paper_authors: Paul Goyes-Penafiel, Leon Suarez-Rodriguez, Claudia Correa, Henry Arguello
for: 提高深度学习方法的适用范围和数据特性适应性
methods: 使用对抗生成网络（GAN）和重构网络，实现数据异常性控制和特征多样性提高
results: 比基eline方法和深度学习秘密法等提高8dB的PSNR表示性能Abstract
Seismic data interpolation plays a crucial role in subsurface imaging, enabling accurate analysis and interpretation throughout the seismic processing workflow. Despite the widespread exploration of deep supervised learning methods for seismic data reconstruction, several challenges still remain open. Particularly, the requirement of extensive training data and poor domain generalization due to the seismic survey's variability poses significant issues. To overcome these limitations, this paper introduces a deep-learning-based seismic data reconstruction approach that leverages data redundancy. This method involves a two-stage training process. First, an adversarial generative network (GAN) is trained using synthetic seismic data, enabling the extraction and learning of their primary and local seismic characteristics. Second, a reconstruction network is trained with synthetic data generated by the GAN, which dynamically adjusts the noise and distortion level at each epoch to promote feature diversity. This approach enhances the generalization capabilities of the reconstruction network by allowing control over the generation of seismic patterns from the latent space of the GAN, thereby reducing the dependency on large seismic databases. Experimental results on field and synthetic seismic datasets both pre-stack and post-stack show that the proposed method outperforms the baseline supervised learning and unsupervised approaches such as deep seismic prior and internal learning, by up to 8 dB of PSNR.
摘要
减少数据的重要作用在地球声图减少中发挥重要作用，帮助减少分析和解释过程中的准确性。despite the widespread exploration of deep supervised learning methods for seismic data reconstruction, several challenges still remain open. Particularly, the requirement of extensive training data and poor domain generalization due to the seismic survey's variability poses significant issues. To overcome these limitations, this paper introduces a deep-learning-based seismic data reconstruction approach that leverages data redundancy. This method involves a two-stage training process. First, an adversarial generative network (GAN) is trained using synthetic seismic data, enabling the extraction and learning of their primary and local seismic characteristics. Second, a reconstruction network is trained with synthetic data generated by the GAN, which dynamically adjusts the noise and distortion level at each epoch to promote feature diversity. This approach enhances the generalization capabilities of the reconstruction network by allowing control over the generation of seismic patterns from the latent space of the GAN, thereby reducing the dependency on large seismic databases. Experimental results on field and synthetic seismic datasets both pre-stack and post-stack show that the proposed method outperforms the baseline supervised learning and unsupervised approaches such as deep seismic prior and internal learning, by up to 8 dB of PSNR.
User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality
results: 对比传统缓存和计算资源分配策略，提出的方法可以显著提高VR视频流媒体性能Abstract
In this paper, we present a novel content caching and delivery approach for mobile virtual reality (VR) video streaming. The proposed approach aims to maximize VR video streaming performance, i.e., minimizing video frame missing rate, by proactively caching popular VR video chunks and adaptively scheduling computing resources at an edge server based on user and network dynamics. First, we design a scalable content placement scheme for deciding which video chunks to cache at the edge server based on tradeoffs between computing and caching resource consumption. Second, we propose a machine learning-assisted VR video delivery scheme, which allocates computing resources at the edge server to satisfy video delivery requests from multiple VR headsets. A Whittle index-based method is adopted to reduce the video frame missing rate by identifying network and user dynamics with low signaling overhead. Simulation results demonstrate that the proposed approach can significantly improve VR video streaming performance over conventional caching and computing resource scheduling strategies.
摘要
在本文中，我们提出了一种新的移动虚拟现实（VR）视频流传输的内容缓存和交付方法。我们的方法旨在最大化VR视频流传输性能，即最小化视频帧缺失率，通过在边缘服务器上推动受欢迎的VR视频块缓存和根据用户和网络动态进行adaptive资源调度。首先，我们设计了一种可扩展的内容分布 schemes，用于决定边缘服务器上缓存哪些视频块。我们通过考虑计算和缓存资源消耗进行了负荷平衡。其次，我们提出了一种基于机器学习的VR视频交付方案，该方案在边缘服务器上分配计算资源来满足多个VR头戴设备的视频交付请求。我们采用了Whittle指数来减少视频帧缺失率，并通过低信号过载来识别网络和用户动态。实验结果表明，提出的方法可以 significatively提高VR视频流传输性能，比 convential缓存和计算资源调度策略更好。
A Universal Framework for Multiport Network Analysis of Reconfigurable Intelligent Surfaces
paper_authors: Matteo Nerini, Shanpu Shen, Hongyu Li, Marco Di Renzo, Bruno Clerckx
for: 本研究旨在提出一种通用的多口网络分析框架，用于研究受支持系统的各种扩展和改进。
methods: 本研究使用了阻抗、导电、散射参数分析方法来模型受支持系统和受支持架构。
results: 研究者通过三种等效模型来描述受支持系统的影响，并提出了选择合适参数的方法。numerical results提供了额外证明这三种模型之间的等效性。Abstract
Reconfigurable intelligent surface (RIS) is an emerging paradigm able to control the propagation environment in wireless systems. Most of the research on RIS has been dedicated to system-level optimization and, with the advent of beyond diagonal RIS (BD-RIS), to RIS architecture design. However, developing general and unified electromagnetic (EM)-compliant models for RIS-aided systems remains an open problem. In this study, we propose a universal framework for the multiport network analysis of RIS-aided systems. With our framework, we model RIS-aided systems and RIS architectures through impedance, admittance, and scattering parameter analysis. Based on these analyses, three equivalent models are derived accounting for the effects of impedance mismatching and mutual coupling. The three models are then simplified by assuming large transmission distances, perfect matching, and no mutual coupling to understand the role of the RIS in the communication model. The derived simplified models are consistent with the model used in related literature, although we show that an additional approximation is commonly considered in the literature. We discuss the benefits of each analysis in characterizing and optimizing the RIS and how to select the most suitable parameters according to the needs. Numerical results provide additional evidence of the equivalence of the three analyses.
摘要
可编程智能表面（RIS）是一种新兴思路，可以控制无线系统的宣传环境。大多数RIS研究都集中在系统水平优化和BD-RIS架构设计上。然而，为RIS协助系统设计通用和统一的电磁（EM）适应模型仍然是一个未解决的问题。在这种研究中，我们提出了RIS协助系统多口网络分析的普适框架。我们通过阻抗、导电、散射参数分析来模型RIS协助系统和RIS架构。根据这些分析，我们 derive three equivalent models, each accounting for the effects of impedance mismatching and mutual coupling. These models are then simplified by assuming large transmission distances, perfect matching, and no mutual coupling to understand the role of the RIS in the communication model. We discuss the benefits of each analysis in characterizing and optimizing the RIS and how to select the most suitable parameters according to the needs. Numerical results provide additional evidence of the equivalence of the three analyses.
results: 本文对3GPP Rel-16位置定位在户外和户内环境中的性能进行了低analyze，并提供了系统配置的变化对位置定位的影响。Abstract
The widespread adoption of the fifth generation (5G) of cellular networks has brought new opportunities for localization-based services. High-precision positioning use cases and functionalities defined by the standards are drawing the interest of vertical industries. In the transition to the deployment, this paper aims to provide an in-depth tutorial on 5G positioning, summarizing the historical events that led to the standardization of cellular-based positioning, describing current and forthcoming releases of the Third Generation Partnership Project (3GPP) standard, and discussing about the major research trends. This paper is intended to represent an exhaustive guide for researchers and practitioners by providing fundamental notions on wireless localization, comprehensive definitions of measurements and architectures, examples of algorithms, and details on simulation approaches. Our approach aims to merge practical aspects of enabled use cases and related requirements with theoretical methodologies and fundamental bounds, allowing to understand the trade-off between system complexity and achievable, i.e., tangible, benefits of 5G positioning services. We also discuss about current limitations to be resolved for delivering accurate positioning solutions. We evaluate the performances of 3GPP Rel-16 positioning in outdoor and indoor environments, providing thorough analyses of the effect of changing the system configuration.
摘要
fifth generation (5G) 通信网络的普及已经带来了基于本地化服务的新机遇。高精度定位用例和功能由标准定义，吸引了专业领域的关注。在部署过程中，这篇论文的目标是为研究人员和实践者提供5G定位的深入教程，从历史事件的发展到3GPP标准的发布，从当前和未来的3GPP标准发布中概述了主要的研究趋势。这篇论文旨在为研究人员和实践者提供5G定位的权威指南，涵盖无线地位定位的基础知识，完整的定义测量和架构，算法的示例，以及模拟方法。我们的方法旨在将实践的使用案例和相关要求与理论方法和基本上限相结合，以便理解系统复杂性和可以实现的优良定位服务的贸易。我们还讨论了5G定位服务的当前限制，以及需要解决的问题。我们评估了Rel-16版本的定位性能在户外和户内环境中，并提供了丰富的分析结果，包括系统配置变化的效果。
Mutual Coupling in RIS-Aided Communication: Experimental Validation and Performance Evaluation
paper_authors: Pinjun Zheng, Ruiqi Wang, Atif Shamim, Tareq Y. Al-Naffouri
for: 这篇论文探讨了协同扩散（RIS）帮助通信系统中的互相干扰。
methods: 该论文首先引入了一种基于3D全波模拟的新的模型训练方法，然后通过实验测量 Validated the obtained model in a 1-bit quasi-passive RIS prototype operating in the mmWave band.
results: 比较分析表明， employed mutual coupling-aware model and the assessed model parameters are precise, offering a realistic evaluation of mutual coupling in authentic RIS hardware. The results also show that the mutual coupling in RIS exhibits heightened significance with increased RIS amplitude gains and showcases a frequency-dependent effect.Abstract
This paper explores the mutual coupling in the reconfigurable intelligent surface (RIS)-aided communication. Despite the existence of several mutual coupling-aware models for RIS-aided communication, a notable gap remains due to the lack of experimental validation. This paper bridges this gap by first introducing a novel model training approach based on the 3D full-wave simulation and subsequently validating the obtained model via experimental measurements in a 1-bit quasi-passive RIS prototype operating in the mmWave band. Comparative analyses reveal precision in both the employed mutual coupling-aware model and the assessed model parameters, offering a realistic evaluation of mutual coupling in authentic RIS hardware. Utilizing the validated mutual coupling-aware communication model, we systematically examine the impact of mutual coupling on communication performance by adopting the achievable rate as a performance indicator. Our results reveal that the mutual coupling in RIS exhibits heightened significance with increased RIS amplitude gains and showcases a frequency-dependent effect.
摘要
“UWBCarGraz” Dataset for Car Occupancy Detection using Ultra-Wideband Radar
results: 对比VMP算法，ResNet架构在低信号响应比（SNR）下表现更好，具体来说，在SNR=-20dB下，VMP检测器的AUC为0.87，而ResNet架构的AUC为0.91，如果目标人在安静呼吸。对于其他活动水平，表现相似。为了在车辆上部硬件中实现，我们进行了一些减少计算复杂性和提高性能的ablation研究。数据集用于训练和评估算法是公开可用的。Abstract
We present a data-driven car occupancy detection algorithm using ultra-wideband radar based on the ResNet architecture. The algorithm is trained on a dataset of channel impulse responses obtained from measurements at three different activity levels of the occupants (i.e. breathing, talking, moving). We compare the presented algorithm against a state-of-the-art car occupancy detection algorithm based on variational message passing (VMP). Our presented ResNet architecture is able to outperform the VMP algorithm in terms of the area under the receiver operating curve (AUC) at low signal-to-noise ratios (SNRs) for all three activity levels of the target. Specifically, for an SNR of -20 dB the VMP detector achieves an AUC of 0.87 while the ResNet architecture achieves an AUC of 0.91 if the target is sitting still and breathing naturally. The difference in performance for the other activities is similar. To facilitate the implementation in the onboard computer of a car we perform an ablation study to optimize the tradeoff between performance and computational complexity for several ResNet architectures. The dataset used to train and evaluate the algorithm is openly accessible. This facilitates an easy comparison in future works.
摘要
我们提出了基于ultra-wideband雷达的数据驱动车辆占用检测算法，使用ResNet架构。我们对这种算法进行了训练，并对其进行了与现有state-of-the-art车辆占用检测算法（基于变分消息传递）的比较。我们发现，在低信号响应比（SNR）下，我们的ResNet架构能够超越VMP算法，在三种不同活动水平下的占用率中，具有更高的接收操作曲线面积（AUC）。具体来说，在SNR=-20dB下，VMP检测器的AUC为0.87，而我们的ResNet架构的AUC为0.91，当目标坐在安静地呼吸时。其他活动水平的差异类似。为了在车辆上部硬件中实现，我们进行了一项减少性能和计算复杂度之间的权衡分析，对多个ResNet架构进行了优化。我们使用的训练和评估数据集是公开 accessible，这使得未来的研究更容易进行比较。
Meta-DSP: A Meta-Learning Approach for Data-Driven Nonlinear Compensation in High-Speed Optical Fiber Systems
paper_authors: Xinyu Xiao, Zhennan Zhou, Bin Dong, Dingjiong Ma, Li Zhou, Jie Sun
for: This paper aims to improve the performance of long-haul, high-speed optical fiber systems by developing a novel data-driven nonlinear compensation model based on meta-learning.
methods: The proposed model, called Meta-DSP, processes multi-modal data across diverse transmission rates, power levels, and channel numbers to enhance signal quality and reduce the complexity of the nonlinear processing algorithm.
results: Compared to existing methods, Meta-DSP delivers a 0.7 dB increase in the Q-factor and reduces computational complexity by a factor of ten while retaining comparable performance. The model’s scalability and generalization performance make it a promising solution for addressing the critical parameters defining optical communication networks.Abstract
Non-linear effects in long-haul, high-speed optical fiber systems significantly hinder channel capacity. While the Digital Backward Propagation algorithm (DBP) with adaptive filter (ADF) can mitigate these effects, it suffers from an overwhelming computational complexity. Recent solutions have incorporated deep neural networks in a data-driven strategy to alleviate this complexity in the DBP model. However, these models are often limited to a specific symbol rate and channel number, necessitating retraining for different settings, their performance declines significantly under high-speed and high-power conditions. We introduce Meta-DSP, a novel data-driven nonlinear compensation model based on meta-learning that processes multi-modal data across diverse transmission rates, power levels, and channel numbers. This not only enhances signal quality but also substantially reduces the complexity of the nonlinear processing algorithm. Our model delivers a 0.7 dB increase in the Q-factor over Electronic Dispersion Compensation (EDC), and compared to DBP, it curtails computational complexity by a factor of ten while retaining comparable performance. From the perspective of the entire signal processing system, the core idea of Meta-DSP can be employed in any segment of the overall communication system to enhance the model's scalability and generalization performance. Our research substantiates Meta-DSP's proficiency in addressing the critical parameters defining optical communication networks.
摘要
非线性效应在长距离、高速光纤系统中带来了通道容量的很大障碍。尽管数字倒推算法（DBP）与自适应滤波器（ADF）可以减轻这些效应，但它们受到极高的计算复杂性的困扰。现有的解决方案通常是通过深度神经网络在数据驱动策略中引入它们。然而，这些模型通常只能在特定的符号速率和通道数下工作，需要重新训练，其性能在高速和高功率条件下减退 significatively。我们介绍了 Meta-DSP，一种基于元学习的数据驱动非线性补做模型。这种模型可以处理多模态数据，并在不同的传输速率、功率水平和通道数下工作。这不仅提高了信号质量，还减少了非线性处理算法的计算复杂性。我们的模型与电子排序补做（EDC）相比，提高了Q因子的值0.7dB，相比DBP，它减少了计算复杂性的因子为10，保持了相似的性能。从整个信号处理系统的角度来看，Meta-DSP的核心思想可以在任何系统中实现，以提高模型的扩展性和泛化性表现。我们的研究证明了Meta-DSP在光通信网络中的灵活性和可扩展性。
Downlink Transmission in FBMC-based Massive MIMO with Co-located and Distributed Antennas
results: 研究人员通过对不完美的通道匀化和 CSI 知识的分析，证明了该预编码器在实际应用中的出色性能。数值评估也表明，该预编码器在比较 OFDM 方法为参照点时表现出色。Abstract
This paper introduces a practical precoding method for the downlink of Filter Bank Multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) systems. The proposed method comprises a two-stage precoder, consisting of a fractionally spaced prefilter (FSP) per subcarrier to equalize the channel across each subcarrier band. This is followed by a conventional precoder that concentrates the signals of different users at their spatial locations, ensuring each user receives only the intended information. In practical scenarios, a perfect channel reciprocity may not hold due to radio chain mismatches in the uplink and downlink. Moreover, the channel state information (CSI) may not be perfectly known at the base station. To address these issues, we theoretically analyze the performance of the proposed precoder in presence of imperfect CSI and channel reciprocity calibration errors. Our investigation covers both co-located (cell-based) and cell-free massive MIMO cases. In the cell-free massive MIMO setup, we propose an access point selection method based on the received SINRs of different users in the uplink. Finally, we conduct numerical evaluations to assess the performance of the proposed precoder. Our results demonstrate the excellent performance of the proposed precoder when compared with the orthogonal frequency division multiplexing (OFDM) method as a benchmark.
摘要
In practical scenarios, the channel reciprocity may not hold due to radio chain mismatches in the uplink and downlink, and the channel state information (CSI) may not be perfectly known at the base station. To address these issues, the paper analyzes the performance of the proposed precoder in the presence of imperfect CSI and channel reciprocity calibration errors. The investigation covers both co-located (cell-based) and cell-free massive MIMO cases.In the cell-free massive MIMO setup, the paper proposes an access point selection method based on the received SINRs of different users in the uplink. Numerical evaluations are conducted to assess the performance of the proposed precoder, and the results show that it outperforms the orthogonal frequency division multiplexing (OFDM) method as a benchmark.
Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach
results: 通过使用目标车辆上的STARS系统，提高感知和通信性能，并在不同环境下进行了比较性分析和对比。Abstract
The utilization of integrated sensing and communication (ISAC) technology has the potential to enhance the communication performance of road side units (RSUs) through the active sensing of target vehicles. Furthermore, installing a simultaneous transmitting and reflecting surface (STARS) on the target vehicle can provide an extra boost to the reflection of the echo signal, thereby improving the communication quality for in-vehicle users. However, the design of this target-mounted STARS system exhibits significant challenges, such as limited information sharing and distributed STARS control. In this paper, we propose an end-to-end multi-agent deep reinforcement learning (MADRL) framework to tackle the challenges of joint sensing and communication optimization in the considered target-mounted STARS assisted vehicle networks. By deploying agents on both RSU and vehicle, the MADRL framework enables RSU and vehicle to perform beam prediction and STARS pre-configuration using their respective local information. To ensure efficient and stable learning for continuous decision-making, we employ the multi-agent soft actor critic (MASAC) algorithm and the multi-agent proximal policy optimization (MAPPO) algorithm on the proposed MADRL framework. Extensive experimental results confirm the effectiveness of our proposed MADRL framework in improving both sensing and communication performance through the utilization of target-mounted STARS. Finally, we conduct a comparative analysis and comparison of the two proposed algorithms under various environmental conditions.
摘要
utilization of integrated sensing and communication (ISAC) technology 可能会增强路边单元 (RSU) 的通信性能 через active sensing of target vehicles. In addition, installing a simultaneous transmitting and reflecting surface (STARS) on the target vehicle can provide an extra boost to the reflection of the echo signal, thereby improving the communication quality for in-vehicle users. However, the design of this target-mounted STARS system presents significant challenges, such as limited information sharing and distributed STARS control.In this paper, we propose an end-to-end multi-agent deep reinforcement learning (MADRL) framework to address the challenges of joint sensing and communication optimization in the considered target-mounted STARS assisted vehicle networks. By deploying agents on both RSU and vehicle, the MADRL framework enables RSU and vehicle to perform beam prediction and STARS pre-configuration using their respective local information. To ensure efficient and stable learning for continuous decision-making, we employ the multi-agent soft actor critic (MASAC) algorithm and the multi-agent proximal policy optimization (MAPPO) algorithm on the proposed MADRL framework.Extensive experimental results confirm the effectiveness of our proposed MADRL framework in improving both sensing and communication performance through the utilization of target-mounted STARS. Finally, we conduct a comparative analysis and comparison of the two proposed algorithms under various environmental conditions.
Joint channel estimation and data detection in massive MIMO systems based on diffusion models
methods: 该论文提出了一种基于扩散模型的 JOINT频率估计和数据检测算法，通过采样joint posterior distribution of symbols和通道来实现约束最大化估计。在实现这个算法时，我们构建了一个扩散过程，该模型joint distribution of channels and symbols given noisy observations，然后运行反向过程来生成样本。
results: 通过数值实验，我们示出了该算法比竞争方法具有更低的归一化平均方差Error和减少预先 overhead。Abstract
We propose a joint channel estimation and data detection algorithm for massive multilple-input multiple-output systems based on diffusion models. Our proposed method solves the blind inverse problem by sampling from the joint posterior distribution of the symbols and channels and computing an approximate maximum a posteriori estimation. To achieve this, we construct a diffusion process that models the joint distribution of the channels and symbols given noisy observations, and then run the reverse process to generate the samples. A unique contribution of the algorithm is to include the discrete prior distribution of the symbols and a learned prior for the channels. Indeed, this is key as it allows a more efficient exploration of the joint search space and, therefore, enhances the sampling process. Through numerical experiments, we demonstrate that our method yields a lower normalized mean squared error than competing approaches and reduces the pilot overhead.
摘要
我们提出了一种共同频道估计和数据检测算法，用于大规模多输入多出力系统，基于协沃分布。我们的提议方法解决了无目标反问题，通过采样 JOINT posterior distribution 的符号和通道，并计算 Approximate Maximum A Posteriori 估计。为实现这一点，我们构建了一个协沃过程，模型 JOINT 分布符号和通道给噪声观测，然后运行反向过程来生成样本。我们的唯一贡献在于包含符号的精确估计和学习的通道先验。这确实是关键，因为它允许更高效地探索 JOINT 搜索空间，并因此提高采样过程。通过数值实验，我们示出了我们的方法比竞争方法具有较低的 норmalized Mean Squared Error，并降低了卫星负荷。