paper_authors: Murat Babek Salman, Emil Björnson, Gokhan Muzaffer Guvensen, Tolga Ciloglu
for: investigate the impact of frequency selectivity on nonlinear distortion in wireless communication systems
methods: closed-form expression for received distortion power as a function of number of multipath components (MPCs) and delay spread
results: in-band and OOB distortion power is inversely proportional to the number of MPCs, and the in-band distortion power is beamformed towards the intended userHere’s the summary in Traditional Chinese:
for: 研究无线通信系统中频率选择性对不对称干扰的影响
methods: 使用关键表达式来表示受到多普通道（MPC）和延迟幅度的受到干扰力
results: 对于不同频率域的干扰力，对于MPC的数量有直接的关系，并且在延迟幅度变窄时，对于指定用户的干扰力会增加。Abstract
Nonlinear distortion stemming from low-cost power amplifiers may severely affect wireless communication performance through out-of-band (OOB) radiation and in-band distortion. The distortion is correlated between different transmit antennas in an antenna array, which results in a beamforming gain at the receiver side that grows with the number of antennas. In this paper, we investigate how the strength of the distortion is affected by the frequency selectivity of the channel. A closed-form expression for the received distortion power is derived as a function of the number of multipath components (MPCs) and the delay spread, which highlight their impact. The performed analysis, which is verified via numerical simulations, reveals that as the number of MPCs increases, distortion exhibits distinct characteristics for in-band and OOB frequencies. It is shown that the received in-band and OOB distortion power is inversely proportional to the number of MPCs, and it is reported that as the delay spread gets narrower, the in-band distortion power is beamformed towards the intended user, which yields higher received in-band distortion compared to the OOB distortion.
摘要
非线性扭曲由低成本功率增强器引起的无线通信性能可能严重受到影响，主要通过射频外带（OOB）辐射和射频扭曲引起。这种扭曲与不同的发射天线之间存在相关性，导致发射天线阵列中的扭曲增强。在这篇论文中，我们研究了频率选择性通道的影响于扭曲强度。我们 derivated一个关于数目多path component（MPC）和延迟跨度的关键表达式，这些表达式阐明了他们对扭曲强度的影响。我们的分析，通过数值仿真验证，显示了随着MPC的增加，扭曲展现出明显的特征。 Specifically, we find that the received in-band and OOB distortion power is inversely proportional to the number of MPCs, and as the delay spread narrows, the in-band distortion power is beamformed towards the intended user, which yields higher received in-band distortion compared to the OOB distortion.Here's the translation in Traditional Chinese:非线性扭曲由低成本功率增强器引起的无线通信性能可能严重受到影响，主要通过射频外带（OOB）辐射和射频扭曲引起。这种扭曲与不同的发射天线之间存在相关性，导致发射天线阵列中的扭曲增强。在这篇论文中，我们研究了频率选择性通道的影响于扭曲强度。我们 derivated一个关于数目多path component（MPC）和延迟跨度的关键表达式，这些表达式阐明了他们对扭曲强度的影响。我们的分析，通过数值仿真验证，显示了随着MPC的增加，扭曲展现出明显的特征。 Specifically, we find that the received in-band and OOB distortion power is inversely proportional to the number of MPCs, and as the delay spread narrows, the in-band distortion power is beamformed towards the intended user, which yields higher received in-band distortion compared to the OOB distortion.
Deep Learning-Enabled Text Semantic Communication under Interference: An Empirical Study
results: 测试结果表明，当 Gaussian 多重干扰（RFI）的数量很大时，DeepSC方法会生成无关 semantic 句子。因此, 为了实现6G中的可靠性和可持续性，需要开发一种基于IR$^2$ SemCom的设计方案。Abstract
At the confluence of 6G, deep learning (DL), and natural language processing (NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G enabler by promising to minimize bandwidth consumption, transmission delay, and power usage. Among text SemCom techniques, \textit{DeepSC} is a popular scheme that leverages advancements in DL and NLP to reliably transmit semantic information in low signal-to-noise ratio (SNR) regimes. To understand the fundamental limits of such a transmission paradigm, our recently developed theory \cite{Getu'23_Performance_Limits} predicted the performance limits of DeepSC under radio frequency interference (RFI). Although these limits were corroborated by simulations, trained deep networks can defy classical statistical wisdom, and hence extensive computer experiments are needed to validate our theory. Accordingly, this empirical work follows concerning the training and testing of DeepSC using the proceedings of the European Parliament (Europarl) dataset. Employing training, validation, and testing sets \textit{tokenized and vectorized} from Europarl, we train the DeepSC architecture in Keras 2.9 with TensorFlow 2.9 as a backend and test it under Gaussian multi-interferer RFI received over Rayleigh fading channels. Validating our theory, the testing results corroborate that DeepSC produces semantically irrelevant sentences as the number of Gaussian RFI emitters gets very large. Therefore, a fundamental 6G design paradigm for \textit{interference-resistant and robust SemCom} (IR$^2$ SemCom) is needed.
摘要
在6G、深度学习（DL）和自然语言处理（NLP）的交叉点上，DL启用的文本semantic communication（SemCom）已经成为6G的推动者，承诺可以减少带宽消耗、传输延迟和功率使用。 among text SemCom技术，\textit{DeepSC} 是一种受欢迎的方案，利用了深度学习和NLP的进步来可靠地在低信号噪响（SNR）的情况下传输semantic信息。为了理解这种传输模式的基本限制，我们最近提出的理论 \cite{Getu'23_Performance_Limits} 预测了DeepSC在电磁干扰（RFI）下的性能限制。虽然这些限制得到了 simulations 的 corroboration，但是训练过的深度网络可能会违背经典统计知识，因此需要广泛的计算实验来验证我们的理论。因此，这项实验关注了使用 Keras 2.9 和 TensorFlow 2.9 作为后端，使用 Евро Parlament（Europarl）数据集进行训练和测试 DeepSC 架构。在 Gaussian 多源干扰 RFI 下接收 Rayleigh 抖动频道上测试 DeepSC，结果证明了我们的理论。因此，为了实现 \textit{干扰抗性和 Robust SemCom}（IR$^2$ SemCom）的6G设计方针，需要进一步研究和开发。
Transmission line condition prediction based on semi-supervised learning
for: 这 paper 的目的是提出一种基于半supervised learning的传输线路状态预测方法，以解决现有模型无法考虑机器稳定性和数据需求的问题。
methods: 这 paper 使用了扩展特征向量、正则矩阵和表示学习来解决填充缺失数据和稀疏编码问题。然后，通过一些标注样本初步确定了不同缺陷状态的线段分类中心。最后，使用了无标注样本来更正估算模型的参数。
results: 例分析表明，这种方法可以提高认知精度和更有效地使用数据，比现有模型更好。Abstract
Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level. Aiming at the problem that existing models cannot take into account the robustness and data demand, this paper proposes a state prediction method based on semi-supervised learning. Firstly, for the expanded feature vector, the regular matrix is used to fill in the missing data, and the sparse coding problem is solved by representation learning. Then, with the help of a small number of labelled samples to initially determine the category centers of line segments in different defective states. Finally, the estimated parameters of the model are corrected using unlabeled samples. Example analysis shows that this method can improve the recognition accuracy and use data more efficiently than the existing models.
摘要
<>对 transmission line 的状态评估和预测是操作和维护战略的重要因素，可以提高操作和维护水平。现有模型无法考虑系统稳定性和数据需求，这篇论文提出了基于半监督学习的状态预测方法。首先，为扩展特征向量，使用常量矩阵填充缺失数据，并通过表示学习解决稀缺编码问题。然后，使用一小部分标注样本初始化不同缺陷状态的线段类中心，并使用无标注样本修正估计模型参数。示例分析表明，这种方法可以提高识别精度和更好地利用数据。Note: "transmission line" is 电力传输线 in Chinese.
Increased Multiplexing Gain with Reconfigurable Surfaces: Simultaneous Channel Orthogonalization and Information Embedding
results: 研究结果表明，使用BIS和FRIS技术可以实现MU-MIMO系统中多个antenna的完全利用，并且可以在系统中嵌入额外信息以提高传输率。Abstract
Reconfigurable surface (RS) has been shown to be an effective solution for improving wireless communication links in general multi-user multiple-input multiple-output (MU-MIMO) setting. Current research efforts have been largely directed towards the study of reconfigurable intelligent surface (RIS), which corresponds to an RS made of passive reconfigurable elements with only phase shifting capabilities. RIS constitutes a cost- and energy- efficient solution for increased beamforming gain since it allows to generate constructive interference towards desired directions, e.g., towards a base station (BS). However, in many situations, multiplexing gain may have greater impact on the achievable transmission rates and number of simultaneously connected devices, while RIS has only been able to achieve minor improvements in this aspect. Recent work has proposed the use of alternative RS technologies, namely amplitude-reconfigurable intelligent surface (ARIS) and fully-reconfigurable intelligent surface (FRIS), to achieve perfect orthogonalization of MU-MIMO channels, thus allowing for maximum multiplexing gain at reduced complexity. In this work we consider the use of ARIS and FRIS for simultaneously orthogonalizing a MU-MIMO channel, while embedding extra information in the orthogonalized channel. We show that the resulting achievable rates allow for full exploitation of the degrees of freedom in a MU-MIMO system with excess of BS antennas.
摘要
响应式表面（RS）已经被证明可以提高无线通信链路的性能，特别是在多用户多输入多输出（MU-MIMO）设置下。当前的研究努力主要集中在研究响应式智能表面（RIS），这是一种具有只能进行相位调整的pasive响应式元件的RS。RIS可以具有更高的增幅功率和更多的连接数，但是在许多情况下，多重化增益可能更大地影响可达的传输率和同时连接的设备数量。最近的工作提议使用另一种RS技术，即幅度调整的智能表面（ARIS）和完全调整的智能表面（FRIS），以实现MU-MIMO频道的完美正交，从而实现最大的多重化增益，同时减少复杂度。在这个工作中，我们考虑使用ARIS和FRIS同时正交MU-MIMO频道，并嵌入额外信息在正交后的频道中。我们发现，所得到的可达率允许在BSantenna数量超出的MU-MIMO系统中完全利用多个自由度。
A Low-Complexity Machine Learning Design for mmWave Beam Prediction
results: 本研究显示，提案的模型可以实现state-of-the-art的准确性，并且降低计算复杂度，实现减少电力消耗和快速的 beam prediction。 I hope this helps! Let me know if you have any other questions.Abstract
The 3rd Generation Partnership Project (3GPP) is currently studying machine learning (ML) for the fifth generation (5G)-Advanced New Radio (NR) air interface, where spatial and temporal-domain beam prediction are important use cases. With this background, this letter presents a low-complexity ML design that expedites the spatial-domain beam prediction to reduce the power consumption and the reference signaling overhead, which are currently imperative for frequent beam measurements. Complexity analysis and evaluation results showcase that the proposed model achieves state-of-the-art accuracy with lower computational complexity, resulting in reduced power consumption and faster beam prediction. Furthermore, important observations on the generalization of the proposed model are presented in this letter.
摘要
现在3rd Generation Partnership Project（3GPP）正在研究 fifth generation（5G）Advanced New Radio（NR）空间域 beam prediction的机器学习（ML）应用，其中空间域和时间域 beam prediction是重要的应用场景。在这种背景下，本封信函数 presenta a low-complexity ML design that accelerates the spatial-domain beam prediction to reduce power consumption and reference signaling overhead, which are currently critical for frequent beam measurements. 复杂性分析和评估结果显示，提案的模型可以实现当前最佳准确率，同时具有较低的计算复杂性，从而降低电力消耗和快速的 beam prediction。此外，本函数还对提案模型的总体化进行了重要的观察。
for: This paper reviews the evolution of coronary stent technology and its impact on patient care.
methods: The paper discusses the development of various stent types, including bare metal stents (BMS), first-generation drug-eluting stents (DES), and second-generation DES and bioresorbable vascular scaffolds (BVS). Clinical trials have been crucial in validating each stent’s effectiveness.
results: The paper highlights the progress made in stent technology, but also acknowledges ongoing challenges in stent selection, approval processes, and minimizing risks. Despite these challenges, the future may see personalized stenting based on patient needs.Abstract
Coronary artery disease (CAD) is a leading cause of death worldwide. Treatments have evolved, with stenting becoming the primary approach over bypass surgery. This article reviews the evolution of coronary stent technology, starting from the first angioplasty in 1977. Pioneers like Forssmann, Dotter, and Gruentzig established the foundation. The late 1980s saw the introduction of bare metal stents (BMS) to address angioplasty limitations. However, BMS had issues, leading to the development of first-generation drug-eluting stents (DES) in the early 2000s, which reduced restenosis but had safety concerns. Subsequent innovations introduced second-generation DES with better results and the latest bioresorbable vascular scaffolds (BVS) that dissolve over time. Clinical trials have been crucial in validating each stent's effectiveness. Despite progress, challenges remain in stent selection, approval processes, and minimizing risks. The future may see personalized stenting based on patient needs, highlighting the significant advancements in stent technology and its impact on patient care.
摘要
心血管疾病（CAD）是全球最主要的死亡原因之一。治疗方法不断演化，自然硬着附加成为主要方法，而不是通过环路手术。本文将评论心血管镜仪技术的演化，从1977年的首次抗生物治疗开始。先驱们如福斯曼、多特和格劳恩茨基础设置了基础。1980年代中期，无质量镜仪（BMS）被引入，以解决抗生物治疗的局限性。然而，BMS存在问题，导致了第一代药粉镜仪（DES）的开发，它可以减少再生长，但存在安全问题。随后的创新引入了第二代DES，并且最新的生物逐渐消失的血管支架（BVS），它们在时间上逐渐消失。临床试验对每种镜仪的效果进行了验证。尽管进步了，但是镜仪选择、批准过程和降低风险仍然是挑战。未来可能会出现个性化镜仪，这 highlights the significant advancements in stent technology and its impact on patient care.
Optimal Status Updates for Minimizing Age of Correlated Information in IoT Networks with Energy Harvesting Sensors
results: 根据广泛的 simulations validate ，我们提出的方法可以减少 Age of Correlated Information（AoCI），并且比较有效地处理相关信息。Abstract
Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In this paper, we consider an IoT network consisting of sensors equipped with the energy harvesting (EH) capability. We optimize the average AoCI at the data fusion center (DFC) by appropriately managing the energy harvested by sensors, whose true battery states are unobservable during the decision-making process. Particularly, we first formulate the dynamic status update procedure as a partially observable Markov decision process (POMDP), where the environmental dynamics are unknown to the DFC. In order to address the challenges arising from the causality of energy usage, unknown environmental dynamics, unobservability of sensors'true battery states, and large-scale discrete action space, we devise a deep reinforcement learning (DRL)-based dynamic status update algorithm. The algorithm leverages the advantages of the soft actor-critic and long short-term memory techniques. Meanwhile, it incorporates our proposed action decomposition and mapping mechanism. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with available DRL algorithms for POMDPs.
摘要
Specifically, we first formulate the dynamic status update procedure as a partially observable Markov decision process (POMDP), where the environmental dynamics are unknown to the DFC. To address the challenges arising from the causality of energy usage, unknown environmental dynamics, unobservability of sensors'true battery states, and large-scale discrete action space, we propose a deep reinforcement learning (DRL)-based dynamic status update algorithm. The algorithm leverages the advantages of soft actor-critic and long short-term memory techniques. At the same time, it incorporates our proposed action decomposition and mapping mechanism. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with available DRL algorithms for POMDPs.