results: simulations 显示，BeamShaper 比 contemporary LUT 基本的解决方案在cosine-similarity 和中央角度上表现更好，并且在几乎相同的时间尺度上表现更好。此外，我们还显示了我们的 DNN 基本方法具有更好的对抗量化噪声的性能，这是因为量化噪声对于数字相位调节器而言是一个重要的问题。Abstract
The widespread proliferation of mmW devices has led to a surge of interest in antenna arrays. This interest in arrays is due to their ability to steer beams in desired directions, for the purpose of increasing signal-power and/or decreasing interference levels. To enable beamforming, array coefficients are typically stored in look-up tables (LUTs) for subsequent referencing. While LUTs enable fast sweep times, their limited memory size restricts the number of beams the array can produce. Consequently, a receiver is likely to be offset from the main beam, thus decreasing received power, and resulting in sub-optimal performance. In this letter, we present BeamShaper, a deep neural network (DNN) framework, which enables fast and accurate beamsteering in any desirable 3-D direction. Unlike traditional finite-memory LUTs which support a fixed set of beams, BeamShaper utilizes a trained NN model to generate the array coefficients for arbitrary directions in \textit{real-time}. Our simulations show that BeamShaper outperforms contemporary LUT based solutions in terms of cosine-similarity and central angle in time scales that are slightly higher than LUT based solutions. Additionally, we show that our DNN based approach has the added advantage of being more resilient to the effects of quantization noise generated while using digital phase-shifters.
摘要
广泛的 millimeter 设备的普及，导致了天线数组的兴趣增加。这种兴趣是因为天线数组可以将指向所需的方向中的能量强化，以提高信号强度和/或降低干扰水平。为实现射频，通常需要存储在Look-Up表（LUT）中的数组系数。尽管LUT 允许快速滚动，但它们的内存大小有限制，因此数组只能生成一定数量的射频。这意味着接收器可能会偏离主束，从而降低接收到的功率，并导致不佳的性能。在这封信中，我们介绍了BeamShaper，一个深度神经网络（DNN）框架，它允许在任意三维方向上快速和准确地实现射频。与传统的有限存储LUT不同，BeamShaper 使用训练的神经网络模型来生成数组系数，而不是固定的数组。我们的 simulations 表明，BeamShaper 在cosine-similarity和中心角时间尺度上都高于当前LUT基本解决方案。此外，我们还发现了我们的神经网络基本方法在使用数字阶梯器时产生的量化噪声的影响更加抗性。
results: 对于提议的 ODDM 技术，一种非正交时frequency空间（NOTFS）数字调制被 derivation，并且在高负荷因子和白噪声频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率频率�Abstract
This paper proposes a Time-Frequency Space Transformation (TFST) to derive non-orthogonal bases for modulation techniques over the delay-doppler plane. A family of Overloaded Delay-Doppler Modulation (ODDM) techniques is proposed based on the TFST, which enhances flexibility and efficiency by expressing modulated signals as a linear combination of basis signals. A Non-Orthogonal Time-Frequency Space (NOTFS) digital modulation is derived for the proposed ODDM techniques, and simulations show that they offer high-mobility communication systems with improved spectral efficiency and low latency, particularly in challenging scenarios such as high overloading factors and Additive White Gaussian Noise (AWGN) channels. A modified sphere decoding algorithm is also presented to efficiently decode the received signal. The proposed modulation and decoding techniques contribute to the advancement of non-orthogonal approaches in the next-generation of mobile communication systems, delivering superior spectral efficiency and low latency, and offering a promising solution towards the development of efficient high-mobility communication systems.
摘要
Translation in Simplified Chinese:这篇论文提出了一种时Frequency空间转换（TFST），用于 derive 非对听基准 для模拟技术在延迟-Doppler平面上。基于 TFST，一家 Overloaded Delay-Doppler Modulation（ODDM）技术是提出的，这种技术可以提高 flexibility 和效率，将模拟信号表示为基准信号的线性组合。基于 NOTFS 数字模拟，一种非对听时Frequency空间（NOTFS）数字模拟是 derivation 的，并且在高负载因子和 Additive White Gaussian Noise（AWGN）频道下进行了丰富的 simulations，表明它们在高移动性系统中提供了更高的 spectral efficiency 和低延迟，特别是在高负载因子和 AWGN 频道下。此外，一种修改的 SPHERE 解码算法也是提出的，以高效地解码接收信号。提出的模拟和解码技术将在下一代移动通信系统中提供更高的 spectral efficiency 和低延迟，并且提供了高效的高移动性通信系统的开发方向。
RIS-Assisted Over-the-Air Adaptive Federated Learning with Noisy Downlink
for: This paper focuses on over-the-air federated learning (OTA-FL) in a heterogeneous edge-intelligent network with non-i.i.d. user dataset distributions and physical layer impairments.
methods: The proposed cross-layer algorithm jointly optimizes RIS configuration, communication, and computation resources to enhance learning performance, with dynamic local update steps, RIS phase shifts, and transmission power control.
results: The proposed algorithm outperforms the existing unified approach under heterogeneous systems and imperfect channel state information (CSI) in numerical results.Abstract
Over-the-air federated learning (OTA-FL) exploits the inherent superposition property of wireless channels to integrate the communication and model aggregation. Though a naturally promising framework for wireless federated learning, it requires care to mitigate physical layer impairments. In this work, we consider a heterogeneous edge-intelligent network with different edge device resources and non-i.i.d. user dataset distributions, under a general non-convex learning objective. We leverage the Reconfigurable Intelligent Surface (RIS) technology to augment OTA-FL system over simultaneous time varying uplink and downlink noisy communication channels under imperfect CSI scenario. We propose a cross-layer algorithm that jointly optimizes RIS configuration, communication and computation resources in this general realistic setting. Specifically, we design dynamic local update steps in conjunction with RIS phase shifts and transmission power to boost learning performance. We present a convergence analysis of the proposed algorithm, and show that it outperforms the existing unified approach under heterogeneous system and imperfect CSI in numerical results.
摘要
“阶层联盟学习（OTA-FL）利用无线通信频道的自然层次性来整合通信和模型集合。尽管是一个具有掌上优势的架构，但它需要注意对物理层问题的处理。在这个工作中，我们考虑了一个多样化的智能边缘网络，其中不同的边缘设备具有不同的资源，并且有不同的用户数据分布。我们运用了智能反射表面（RIS）技术来增强OTA-FL系统，并在同时进行同频道上的上传和下传噪音通信频道下进行无瑕的通信。我们提出了一个跨层数据算法，将RIS配置、通信和计算资源进行统一优化。具体来说，我们设计了动态本地更新步骤，与RIS相位调整和传输功率进行协同运作，以提高学习性能。我们提供了一个对照分析，说明了我们的方法在不同的多样化系统和实际问题下的性能优势。”
ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals
results: 该研究表明，ResEMGNet可以达到94.43%的全Subject-independent性能，在比较其他方法时表现出色。Abstract
Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as powerful tools in this context. We present ResEMGNet, designed to identify ALS and Myopathy directly from raw electromyography (EMG) signals. Unlike traditional methods that require intricate handcrafted feature extraction, ResEMGNet takes raw EMG data as input, reducing computational complexity and enhancing practicality. Our approach was rigorously evaluated using various metrics in comparison to existing methods. ResEMGNet exhibited exceptional subject-independent performance, achieving an impressive overall three-class accuracy of 94.43\%.
摘要
Amyotrophic Lateral Sclerosis (ALS) 和 Myopathy 是肌肉疾病，需要精准和高效的诊断方法。在这项研究中，我们利用深度学习技术来识别 ALS 和 Myopathy。卷积神经网络 (CNNs) 在这个上是非常有力的工具。我们介绍了 ResEMGNet，可以直接从 Raw 电romyography (EMG) 信号中识别 ALS 和 Myopathy。与传统方法不同，ResEMGNet 不需要手动提取特征，从而降低计算复杂性和提高实用性。我们的方法在不同的指标下进行了严格的评估，与现有方法进行了比较。ResEMGNet 在三类精度测试中取得了94.43%的总平均精度，表现出色。
BeamSec: A Practical mmWave Physical Layer Security Scheme Against Strong Adversaries
methods: 使用无知 adversary 位置/通道的方法，Robust against 协作窃听攻击，兼容标准
results: 与基eline schemes 比较，BSec 可以提高机密率 by 79.8%，防止单个和协作窃听攻击Abstract
The high directionality of millimeter-wave (mmWave) communication systems has proven effective in reducing the attack surface against eavesdropping, thus improving the physical layer security. However, even with highly directional beams, the system is still exposed to eavesdropping against adversaries located within the main lobe. In this paper, we propose \acrshort{BSec}, a solution to protect the users even from adversaries located in the main lobe. The key feature of BeamSec are: (i) Operating without the knowledge of eavesdropper's location/channel; (ii) Robustness against colluding eavesdropping attack and (iii) Standard compatibility, which we prove using experiments via our IEEE 802.11ad/ay-compatible 60 GHz phased-array testbed. Methodologically, BeamSec first identifies uncorrelated and diverse beam-pairs between the transmitter and receiver by analyzing signal characteristics available through standard-compliant procedures. Next, it encodes the information jointly over all selected beam-pairs to minimize information leakage. We study two methods for allocating transmission time among different beams, namely uniform allocation (no knowledge of the wireless channel) and optimal allocation for maximization of the secrecy rate (with partial knowledge of the wireless channel). Our experiments show that \acrshort{BSec} outperforms the benchmark schemes against single and colluding eavesdroppers and enhances the secrecy rate by 79.8% over a random paths selection benchmark.
摘要
高度的毫米波（mmWave）通信系统的指向性有效地减少了听到攻击的面积，从而提高物理层安全性。然而，即使使用非常指向的扩散，系统仍然面临着在主脉搜索区域内的听到攻击。在这篇论文中，我们提议了一种解决方案，称为BeamSec，以保护用户，即使在主脉搜索区域内。BeamSec的关键特点包括：* 无需知道听到者的位置和通道情况进行操作* 对协同听到攻击进行鲁棒性* 兼容标准，我们通过使用IEEE 802.11ad/ay兼容60GHz分布式测试床进行实验证明。方法上，BeamSec先通过分析标准可用的信号特征来确定不相关和多样的扩散对between the transmitter and receiver。接着，它将信息共同编码在所选择的所有扩散上以最小化信息泄露。我们研究了两种分配传输时间的方法，一种是均匀分配（不知道无线通道），另一种是最大化机密率的分配（具有部分无线通道知识）。我们的实验显示，BeamSec在单个和协同听到者面前的性能都高于参考方案，并提高机密率 by 79.8% compared to a random paths selection benchmark。
A Multi Constrained Transformer-BiLSTM Guided Network for Automated Sleep Stage Classification from Single-Channel EEG
paper_authors: Farhan Sadik, Md Tanvir Raihan, Rifat Bin Rashid, Minhjaur Rahman, Sabit Md Abdal, Shahed Ahmed, Talha Ibn Mahmud
for: automatic sleep scoring from single-channel EEG signals
methods: utilizes Convolutional Neural Network (CNN), transformer network, and Bidirectional Long Short Term Memory (BiLSTM)
results: outperforms different state-of-the-art techniques by a large margin in terms of accuracy, precision, and F1-score.Here’s the format you requested:
for: automatic sleep scoring from single-channel EEG signals
results: outperforms différents state-of-the-art techniques by a large margin in terms of accuracy, precision, and F1-score.Abstract
Sleep stage classification from electroencephalogram (EEG) is significant for the rapid evaluation of sleeping patterns and quality. A novel deep learning architecture, ``DenseRTSleep-II'', is proposed for automatic sleep scoring from single-channel EEG signals. The architecture utilizes the advantages of Convolutional Neural Network (CNN), transformer network, and Bidirectional Long Short Term Memory (BiLSTM) for effective sleep scoring. Moreover, with the addition of a weighted multi-loss scheme, this model is trained more implicitly for vigorous decision-making tasks. Thus, the model generates the most efficient result in the SleepEDFx dataset and outperforms different state-of-the-art (IIT-Net, DeepSleepNet) techniques by a large margin in terms of accuracy, precision, and F1-score.
摘要
休眠阶段分类从电enzephalogram（EEG）是重要的，可以快速评估休眠模式和质量。一种新的深度学习架构“DenseRTSleep-II”被提议用于自动休眠分类从单通道EEG信号。该架构利用了Convolutional Neural Network（CNN）、transformer网络和Bidirectional Long Short Term Memory（BiLSTM）等优点，以便更有效地进行休眠分类。此外，通过加入权重多失函数学习策略，这个模型在强制决策任务中更加准确地进行分类。因此，该模型在SleepEDFx数据集中 генетиче最高效果，并在与不同的现有技术（IIT-Net、DeepSleepNet）的比较中，在精度、准确率和F1-score等方面表现出了明显的优势。
EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN
results: 我们的方法在分类 Myopathy, Normal, 和 ALS 三类时表现出色，总准确率达到 92%。这个研究为 neuromuscular disorder 诊断带来了一个数据驱动、多类分类的方法，为早期检测提供了有价值的洞察。Abstract
Amyotrophic Lateral Sclerosis (ALS) and Myopathy present considerable challenges in the realm of neuromuscular disorder diagnostics. In this study, we employ advanced deep-learning techniques to address the detection of ALS and Myopathy, two debilitating conditions. Our methodology begins with the extraction of informative features from raw electromyography (EMG) signals, leveraging the Log-spectrum, and Delta Log spectrum, which capture the frequency contents, and spectral and temporal characteristics of the signals. Subsequently, we applied a deep-learning model, SpectroEMG-Net, combined with Convolutional Neural Networks (CNNs) and Attention for the classification of three classes. The robustness of our approach is rigorously evaluated, demonstrating its remarkable performance in distinguishing among the classes: Myopathy, Normal, and ALS, with an outstanding overall accuracy of 92\%. This study marks a contribution to addressing the diagnostic challenges posed by neuromuscular disorders through a data-driven, multi-class classification approach, providing valuable insights into the potential for early and accurate detection.
摘要
amyotrophic lateral sclerosis (ALS) 和 myopathy 在 neuromuscular disorder 诊断中存在很大的挑战。在这项研究中，我们使用高级深度学习技术来解决 ALS 和 myopathy 两种致命的疾病的检测。我们的方法开始于 raw electromyography (EMG) 信号中提取有用特征，利用 Log-spectrum 和 Delta Log spectrum，这两种特征捕捉信号的频谱特征和时间特征。然后，我们应用了深度学习模型 SpectroEMG-Net，结合 Convolutional Neural Networks (CNNs) 和 Attention，用于三类分类。我们的方法的稳定性得到了严格的评估，表明其在分类 Myopathy、Normal 和 ALS 中表现出色，总准确率达 92%。这项研究对 neuromuscular disorders 的诊断带来了一项数据驱动的多类分类方法，为早期检测提供了有价值的发现。
Coverage Analysis of Dynamic Coordinated Beamforming for LEO Satellite Downlink Networks
paper_authors: Daeun Kim, Jeonghun Park, Namyoon Lee
for: investigate the coverage performance of downlink satellite networks employing dynamic coordinated beamforming.
methods: modeling the spatial arrangement of satellites and users using Poisson point processes situated on concentric spheres, deriving analytical expressions for the coverage probability, and developing an approximation for the coverage probability.
results: dynamic coordinated beamforming significantly improves coverage compared to the absence of satellite coordination, and the optimal cluster size, which maximizes the ergodic spectral efficiency, increases with higher satellite density, provided that the number of antennas on the satellites is sufficiently large.Abstract
In this paper, we investigate the coverage performance of downlink satellite networks employing dynamic coordinated beamforming. Our approach involves modeling the spatial arrangement of satellites and users using Poisson point processes situated on concentric spheres. We derive analytical expressions for the coverage probability, which take into account the in-cluster geometry of the coordinated satellite set. These expressions are formulated in terms of various parameters, including the number of antennas per satellite, satellite density, fading characteristics, and path-loss exponent. To offer a more intuitive understanding, we also develop an approximation for the coverage probability. Furthermore, by considering the distribution of normalized distances, we derive the spatially averaged coverage probability, thereby validating the advantages of coordinated beamforming from a spatial average perspective. Our primary finding is that dynamic coordinated beamforming significantly improves coverage compared to the absence of satellite coordination, in direct proportion to the number of antennas on each satellite. Moreover, we observe that the optimal cluster size, which maximizes the ergodic spectral efficiency, increases with higher satellite density, provided that the number of antennas on the satellites is sufficiently large. Our findings are corroborated by simulation results, confirming the accuracy of the derived expressions.
摘要
在这篇论文中，我们研究了下降链接卫星网络的覆盖性能，使用动态协调扫描。我们的方法包括使用Poisson点过程模型卫星和用户的空间布局，并 derivate了覆盖概率的分析表达式。这些表达式考虑了协调卫星集的内部几何结构。为了更好地理解，我们还开发了覆盖概率的近似方法。此外，通过考虑 нормализа的距离分布，我们 derivate了平均覆盖概率，从而验证了协调扫描的优点。我们的主要发现是，动态协调扫描可以在不同卫星的覆盖性能方面提供显著改善，并且与卫星antenna的数量直接相关。此外，我们发现在卫星密度增加时，最佳团集大小，用于最大化随机 Spectral efficiency，随着卫星antenna的数量增加，而增加。我们的发现得到了仪表结果的验证，证明了我们 derive的表达式的准确性。
Enhancing Congestion Control to Improve User Experience in IoT Using LSTM Network
results: 本研究表明，通过使用LSTM网络预测技术，可以提高用户满意度和iot连接可靠性。通过测试和比较传统压力控制方法，评估了提议的策略的性能。Abstract
This study suggests a new strategy for improving congestion control by deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural networks (RNN), that excel at capturing temporal relationships and patterns in data. IoT-specific data such as network traffic patterns, device interactions, and congestion occurrences are gathered and analyzed. The gathered data is used to create and train an LSTM network architecture specific to the IoT environment. Then, the LSTM model's predictive skills are incorporated into the congestion control methods. This work intends to optimize congestion management methods using LSTM networks, which results in increased user satisfaction and dependable IoT connectivity. Utilizing metrics like throughput, latency, packet loss, and user satisfaction, the success of the suggested strategy is evaluated. Evaluation of performance includes rigorous testing and comparison to conventional congestion control methods.
摘要
Time Stretch with Continuous-Wave Lasers for Practical Fast Realtime Measurements
results: 通过 simulations和实验验证了该新方法的可行性，并描述了两种应用场景。Abstract
Realtime high-throughput sensing and detection enables the capture of rare events within sub-picosecond time scale, which makes it possible for scientists to uncover the mystery of ultrafast physical processes. Photonic time stretch is one of the most successful approaches that utilize the ultra-wide bandwidth of mode-locked laser for detecting ultrafast signal. Though powerful, it relies on supercontinuum mode-locked laser source, which is expensive and difficult to integrate. This greatly limits the application of this technology. Here we propose a novel Continuous Wave (CW) implementation of the photonic time stretch. Instead of a supercontinuum mode-locked laser, a wavelength division multiplexed (WDM) CW laser, pulsed by electro-optic (EO) modulation, is adopted as the laser source. This opens up the possibility for low-cost integrated time stretch systems. This new approach is validated via both simulation and experiment. Two scenarios for potential application are also described.
摘要
实时高通过率探测和检测可以在sub-picosecond时间尺度内捕捉罕见事件，使科学家可以揭示ultrafast物理过程的谜。光子时间延迟是这种方法中最成功的一种，它利用mode-locked激光器的ultra-wide频率带宽，以检测ultrafast信号。虽然强大，但它依赖于supercontinuum mode-locked激光源，这是昂贵的和困难集成的。这限制了这种技术的应用。我们提议了一种新的Continuous Wave（CW）实现方式， substitute supercontinuum mode-locked激光源，使用wavelength division multiplexed（WDM）CW激光器，由电子-光学（EO）模ulation启动。这开 up了low-cost集成时间延迟系统的可能性。这新的方法通过实验和 simulate validate。两种应用场景也被描述。
Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons
results: 研究结果显示，这个提出的协调策略可以实现VFC系统中的卸载延误最小化，并且与其他参考策略比较，有着更高的效率和可靠性。Abstract
Vehicles in platoons need to process many tasks to support various real-time vehicular applications. When a task arrives at a vehicle, the vehicle may not process the task due to its limited computation resource. In this case, it usually requests to offload the task to other vehicles in the platoon for processing. However, when the computation resources of all the vehicles in the platoon are insufficient, the task cannot be processed in time through offloading to the other vehicles in the platoon. Vehicular fog computing (VFC)-assisted platoon can solve this problem through offloading the task to the VFC which is formed by the vehicles driving near the platoon. Offloading delay is an important performance metric, which is impacted by both the offloading strategy for deciding where the task is offloaded and the number of the allocated vehicles in VFC to process the task. Thus, it is critical to propose an offloading strategy to minimize the offloading delay. In the VFC-assisted platoon system, vehicles usually adopt the IEEE 802.11p distributed coordination function (DCF) mechanism while having various computation resources. Moreover, when vehicles arrive and depart the VFC randomly, their tasks also arrive at and depart the system randomly. In this paper, we propose a semi-Markov decision process (SMDP) based offloading strategy while considering these factors to obtain the maximal long-term reward reflecting the offloading delay. Our research provides a robust strategy for task offloading in VFC systems, its effectiveness is demonstrated through simulation experiments and comparison with benchmark strategies.
摘要
车辆队伍中的车辆需要处理多个任务以支持不同的实时交通应用。当任务到达车辆时，车辆可能无法处理任务由于其有限的计算资源。在这种情况下，它通常会请求卸载任务到其他车辆队伍中的车辆进行处理。但当所有车辆队伍中的计算资源都不够时，任务无法在时间内进行处理 durch卸载到其他车辆队伍中的车辆。由于车辆队伍中的计算资源是有限的，因此需要提出一种卸载策略，以最小化卸载延迟。在VFC协助的车辆队伍系统中，车辆通常采用IEEE 802.11p分布协调功能（DCF）机制，而具有不同的计算资源。此外，当车辆随机到达和离开VFC时，其任务也随机到达和离开系统。在这篇论文中，我们提出了基于Markov决策过程（SMDP）的卸载策略，并考虑了这些因素，以获得最大化长期奖励，反映卸载延迟。我们的研究提供了VFC系统中任务卸载策略的稳定性和有效性，通过实验和比较基准策略进行证明。
A Generalized Approach for Recovering Time Encoded Signals with Finite Rate of Innovation
results: 我们通过数学实验 validate 了我们的方法，使用过过去文献中使用过的筛选器，以及不兼容的筛选器。此外，我们还 validate 了结果通过实验设备。Abstract
In this paper, we consider the problem of recovering a sum of filtered Diracs, representing an input with finite rate of innovation (FRI), from its corresponding time encoding machine (TEM) measurements. So far, the recovery was guaranteed for cases where the filter is selected from a number of particular mathematical functions. Here, we introduce a new generalized method for recovering FRI signals from the TEM output. On the theoretical front, we significantly increase the class of filters for which reconstruction is guaranteed, and provide a condition for perfect input recovery depending on the first two local derivatives of the filter. We extend this result with reconstruction guarantees in the case of noise corrupted FRI signals. On the practical front, in cases where the filter has an unknown mathematical function, the proposed method streamlines the recovery process by bypassing the filter modelling stage. We validate the proposed method via numerical simulations with filters previously used in the literature, as well as filters that are not compatible with the existing results. Additionally, we validate the results using a TEM hardware implementation.
摘要
在本文中，我们考虑了一个滤波后的Dirac恒等值的恢复问题，该问题的输入是有限速度变化（FRI）的。以前，恢复是对特定数学函数选择的筛选器进行保证的。在这里，我们提出了一种新的通用方法，可以将FRI信号从TEM输出中恢复。从理论角度来看，我们在筛选器的选择范围中提高了恢复是 garantido的类型，并提供了一个基于筛选器的第一两个本地导数的条件，以确定完美的输入恢复。此外，我们在噪声损害FRI信号时也提供了恢复保证。从实践角度来看，如果筛选器的数学函数未知，我们的方法可以缩短恢复过程，直接跳过筛选器模型阶段。我们通过使用以前在文献中使用过的筛选器、不兼容现有结果的筛选器进行数值实验 validate our method。此外，我们还使用TEM硬件实现来验证我们的结果。