paper_authors: M V Narayana, Kranthi Kumar Rachvarapu, Devendra Jalihal, Shiva Nagendra S M for:这个研究旨在提高低成本测器（LCS）的精确性，以便在空气质量监控中大规模地应用。methods:这个研究使用了一种基于BERT的学习方法，称为Sens-BERT，来对LCS进行准确化。这个方法分成两个阶段：自主学习预训和精确训练。在预训阶段，Sens-BERT被训练以使其学习LCS测器的资料分布特征，并生成对应的嵌入。在精确训练阶段，我们使用Sens-BERT嵌入来学习一个准确化模型。results:这个研究的结果显示，Sens-BERT可以对LCS进行高精确性的准确化，而且不需要大量的对照站资料或频繁的重新准确化。此外，Sens-BERT可以跨测器和位置进行转移学习，因此可以在不同的测器和位置上进行准确化。Abstract
Low-cost sensors measurements are noisy, which limits large-scale adaptability in airquality monitoirng. Calibration is generally used to get good estimates of air quality measurements out from LCS. In order to do this, LCS sensors are typically co-located with reference stations for some duration. A calibration model is then developed to transfer the LCS sensor measurements to the reference station measurements. Existing works implement the calibration of LCS as an optimization problem in which a model is trained with the data obtained from real-time deployments; later, the trained model is employed to estimate the air quality measurements of that location. However, this approach is sensor-specific and location-specific and needs frequent re-calibration. The re-calibration also needs massive data like initial calibration, which is a cumbersome process in practical scenarios. To overcome these limitations, in this work, we propose Sens-BERT, a BERT-inspired learning approach to calibrate LCS, and it achieves the calibration in two phases: self-supervised pre-training and supervised fine-tuning. In the pre-training phase, we train Sens-BERT with only LCS data (without reference station observations) to learn the data distributional features and produce corresponding embeddings. We then use the Sens-BERT embeddings to learn a calibration model in the fine-tuning phase. Our proposed approach has many advantages over the previous works. Since the Sens-BERT learns the behaviour of the LCS, it can be transferable to any sensor of the same sensing principle without explicitly training on that sensor. It requires only LCS measurements in pre-training to learn the characters of LCS, thus enabling calibration even with a tiny amount of paired data in fine-tuning. We have exhaustively tested our approach with the Community Air Sensor Network (CAIRSENSE) data set, an open repository for LCS.
摘要
低成本感测数据具有噪声，限制了大规模适应性在空气质量监测中。通常情况下，使用均拌法来获得良好的空气质量测量结果。为了实现这一点，低成本感测器通常会与参照站同时进行数据采集。然后，通过开发一个均拌模型，将低成本感测器的测量结果转换为参照站的测量结果。现有的方法通常是通过实时部署来训练一个模型，然后使用这个训练好的模型来估计当地的空气质量测量结果。然而，这种方法具有感测器和地点特定的限制，需要频繁重新均拌，并且重新均拌需要大量的数据，如初始均拌，这在实际应用中是一个繁琐的过程。为了解决这些限制，在这项工作中，我们提出了一种基于BERT的学习方法来均拌低成本感测器。我们的方法分为两个阶段：自主启动阶段和精度调整阶段。在自主启动阶段，我们使用只有低成本感测器数据（没有参照站观测）来帮助Sens-BERT学习数据分布特征，并生成相应的嵌入。然后，在精度调整阶段，我们使用Sens-BERT嵌入来学习一个均拌模型。我们的方法具有许多优势。因为Sens-BERT学习了低成本感测器的行为，因此它可以在任何相同感测原理的感测器上进行传输学习，不需要单独对每个感测器进行均拌。此外，我们只需要在启动阶段使用低成本感测器数据来学习低成本感测器的特征，因此在精度调整阶段只需要小量的配对数据，这在实际应用中是一个方便的。我们在社区空气感测网络（CAIRSENSE）数据集上进行了广泛的测试，并证明了我们的方法的可行性。
Multi-Static ISAC in Cell-Free Massive MIMO: Precoder Design and Privacy Assessment
results: 结果表明，在多Static 环境中，可以更精准地估算目标位置，比单Static 实现更好。Abstract
A multi-static sensing-centric integrated sensing and communication (ISAC) network can take advantage of the cell-free massive multiple-input multiple-output infrastructure to achieve remarkable diversity gains and reduced power consumption. While the conciliation of sensing and communication requirements is still a challenge, the privacy of the sensing information is a growing concern that should be seriously taken on the design of these systems to prevent other attacks. This paper tackles this issue by assessing the probability of an internal adversary to infer the target location information from the received signal by considering the design of transmit precoders that jointly optimizes the sensing and communication requirements in a multi-static-based cell-free ISAC network. Our results show that the multi-static setting facilitates a more precise estimation of the location of the target than the mono-static implementation.
摘要
一种多Static感知中心Integrated sensing and communication（ISAC）网络可以利用无细结构巨量多输入多输出基础设施，实现Remarkable的多样性增强和降低功率消耗。虽然感知和通信需求的妥协仍然是一个挑战，但感知信息的隐私问题在这些系统的设计中应该严重考虑，以防止其他攻击。本文通过评估接收信号中target位置信息的泄露概率，来评估 transmit precoder的设计，并jointly optimizes the sensing and communication requirements in a multi-static-based cell-free ISAC network。我们的结果表明，在多Static设计下，可以更准确地估计目标的位置，比单Static实现更高精度。
Reinforcement Learning for Robust Header Compression under Model Uncertainty
For: This paper investigates the integration of bi-directional header compression (BD-ROHC) with reinforcement learning (RL) to improve data efficiency in modern wireless communication systems.* Methods: The paper formulates a partially observable Markov decision process (POMDP) to model the compression process, and uses a deep Q-network (DQN) to learn the optimal compression policy.* Results: Compared to ideal dynamic programming (DP), the proposed method is more scalable and does not require prior knowledge of the transition dynamics or accurate observation dependency of the model.Abstract
Robust header compression (ROHC), critically positioned between the network and the MAC layers, plays an important role in modern wireless communication systems for improving data efficiency. This work investigates bi-directional ROHC (BD-ROHC) integrated with a novel architecture of reinforcement learning (RL). We formulate a partially observable \emph{Markov} decision process (POMDP), in which agent is the compressor, and the environment consists of the decompressor, channel and header source. Our work adopts the well-known deep Q-network (DQN), which takes the history of actions and observations as inputs, and outputs the Q-values of corresponding actions. Compared with the ideal dynamic programming (DP) proposed in the existing works, our method is scalable to the state, action and observation spaces. In contrast, DP often suffers from formidable computational complexity when the number of states becomes large due to long decompressor feedback delay and complex channel models. In addition, our method does not require prior knowledge of the transition dynamics and accurate observation dependency of the model, which are often not available in many practical applications.
摘要
Robust header compression（ROHC），位于网络和 MAC 层之间，在现代无线通信系统中扮演着重要的角色，以提高数据效率。这项工作 investigate 双向 ROHC（BD-ROHC）与 reinforcement learning（RL）新的架构相结合。我们将 partially observable 马尔可夫决策过程（POMDP）形式ulated，其中 compressor 是 agent，环境包括 decompressor、通道和 header source。我们采用了著名的深度优化网络（DQN），它接受了历史动作和观察输入，并输出对应动作的 Q-值。相比于现有的理想动态计划（DP），我们的方法可扩展到状态、动作和观察空间。而 DP 则经常由长 decompressor 反馈延迟和复杂的通道模型而受到强大的计算复杂度限制。此外，我们的方法不需要transition dynamics 和 observation dependency 的准确知识，这些知识在许多实际应用中通常不可获得。
How to Differentiate between Near Field and Far Field: Revisiting the Rayleigh Distance
paper_authors: Shu Sun, Renwang Li, Xingchen Liu, Liuxun Xue, Chong Han, Meixia Tao
for: This paper aims to provide a comprehensive overview of the existing near field (NF) and far field (FF) boundaries in wireless communication systems, and to introduce a novel NF-FF demarcation method based on effective degrees of freedom (EDoF) of the channel.
methods: The proposed method uses EDoF to characterize the channel and demarcate the NF and FF regions. The authors analyze the main features of the EDoF-based NF-FF boundary and provide insights into wireless system design.
results: The authors demonstrate that the EDoF-based border is able to characterize key channel performance more accurately than the classic Rayleigh distance, and provide insights into wireless system design.Here is the result in Simplified Chinese text:
results: 作者表明，EDoF基于的边界能够更准确地特征化频率响应的关键性能特征，并提供无线系统设计的启示。Abstract
Future wireless communication systems are likely to adopt extremely large aperture arrays and millimeter-wave/sub-THz frequency bands to achieve higher throughput, lower latency, and higher energy efficiency. Conventional wireless systems predominantly operate in the far field (FF) of the radiation source of signals. As the array size increases and the carrier wavelength shrinks, however, the near field (NF) becomes non-negligible. Since the NF and FF differ in many aspects, it is essential to distinguish their corresponding regions. In this article, we first provide a comprehensive overview of the existing NF-FF boundaries, then introduce a novel NF-FF demarcation method based on effective degrees of freedom (EDoF) of the channel. Since EDoF is intimately related to spectral efficiency, the EDoF-based border is able to characterize key channel performance more accurately, as compared with the classic Rayleigh distance. Furthermore, we analyze the main features of the EDoF-based NF-FF boundary and provide insights into wireless system design.
摘要
未来无线通信系统可能会采用非常大的天线数组和毫米波/亿赫兹频段来实现更高的传输速率、更低的延迟时间和更高的能效率。传统无线系统主要在辐射源信号的远场（FF）中运行。然而，随着天线数组的增大和辐射波长的减小，近场（NF）变得不可或缺。由于NF和FF在多方面存在差异，因此需要明确NF-FF的分界线。在这篇文章中，我们首先提供了NF-FF分界线的全面回顾，然后介绍了一种基于效果度量（EDoF）的通道分界方法。由于EDoF与spectral efficiency之间存在紧密的关系，EDoF-based分界线能够更加准确地描述通道性能的关键特征，相比于 классический辐射距离。此外，我们还分析了NF-FF分界线的主要特征，并对无线系统设计提供了深入的理解。