型。然后将虚拟网络嵌入(VNE)转化为优化问题,并提出了一种多约束虚拟网络嵌入算法来解决该问题。最后,对该方案进行了评估,证明了其对卫星网络的适应性。
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引用次数: 7
Multi-Fingerprint for Wireless Localization in Time-Varying Indoor Environment 时变室内环境下的多指纹无线定位
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9348052
Lu Yu, Y. Leung, X. Chu, J. Ng
Fingerprint is one of the representative methods for wireless indoor localization. It uses a fingerprint database (measured in the offline phase) and the current received signal strengths (RSSs) (measured by the user's device in the online phase) to determine the location of this device. However, the RSSs and hence the localization accuracy would be affected by time-varying environmental factors (e.g., number of people in a shopping mall). In this paper, we propose a new method for wireless localization in time-varying indoor environments. In the offline phase, the proposed method measures extra information: it measures $E$ fingerprint databases for $E$ respective environmental conditions, where $E$ is a design parameter (e.g., $E=2$ for the peak period and the non-peak period in a shopping mall). In the online phase, it leverages the extra information for better localization in time-varying indoor environment, even when the current environmental condition is different from the ones considered in the offline phase. The proposed method is particularly suitable for the indoor venues for which their primary concern is to provide good quality localization services while they could afford a moderate amount of extra resources for one-off measurement in the offline phase (e.g., exhibition centers, airports, shopping malls, etc.). We conduct a simulation experiment and a real-world experiment to demonstrate that the proposed method gives accurate localization.
指纹是室内无线定位的代表性方法之一。它使用指纹数据库(在离线阶段测量)和当前接收到的信号强度(rss)(由用户的设备在在线阶段测量)来确定该设备的位置。然而,rss及其定位精度会受到时变环境因素(例如,购物中心的人数)的影响。本文提出了一种时变室内环境下的无线定位方法。在离线阶段,提出的方法测量额外的信息:它测量$E$指纹数据库的$E$各自的环境条件,其中$E$是一个设计参数(例如,$E=2$代表购物中心的高峰时段和非高峰时段)。在在线阶段,它利用额外的信息在时变的室内环境中进行更好的定位,即使当前的环境条件与离线阶段所考虑的环境条件不同。所提出的方法特别适用于室内场所,因为他们主要关注的是提供高质量的本地化服务,同时他们可以负担得起适量的额外资源,用于离线阶段的一次性测量(例如,展览中心、机场、购物中心等)。通过仿真实验和实际实验验证了该方法的定位精度。
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引用次数: 3
Cooperative path planning of a UAV swarm to meet temporal-spatial user demands 满足用户时空需求的无人机群协同路径规划
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9322226
Kai Wang, Xiao Zhang, Lingjie Duan
Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing, fast Internet connection, and local caching) to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing demands locally. It is necessary for different UAVs to cooperate with each other for servicing many users, and how to determine their cooperative path planning to best meet many users’ spatio-temporally distributed demands is an important question. This paper is the first to design and analyze cooperative path-planning algorithms of a UAV swarm for optimally servicing many spatial locations with dynamic user arrivals and waiting deadlines in the time horizon. For each UAV, it needs to decide whether to wait at the current location or chase a newly released demand in another location, under upper coordination with the other UAVs in the swarm. For each UAV’s routing problem even without coordinating with the rest UAVs, it follows dynamic programming structure and is difficult to solve directly given many user demands. We manage to simplify and propose an optimal algorithm of fast computation time (only polynomial with respect to both the numbers of user locations and user demands) for returning the UAV’s optimal path-planning. When a large number $|K|$ of UAVs are coordinating, the dynamic programming simplification becomes intractable. Alternatively, we present an iterative cooperation algorithm with approximation ratio 1 - $left(1 - frac{1}{|k|}right)^{|K|}$) in the worst case, which is proved to obviously outperform the traditional idea of partitioning UAVs to serve different user/location clusters separately. Finally, we conduct simulation experiments to show that our algorithm’s average performance is close to the optimum.
无人机(UAV)技术是为地面用户提供高质量移动服务(如边缘计算、快速互联网连接和本地缓存)的一种很有前途的解决方案,在这种情况下,服务覆盖范围有限的无人机在多个地理用户位置(如热点)之间飞行,以满足当地的服务需求。为满足多用户需求,不同无人机之间需要相互协作,如何确定协同路径规划以满足多用户的时空分布需求是一个重要问题。本文首次设计并分析了无人机群的协同路径规划算法,以最优地服务于具有动态用户到达和等待期限的多个空间位置。对于每一架无人机,在与群中其他无人机的上层协调下,需要决定是在当前位置等待,还是在另一个位置追逐新释放的需求。对于每架无人机的路由问题,即使不与其他无人机进行协调,也遵循动态规划结构,在用户需求较多的情况下,难以直接求解。我们设法简化并提出了一种快速计算时间(仅对用户位置数和用户需求数均为多项式)的最优算法,用于返回无人机的最优路径规划。当大量$|K|$无人机进行协调时,动态规划的简化变得难以解决。另外,在最坏情况下,我们提出了一种近似比为1 - $left(1 - frac{1}{|k|}right)^{|K|}$)的迭代合作算法,该算法被证明明显优于传统的划分无人机分别为不同用户/位置集群服务的思想。最后,我们进行了仿真实验,表明我们的算法的平均性能接近最优。
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引用次数: 3
Graph-Based Random Sampling for Massive Access in IoT Networks 基于图的物联网网络海量接入随机抽样
Pub Date : 2020-12-01 DOI: 10.1109/GLOBECOM42002.2020.9348082
Shiyu Zhai, Guobing Li, Zefeng Qi, Guomei Zhang
In this paper the massive access problem in IoT networks is studied from the perspective of graph signal processing (GSP). First, we reveal the connections of massive access in IoT networks and the sampling of a graph signal, and model the massive access problem as a graph-based random sampling problem. Second, inspired by the restricted isometry property (RIP) condition in compressed sensing, we derive the RIP condition for random sampling on band-limited graph signals, showing at the first time that band-limited graph signals can be recovered from randomly-selected noisy samples in a given probability. Based on the proposed RIP condition, the sampling probability of each sensing device is optimized through minimizing the Chebyshev or Gaussian approximations of mean square error between the original and the recovered signals. Experiments on the Bunny and Community graphs verify the stability of random sampling, and show the performance gain of the proposed random sampling solutions.
本文从图信号处理(GSP)的角度研究了物联网网络中的海量接入问题。首先,我们揭示了物联网网络中海量接入与图信号采样的联系,并将海量接入问题建模为基于图的随机采样问题。其次,受压缩感知中限制等距特性(RIP)条件的启发,导出了限带图信号随机采样的RIP条件,首次证明了限带图信号可以在给定概率下从随机选择的噪声样本中恢复出来。基于所提出的RIP条件,通过最小化原始信号与恢复信号之间均方误差的切比雪夫近似或高斯近似来优化每个传感设备的采样概率。在Bunny图和Community图上的实验验证了随机抽样的稳定性,并展示了所提出的随机抽样方案的性能增益。
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引用次数: 3
期刊
GLOBECOM 2020 - 2020 IEEE Global Communications Conference
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