paper_authors: Zhou Zhang, Saman Atapattu, Yizhu Wang, Marco Di Renzo
for: 提高分布式网络中多用户通道访问的优化
methods: 基于可配置智能面(RIS)的分布式CSMA/CA策略，包括机会检测和避免冲突
results: 提出了一种优化的分布式CSMA/CA策略，可以 maximize 系统吞吐量，并且在数据分析和实验验证中表现出色，与现有方法相比表现更优。Abstract
This paper focuses on achieving optimal multi-user channel access in distributed networks using a reconfigurable intelligent surface (RIS). The network includes wireless channels with direct links between users and RIS links connecting users to the RIS. To maximize average system throughput, an optimal channel access strategy is proposed, considering the trade-off between exploiting spatial diversity gain with RIS assistance and the overhead of channel probing. The paper proposes an optimal distributed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic RIS assistance, based on statistics theory of optimal sequential observation planned decision. Each source-destination pair makes decisions regarding the use of direct links and/or probing source-RIS-destination links. Channel access occurs in a distributed manner after successful channel contention. The optimality of the strategy is rigorously derived using multiple-level pure thresholds. A distributed algorithm, which achieves significantly lower online complexity at $O(1)$, is developed to implement the proposed strategy. Numerical simulations verify the theoretical results and demonstrate the superior performance compared to existing approaches.
摘要
Translation:这篇论文关注了使用分布式网络中的可配置智能表面（RIS）实现最佳多用户通道访问。网络包括无线通道和RIS连接用户和RIS之间的连接。为了最大化系统吞吐量，提出了一种最佳的多用户通道访问策略，考虑了RIS协助下的空间多普通耗和扫描过程的开销。提出了基于统计学理论的最佳分布式CSMA/CA策略，每个源-目的对象对使用直接链接和/或探测源-RIS-目的链接进行决策。通道访问发生在分布式方式下，并且在成功扫描后进行通道竞争。提出的策略的优化性基于多级纯阈值理论。开发了一种实现该策略的分布式算法，具有较低的在线复杂度($O(1)$)。numerical simulations verify the theoretical results and demonstrate the superior performance compared to existing approaches.
Deep Reinforcement Learning for Backscatter Communications: Augmenting Intelligence in Future Internet of Things
results: 研究表明，DRL可以帮助BC系统提高性能和可靠性，同时也可以减少能耗。一个使用RIS增强非对称多access BC系统的实践案例也被详细探讨，以 highlight its potential。Abstract
Backscatter communication (BC) technology offers sustainable solutions for next-generation Internet-of-Things (IoT) networks, where devices can transmit data by reflecting and adjusting incident radio frequency signals. In parallel to BC, deep reinforcement learning (DRL) has recently emerged as a promising tool to augment intelligence and optimize low-powered IoT devices. This article commences by elucidating the foundational principles underpinning BC systems, subsequently delving into the diverse array of DRL techniques and their respective practical implementations. Subsequently, it investigates potential domains and presents recent advancements in the realm of DRL-BC systems. A use case of RIS-aided non-orthogonal multiple access BC systems leveraging DRL is meticulously examined to highlight its potential. Lastly, this study identifies and investigates salient challenges and proffers prospective avenues for future research endeavors.
摘要
🇨🇳 备受关注的技术：后递射通信（BC）技术可以为下一代互联网关键设备（IoT）网络提供可持续的解决方案，其中设备可以通过反射和调整 incident 无线电频信号来传输数据。同时，深度强化学习（DRL）技术在最近几年内 emerge 为优化低功耗 IoT 设备的工具。本文从 BC 系统的基础原理出发，然后介绍了多种 DRL 技术和其实践。接着，它 investigate 了 BC-DRL 系统在不同领域的应用前景，并 analyze 了一些最新的进展。最后，本文详细介绍了 RIS-assisted 非对称多接入 BC 系统的应用，以 illustrate 其潜在的优势。总之，本文概括了 BC 技术和 DRL 技术的相互作用，并 analyze 了它们在 IoT 网络中的应用前景。此外，它还提出了未来研究的挑战和机遇。
Secure Degree of Freedom of Wireless Networks Using Collaborative Pilots
results: 研究发现了一些重要的结论，包括：a) 阶段1的SDoF相同于多用户ANECE和对等ANECE，但前者可能需要较少的时间槽数；b) 三个节点网络中的阶段2SDoF通常比对等ANECE更大；c) 两节点网络中使用修改后的ANECE，使用块形非零幂频道Matrix，可以提高总的SDoF。这些多用户ANECE和修改后的两节点ANECE在安全度量方面与每个节点使用给定数量的天线进行发送和接收是今天已知最佳的全双工协议。Abstract
A wireless network of full-duplex nodes/users, using anti-eavesdropping channel estimation (ANECE) based on collaborative pilots, can yield a positive secure degree-of-freedom (SDoF) regardless of the number of antennas an eavesdropper may have. This paper presents novel results on SDoF of ANECE by analyzing secret-key capacity (SKC) of each pair of nodes in a network of multiple collaborative nodes per channel coherence period. Each transmission session of ANECE has two phases: phase 1 is used for pilots, and phase 2 is used for random symbols. This results in two parts of SDoF of ANECE. Both lower and upper bounds on the SDoF of ANECE for any number of users are shown, and the conditions for the two bounds to meet are given. This leads to important discoveries, including: a) The phase-1 SDoF is the same for both multi-user ANECE and pair-wise ANECE while the former may require only a fraction of the number of time slots needed by the latter; b) For a three-user network, the phase-2 SDoF of all-user ANECE is generally larger than that of pair-wise ANECE; c) For a two-user network, a modified ANECE deploying square-shaped nonsingular pilot matrices yields a higher total SDoF than the original ANECE. The multi-user ANECE and the modified two-user ANECE shown in this paper appear to be the best full-duplex schemes known today in terms of SDoF subject to each node using a given number of antennas for both transmitting and receiving.
摘要
一个无线网络，由全双工节点/用户组成，使用反听抓取渠道估计（ANECE），可以获得一定的安全度量（SDoF），无论抓取者具有多少天线。这篇论文提出了新的SDoF结果，通过分析每对节点的秘密键容量（SKC），并分析每个通信会话的两个阶段：第一阶段用于测试，第二阶段用于随机符号。这导致了两个SDoF的部分，其中一个是第一阶段的SDoF，另一个是第二阶段的SDoF。这篇论文还提供了对SDoF的下界和上界，以及这两个界限之间的条件。这些结果包括：a) 第一阶段SDoF在多用户ANECE和对应的对抗式ANECE中是相同的，而后者可能需要更少的时间槽数；b) 对于三个用户网络，第二阶段SDoF的全用户ANECE通常大于对抗式ANECE的SDoF；c) 对于两个用户网络，使用方形非零幂测试矩阵的修改后ANECE可以获得更高的总SDoF，比原始ANECE更高。这些多用户ANECE和修改后的两用户ANECE在今天可能是使用给定数量天线的最佳全双工方案，从SDoF的角度来看。
Near Field Optimization Algorithm for Reconfigurable Intelligent Surface
results: 通过电磁动力学 simulations，研究人员发现该算法可以很有效地重新配置智能表面，使电磁波能够强制方向性地传递到点 interests。Abstract
Reconfigurable intelligent surface (RIS) is a type of wireless communication technology that uses a reconfigurable surface, such as a wall or building that is able to adjust its properties by an integrated optimization algorithm in order to optimize the signal propagation for a given communication scenario. As a reconfiguration algorithm the multidimensional optimization of the GNU scientific library was analyzed to evaluate the performance of the smart surface in the quality of signal reception. This analysis took place by means of electrodynamic simulations based on the finite difference time domain method. Through these simulations it was possible to observe the efficiency of the algorithm in the reconfiguration of the RIS, managing to focus the electromagnetic waves in a remarkable way towards the point of interest.
摘要
智能表面重配置技术 (RIS) 是一种无线通信技术，使用可重配置的表面，如墙或建筑物，通过内置优化算法来调整其属性，以优化给定通信场景中信号协议的传播。作为重配置算法，多维度优化 GNU 科学库的分析进行了评估，以评估智能表面在信号接收质量方面的性能。这种分析通过基于 Finite Difference Time Domain 方法的电磁动力学模拟来进行。通过这些模拟，可以观察智能表面重配置算法的效率，并能够很有效地将电磁波集中到 interess point。
RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet
results: 根据我们的评估，RadYOLOLet 可以在不同的实验中，实现 100% 的 radar 探测精度，并且可以在干扰输入比例 (SINR) up to 16 dB 下运作正确。Abstract
Detection of radar signals without assistance from the radar transmitter is a crucial requirement for emerging and future shared-spectrum wireless networks like Citizens Broadband Radio Service (CBRS). In this paper, we propose a supervised deep learning-based spectrum sensing approach called RadYOLOLet that can detect low-power radar signals in the presence of interference and estimate the radar signal parameters. The core of RadYOLOLet is two different convolutional neural networks (CNN), RadYOLO and Wavelet-CNN, that are trained independently. RadYOLO operates on spectrograms and provides most of the capabilities of RadYOLOLet. However, it suffers from low radar detection accuracy in the low signal-to-noise ratio (SNR) regime. We develop Wavelet-CNN specifically to deal with this limitation of RadYOLO. Wavelet-CNN operates on continuous Wavelet transform of the captured signals, and we use it only when RadYOLO fails to detect any radar signal. We thoroughly evaluate RadYOLOLet using different experiments corresponding to different types of interference signals. Based on our evaluations, we find that RadYOLOLet can achieve 100% radar detection accuracy for our considered radar types up to 16 dB SNR, which cannot be guaranteed by other comparable methods. RadYOLOLet can also function accurately under interference up to 16 dB SINR.
摘要
“探测无助者的激光讯号是未来共享频率无线网络的重要需求，如公民广播电台服务（CBRS）。在这篇论文中，我们提出了一个监督学习基于的对应方法，名为RadYOLOLet，可以探测低功率激光讯号在干扰下的存在，并且估算激光讯号的参数。RadYOLOLet的核心是两个不同的卷积神经网（CNN）：RadYOLO和浪潮-CNN。这两个神经网在独立地训练。RadYOLO在spectrogram中运作，它提供了RadYOLOLet的大部分功能。然而，它在低信号载波比例（SNR） regime下的激光探测精度较低。我们为了解决这个问题，我们开发了特别的浪潮-CNN，它在捕捉到的信号中使用浪潮变换，并且仅在RadYOLO失败探测任何激光讯号时使用。我们对RadYOLOLet进行了不同类型的实验，包括不同类型的干扰信号。根据我们的评估，RadYOLOLet可以在考虑的激光型别上达到100%的探测精度，并且在干扰较高的16 dB SINR下还能正确运作。”
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning
For: 本文旨在研究无人飞行器群（UAV群）的投放和轨迹计划，以满足三维（3D）enario中的无线通信服务。* Methods: 本文提出了层次群组织机制，以有效地服务大面积用户。问题转化为最小化UAV群的总轨迹损失。但问题具有非托Formatter property，因此将其拆分为用户卷积、UAV群停留点选择和群 trajectory 确定。* Results: 本文采用Q学习算法加速解决效率。经过广泛的 simulations，提出的机制和算法被证明超过其他相关方法。Abstract
Unmanned aerial vehicles (UAVs) are recognized as promising technologies for area coverage due to the flexibility and adaptability. However, the ability of a single UAV is limited, and as for the large-scale three-dimensional (3D) scenario, UAV swarms can establish seamless wireless communication services. Hence, in this work, we consider a scenario of UAV swarm deployment and trajectory to satisfy 3D coverage considering the effects of obstacles. In detail, we propose a hierarchical swarm framework to efficiently serve the large-area users. Then, the problem is formulated to minimize the total trajectory loss of the UAV swarm. However, the problem is intractable due to the non-convex property, and we decompose it into smaller issues of users clustering, UAV swarm hovering points selection, and swarm trajectory determination. Moreover, we design a Q-learning based algorithm to accelerate the solution efficiency. Finally, we conduct extensive simulations to verify the proposed mechanisms, and the designed algorithm outperforms other referred methods.
摘要
无人飞行器（UAV）被认为是广泛覆盖区域的有望技术，由于它们的灵活和适应能力。然而，单个UAV的能力有限，而在大规模三维（3D）场景中，UAV群可以建立无缝无线通信服务。因此，在这项工作中，我们考虑了UAV群的部署和轨迹，以满足3D覆盖的需求，并考虑了障碍物的影响。在详细的描述中，我们提出了层次群组织，以高效地服务于大面积用户。然后，我们将问题定义为最小化UAV群的总轨迹损失。然而，问题的非核心性使得它不可解，我们将其分解为用户划分、UAV群停留点选择和群轨迹决定。此外，我们设计了Q学习算法，以加速解决效率。最后，我们进行了广泛的 simulate 来验证我们的机制，并发现我们的设计算法比其他已知方法更高效。
Alteration of skeletal muscle energy metabolism assessed by 31P MRS in clinical routine, part 2: Clinical application
paper_authors: Antoine Naëgel, Hélène Ratiney, Jabrane Karkouri, Djahid Kennouche, Nicolas Royer, Jill M Slade, Jérôme Morel, Pierre Croisille, Magalie Viallon
For: This study aimed to evaluate the impact of an advanced quality control pipeline on dynamic 31P-MRS studies of two patient populations with different types of fatigue, COVID-19 and multiple sclerosis (MS).* Methods: The study used 31P-MRS on a 3T clinical MRI to collect data from 19 COVID-19 patients, 38 MS patients, and 40 healthy controls. The advanced quality control pipeline was applied to the selected patient cohorts to investigate its impact on clinical outcomes.* Results: The application of the quality control pipeline resulted in increased statistical power, changed the values of several outcome measures, and reduced variability. Significant differences were found between the two patient populations and healthy controls for several metabolite concentrations, including T1PCr and T1Pi for MS patients, and resting [PCr], [Pi], [ADP], [H2PO4-], and pH for COVID-19 patients. Additionally, the use of a fixed correction factor led to systematically higher estimated concentrations of PCr and Pi than when using individually corrected factors.Abstract
Background: In this second part of a two-part paper, we intend to demonstrate the impact of the previously proposed advanced quality control pipeline. To understand its benefit and challenge the proposed methodology in a real scenario, we chose to compare the outcome when applying it to the analysis of two patient populations with a significant but highly different types of fatigue: COVID19 and multiple sclerosis (MS). Experimental: 31P-MRS was performed on a 3T clinical MRI, in 19 COVID19 patients, 38 MS patients, and 40 matched healthy controls. Dynamic acquisitions using an MR-compatible ergometer ran over a rest(40s), exercise(2min), and a recovery phase(6min). Long and short TR acquisitions were also made at rest for T1 correction. The advanced data quality control pipeline presented in part 1 is applied to the selected patient cohorts to investigate its impact on clinical outcomes. We first used power and sample size analysis to estimate objectively the impact of adding QCS. Then, comparisons between patients and healthy control groups using validated QCS were performed using unpaired T-tests or Mann-Whitney tests (p<0.05).Results: The application of the QCS resulted in increased statistical power, changed the values of several outcome measures, and reduced variability (SD). A significant difference was found between the T1PCr and T1Pi of MS patients and healthy controls. Furthermore, the use of a fixed correction factor led to systematically higher estimated concentrations of PCr and Pi than when using individually corrected factors. We observed significant differences between the two patient populations and healthy controls for resting [PCr] -- MS only, [Pi], [ADP], [H2PO4-] and pH -- COVID19 only, and post-exercise [PCr],[Pi] and [H2PO4-] - MS only. The dynamic indicators $\tau$PCr, $\tau$Pi, ViPCr and Vmax were reduced for COVID19 and MS patients compared to controls. Conclusion: Our results show that QCS in dynamic 31P-MRS studies results in smaller data variability and therefore impacts study sample size and power. Although QCS resulted in discarded data and therefore reduced the acceptable data and subject numbers, this rigorous and unbiased approach allowed for proper assessment of muscle metabolites and metabolism in patient populations. The outcomes include an increased metabolite T1, which directly affect the T1 correction factor applied to the amplitudes of the metabolite, and a prolonged $\tau$PCr indicating reduced muscle oxidative capacity for patients with MS and COVID19.
摘要
Background: 在这篇两部分文章的第二部分中，我们想要证明先前提出的高级质量控制管道的影响。为了了解其效果和挑战，我们选择了对两种不同类型的疲劳 patient population进行比较：COVID-19和多发性硬化病（MS）。Experimental: 我们使用3T临床MRI设备进行31P-MRS测量，共有19例COVID-19患者、38例MS患者和40例健康控制群。动态获取使用MR相容的耐力测试器在休息（40秒）、运动（2分）和恢复阶段（6分）进行测量。同时，我们还进行了长TR和短TR的获取，以便对T1的修正。我们对选择的患者群进行了高级数据质量控制管道的应用，以调查其影响临床结果。我们首先使用力和样本大小分析来对添加QCS的影响进行 объектив评估。然后，我们使用无对照组T检测或曼恩-怀特评估测试（p<0.05）来比较患者和健康控制群。Results: QCS的应用导致数据变量减少，提高了统计力，并改变了一些结果探索结果。COVID-19和MS患者的T1PCr和T1Pi与健康控制群相比显著不同。此外，使用固定修正因子导致PCr和Pi的估计值高于使用个体修正因子。我们发现COVID-19和MS患者在休息期的PCr、Pi、ADP、H2PO4-和pH中具有显著差异。在运动后，COVID-19和MS患者的PCr、Pi和H2PO4-中具有显著差异。动态指标$\tau$PCr、$\tau$Pi、ViPCr和Vmax在COVID-19和MS患者中相比于控制群表现为下降。Conclusion: 我们的结果表明，在动态31P-MRS研究中应用QCS会减少数据变量，因此影响研究样本大小和 statistically power。虽然QCS导致了抛弃数据，因此减少了可接受的数据和试验者数量，但这种不偏袋中和不偏障的方法允许我们对患者群进行正确的 метабоلит和代谢评估。结果包括T1的增加，直接影响了应用于激发物质的T1修正因子，以及COVID-19和MS患者的 prolonged $\tau$PCr， indicating reduced muscle oxidative capacity.
Index Modulation-based Information Harvesting for Far-Field RF Power Transfer
results: 研究结果表明，通过在现有的远场能量传输系统中应用IM技术，可以实现数据传输，特别在下一代物联网无线网络中表现出了明显的潜力。Abstract
While wireless information transmission (WIT) is evolving into its sixth generation (6G), maintaining terminal operations that rely on limited battery capacities has become one of the most paramount challenges for Internet-of-Things (IoT) platforms. In this respect, there exists a growing interest in energy harvesting technology from ambient resources, and wireless power transfer (WPT) can be the key solution towards enabling battery-less infrastructures referred to as zero-power communication technology. Indeed, eclectic integration approaches between WPT and WIT mechanisms are becoming a vital necessity to limit the need for replacing batteries. Beyond the conventional separation between data and power components of the emitted waveforms, as in simultaneous wireless information and power transfer (SWIPT) mechanisms, a novel protocol referred to as information harvesting (IH) has recently emerged. IH leverages existing WPT mechanisms for data communication by incorporating index modulation (IM) techniques on top of the existing far-field power transfer mechanism. In this paper, a unified framework for the IM-based IH mechanisms has been presented where the feasibility of various IM techniques are evaluated based on different performance metrics. The presented results demonstrate the substantial potential to enable data communication within existing far-field WPT systems, particularly in the context of next-generation IoT wireless networks.
摘要
sixth generation 无线信息传输 (6G) 的发展，使得互联网物联网 (IoT) 平台上的终端设备靠电池能力有限的问题变得非常紧迫。在这种情况下，能量收集技术从周围环境的资源成为了一种不可或缺的解决方案。无线电力传输 (WPT) 可以是针对无电池基础设施的关键解决方案。此外，将 WPT 和无线信息传输 (WIT) 机制结合在一起，以限制更换电池的需求。在传统的数据和电力两部分分离的情况下，新的协议被称为信息收集 (IH)，利用现有的 WPT 机制来实现数据传输。在这篇论文中，一种基于 индекс修改 (IM) 技术的 IH 机制的一体化框架被提出，并对不同的性能指标进行评估。获得的结果表明，可以在现有的远场 WPT 系统中实现数据传输，特别是在下一代 IoT 无线网络中。
Multi-Passive/Active-IRS Enhanced Wireless Coverage: Deployment Optimization and Cost-Performance Trade-off
results: 研究人员通过调整PIRS/AIRS的数量和部署位置，以实现给定的信噪比（SNR）目标，同时尽量降低总部署成本。 simulation结果表明，提议的算法可以在困难的 combinatorial optimization 问题中做出优化的选择，并且在cost-performance trade-off中表现更好。Abstract
Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In this paper, we study a multi-passive/active-IRS (PIRS/AIRS) aided wireless network with a multi-antenna base station (BS) in a given region. First, we divide the region into multiple non-overlapping cells, each of which may contain one candidate location that can be deployed with a single PIRS or AIRS. Then, we show several trade-offs between minimizing the total IRS deployment cost and enhancing the signal-to-noise ratio (SNR) performance over all cells via direct/cascaded LoS transmission with the BS. To reconcile these trade-offs, we formulate a joint multi-PIRS/AIRS deployment problem to select an optimal subset of all candidate locations for deploying IRS and also optimize the number of passive/active reflecting elements deployed at each selected location to satisfy a given SNR target over all cells, such that the total deployment cost is minimized. However, due to the combinatorial optimization involved, the formulated problem is difficult to be solved optimally. To tackle this difficulty, we first optimize the reflecting element numbers with given PIRS/AIRS deployed locations via sequential refinement, followed by a partial enumeration to determine the PIRS/AIRS locations. Simulation results show that our proposed algorithm achieves better cost-performance trade-offs than other baseline deployment strategies.
摘要
<TRANSLATE_TEXT> Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In this paper, we study a multi-passive/active-IRS (PIRS/AIRS) aided wireless network with a multi-antenna base station (BS) in a given region. First, we divide the region into multiple non-overlapping cells, each of which may contain one candidate location that can be deployed with a single PIRS or AIRS. Then, we show several trade-offs between minimizing the total IRS deployment cost and enhancing the signal-to-noise ratio (SNR) performance over all cells via direct/cascaded LoS transmission with the BS. To reconcile these trade-offs, we formulate a joint multi-PIRS/AIRS deployment problem to select an optimal subset of all candidate locations for deploying IRS and also optimize the number of passive/active reflecting elements deployed at each selected location to satisfy a given SNR target over all cells, such that the total deployment cost is minimized. However, due to the combinatorial optimization involved, the formulated problem is difficult to be solved optimally. To tackle this difficulty, we first optimize the reflecting element numbers with given PIRS/AIRS deployed locations via sequential refinement, followed by a partial enumeration to determine the PIRS/AIRS locations. Simulation results show that our proposed algorithm achieves better cost-performance trade-offs than other baseline deployment strategies.Translated by Google Translate
REM-U-net: Deep Learning Based Agile REM Prediction with Energy-Efficient Cell-Free Use Case
paper_authors: Hazem Sallouha, Shamik Sarkar, Enes Krijestorac, Danijela Cabric for:这篇论文是为了提出一种快速、准确地预测Radio环境地图（REM）的深度学习方法，以便优化无线网络部署、提高网络性能和有效地管理频率资源。methods:该论文使用了u-net网络，并在大规模3D地图 dataset上进行了训练。此外，文章还提出了数据处理步骤来进一步改进 REM 预测精度。results:论文在2023年IEEE ICASSP Signal Processing Grand Challenge中进行了评估，得到了0.045的 normalized root-mean-square error（RMSE）和14毫秒的平均运行时间。此外，文章还示出了在CF-mMIMO网络中预测 REM 的精度可以代替大规模的折射测量，从而减少能源消耗。Abstract
Radio environment maps (REMs) hold a central role in optimizing wireless network deployment, enhancing network performance, and ensuring effective spectrum management. Conventional REM prediction methods are either excessively time-consuming, e.g., ray tracing, or inaccurate, e.g., statistical models, limiting their adoption in modern inherently dynamic wireless networks. Deep-learning-based REM prediction has recently attracted considerable attention as an appealing, accurate, and time-efficient alternative. However, existing works on REM prediction using deep learning are either confined to 2D maps or use a limited dataset. In this paper, we introduce a runtime-efficient REM prediction framework based on u-nets, trained on a large-scale 3D maps dataset. In addition, data preprocessing steps are investigated to further refine the REM prediction accuracy. The proposed u-net framework, along with preprocessing steps, are evaluated in the context of the 2023 IEEE ICASSP Signal Processing Grand Challenge, namely, the First Pathloss Radio Map Prediction Challenge. The evaluation results demonstrate that the proposed method achieves an average normalized root-mean-square error (RMSE) of 0.045 with an average of 14 milliseconds (ms) runtime. Finally, we position our achieved REM prediction accuracy in the context of a relevant cell-free massive multiple-input multiple-output (CF-mMIMO) use case. We demonstrate that one can obviate consuming energy on large-scale fading measurements and rely on predicted REM instead to decide on which sleep access points (APs) to switch on in a CF-mMIMO network that adopts a minimum propagation loss AP switch ON/OFF strategy.
摘要
Radio 环境地图 (REM) 在无线网络部署、提高网络性能和有效spectrum管理中扮演中心角色。传统的 REM 预测方法是 either 过时 consume 时间 (如射线追踪) 或者不准确 (如统计模型)，这限制了它们在现代自然动态无线网络中的采用。深度学习基于的 REM 预测在最近吸引了大量关注，因为它们是一种吸引人的、准确的和高效的替代方案。然而，现有的 REM 预测使用深度学习的工作都是 confined to 2D 地图或者使用有限的数据集。在这篇文章中，我们提出了一个高效的 REM 预测框架，基于 u-nets，在大规模 3D 地图数据集上进行训练。此外，我们也 investigate 了数据预处理步骤，以进一步精细化 REM 预测精度。我们的提出的 u-net 框架、预处理步骤和评估结果在 2023 IEEE ICASSP Signal Processing Grand Challenge 中进行了评估。结果表明，我们的方法在normalized root-mean-square error (RMSE) 方面 achieve 平均值为 0.045，并且平均运行时间为 14 毫秒。最后，我们将我们实现的 REM 预测精度与相关的 cell-free massive multiple-input multiple-output (CF-mMIMO) 应用场景进行比较。我们表明，可以不消耗大量的能源进行大规模的折射损失测量，而是可以依靠预测的 REM 来决定在 CF-mMIMO 网络中 Switch ON/OFF 的大量睡眠Access Points (APs)。
On the Performance Analysis of RIS-Empowered Communications Over Nakagami-m Fading
paper_authors: Dimitris Selimis, Kostas P. Peppas, George C. Alexandropoulos, Fotis I. Lazarakis
for: 研究了无线通信透过具备自适应智能面（RISs）的 nakagami-m 调频通道性能。
methods: 考虑了两种阶段配置设计 для RIS：一个随机的和另一个基于协调频率调整。
results: 显示了对 binary 调变方案的停机概率、错误率和均质质量的单纯积分表达，并提出了精确的关键数据表示。Abstract
In this paper, we study the performance of wireless communications empowered by Reconfigurable Intelligent Surface (RISs) over Nakagami-m fading channels. We consider two phase configuration designs for the RIS, one random and another one based on coherent phase shifting. For both phase configuration cases, we present single-integral expressions for the outage probability and the bit error rate of binary modulation schemes, which can be efficiently evaluated numerically. In addition, we propose accurate closed-form approximations for the ergodic capacity of the considered system. For all considered metrics, we have also derived simple analytical expressions that become tight for large numbers of RIS reflecting elements. Numerically evaluated results compared with Monte Carlo simulations are presented in order to verify the correctness of the proposed analysis and showcase the impact of various system settings.
摘要
在这篇论文中，我们研究了基于可 configurable智能表面（RIS）的无线通信系统在 nakagami-m 折射通道上的性能。我们考虑了两种阶段配置设计 для RIS，一个是随机的，另一个是基于 coherent 相位调制。对于两种阶段配置情况，我们提供了单一积分表达式，可以高效地评估 numerically。此外，我们提出了准确的闭式表达式，用于评估系统的平均容量。对所有考虑的指标，我们还 deriv了简单的分析表达式，这些表达式在大量 RIS 反射元件时变得紧张。我们通过与 Monte Carlo 仿真结果进行比较，以验证我们的分析的正确性，并显示了不同系统设置对系统性能的影响。
Near-Field Beam Training: Joint Angle and Range Estimation with DFT Codebook
results: 经过numerical simulations表明，提议方法可以大幅降低near-field beam training的训练开销和提高范围估计精度，与various benchmark schemes相比有显著优势Abstract
Prior works on near-field beam training have mostly assumed dedicated polar-domain codebook and on-grid range estimation, which, however, may suffer long training overhead and degraded estimation accuracy. To address these issues, we propose in this paper new and efficient beam training schemes with off-grid range estimation by using conventional discrete Fourier transform (DFT) codebook. Specifically, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and show an interesting result that this beam pattern contains useful user angle and range information. Then, we propose two efficient schemes to jointly estimate the user angle and range with the DFT codebook. The first scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width, while the second scheme estimates the user range by minimizing a power ratio mean square error (MSE) to improve the range estimation accuracy. Finally, numerical simulations show that our proposed schemes greatly reduce the near-field beam training overhead and improve the range estimation accuracy as compared to various benchmark schemes.
摘要
先前的远近场域训练研究多做出了专门的极域编码ebook和在网格上的距离估计，但这些方法可能会带来长时间的训练开销和估计精度下降。为了解决这些问题，本文提出了一些新的和高效的远近场域训练方案，使用常见的快速傅立叶变换（DFT）编码ebook。 Specifically, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and show an interesting result that this beam pattern contains useful user angle and range information. Then, we propose two efficient schemes to jointly estimate the user angle and range with the DFT codebook. The first scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width, while the second scheme estimates the user range by minimizing a power ratio mean square error (MSE) to improve the range estimation accuracy. Finally, numerical simulations show that our proposed schemes greatly reduce the near-field beam training overhead and improve the range estimation accuracy as compared to various benchmark schemes.
Joint Beamforming for RIS Aided Full-Duplex Integrated Sensing and Uplink Communication
paper_authors: Yuan Guo, Yang Liu, Qingqing Wu, Xin Zeng, Qingjiang Shi
For: This paper studies the integrated sensing and communication (ISAC) technology in a full-duplex (FD) uplink communication system, with the aim of improving the uninterrupted target sensing and reducing self-interference (SI).* Methods: The paper employs reconfigurable intelligent surface (RIS) technology to improve the SI suppression and signal processing gain, and develops an iterative solution using convex optimization techniques such as majorization-minimization (MM) and penalty-dual-decomposition (PDD) to optimize all variables.* Results: Numerical results demonstrate the effectiveness of the proposed solution and the great benefit of employing RIS in the FD ISAC system.Abstract
This paper studies integrated sensing and communication (ISAC) technology in a full-duplex (FD) uplink communication system. As opposed to the half-duplex system, where sensing is conducted in a first-emit-then-listen manner, FD ISAC system emits and listens simultaneously and hence conducts uninterrupted target sensing. Besides, impressed by the recently emerging reconfigurable intelligent surface (RIS) technology, we also employ RIS to improve the self-interference (SI) suppression and signal processing gain. As will be seen, the joint beamforming, RIS configuration and mobile users' power allocation is a difficult optimization problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Numerical results demonstrate the effectiveness of our proposed solution and the great benefit of employing RIS in the FD ISAC system.
摘要
Semi-Supervised Variational Inference over Nonlinear Channels
methods: 这篇论文使用了 semi-supervised learning 方法，包括 Monte Carlo expectation maximization 和 variational autoencoder，以解码未知非线性通信 канаnl。
results: 这些方法可以充分利用少量的试验符号和数据payload，并且在充分多的数据payload情况下，variational autoencoder 也可以实现更低的错误率，比 meta learning 使用当前和前一个传输块的试验符号。Abstract
Deep learning methods for communications over unknown nonlinear channels have attracted considerable interest recently. In this paper, we consider semi-supervised learning methods, which are based on variational inference, for decoding unknown nonlinear channels. These methods, which include Monte Carlo expectation maximization and a variational autoencoder, make efficient use of few pilot symbols and the payload data. The best semi-supervised learning results are achieved with a variational autoencoder. For sufficiently many payload symbols, the variational autoencoder also has lower error rate compared to meta learning that uses the pilot data of the present as well as previous transmission blocks.
摘要
深度学习方法在未知非线性通道上进行通信已经吸引了相当多的关注。在这篇论文中，我们考虑使用变量推理的半监督学习方法来解码未知非线性通道。这些方法包括Monte Carlo预期最大化和变量自适应器，它们可以充分利用几个示例符号和数据 payload。变量自适应器在具有足够多payload符号时实现最佳半监督学习结果，并且在使用当前和前一个传输块的Meta学习时也具有较低的错误率。
A Comprehensive Study of PAPR Reduction Techniques for Deep Joint Source Channel Coding in OFDM Systems
paper_authors: Maolin Liu, Wei Chen, Jialong Xu, Bo Ai
for: 这篇论文主要针对的是深度联合源渠道编码（DJSCC）系统中的干扰率（PAPR）问题。
methods: 本论文使用了多种OFDM干扰率减少技术，包括传统技术such as clipping、companding、SLM和PTS，以及深度学习基于的PAPR减少技术such as PAPR损失和clipping with retraining。
results: 我们的调查发现，虽然传统的PAPR减少技术可以应用于DJSCC，但其性能与传统的分源渠道编码不同。此外，我们发现，对信号损害PAPR减少技术，clipping with retraining可以在DJSCC中实现最好的性能，并且不会对信号重建率产生负面影响。同时，对信号非损害PAPR减少技术可以成功地减少DJSCC中的PAPR，不会影响信号重建率。Abstract
Recently, deep joint source channel coding (DJSCC) techniques have been extensively studied and have shown significant performance with limited bandwidth and low signal to noise ratio. Most DJSCC work considers discrete-time analog transmission, while combining it with orthogonal frequency division multiplexing (OFDM) creates serious high peak-to-average power ratio (PAPR) problem. This paper conducts a comprehensive analysis on the use of various OFDM PAPR reduction techniques in the DJSCC system, including both conventional techniques such as clipping, companding, SLM and PTS, and deep learning-based PAPR reduction techniques such as PAPR loss and clipping with retraining. Our investigation shows that although conventional PAPR reduction techniques can be applied to DJSCC, their performance in DJSCC is different from the conventional split source channel coding. Moreover, we observe that for signal distortion PAPR reduction techniques, clipping with retraining achieves the best performance in terms of both PAPR reduction and recovery accuracy. It is also noticed that signal non-distortion PAPR reduction techniques can successfully reduce the PAPR in DJSCC without compromise to signal reconstruction.
摘要
近来，深度联合源渠道编码（DJSCC）技术已经得到了广泛研究和应用，它可以在具有有限带宽和低信噪比的情况下显示出较高的性能。大多数DJSCC工作都是对离散时间分析传输进行研究，而将OFDM分配多谱分多层（PAPR）问题引入到DJSCC系统中会产生严重的高峰值至平均功率比（PAPR）问题。本文对DJSCC系统中OFDM PAPR减少技术的使用进行了全面的分析，包括传统技术such as clipping、companding、SLM和PTS，以及深度学习基于PAPR减少技术such as PAPR损失和clipping with retraining。我们的调查表明，虽然传统PAPR减少技术可以应用于DJSCC，但它们在DJSCC中的性能与传统分Split source channel coding不同。此外，我们发现在信号损害PAPR减少技术中，clipping with retraining可以在PAPR减少和重建精度方面达到最佳性能。此外，我们还发现了在非损信号PAPR减少技术中，可以成功地减少DJSCC中的PAPR，而无需牺牲信号重建精度。
Quantum Circuits for Stabilizer Error Correcting Codes: A Tutorial
results: 论文验证了这些电路的正确性，并提供了使用IBM Qiskit进行验证的方法。Abstract
Quantum computers have the potential to provide exponential speedups over their classical counterparts. Quantum principles are being applied to fields such as communications, information processing, and artificial intelligence to achieve quantum advantage. However, quantum bits are extremely noisy and prone to decoherence. Thus, keeping the qubits error free is extremely important toward reliable quantum computing. Quantum error correcting codes have been studied for several decades and methods have been proposed to import classical error correcting codes to the quantum domain. However, circuits for such encoders and decoders haven't been explored in depth. This paper serves as a tutorial on designing and simulating quantum encoder and decoder circuits for stabilizer codes. We present encoding and decoding circuits for five-qubit code and Steane code, along with verification of these circuits using IBM Qiskit. We also provide nearest neighbour compliant encoder and decoder circuits for the five-qubit code.
摘要
量子计算机有可能提供指数增速于其经典对手。量子原理在通信、信息处理和人工智能等领域应用以实现量子优势。然而，量子比特非常易受噪声和降解的影响。因此，保持量子比特错误自由非常重要于可靠的量子计算。量子错误修复代码已经在几十年内研究，并提出了将经典错误修复代码引入量子领域的方法。然而，这些圈定器和解码器电路的设计和仿真还没有得到深入研究。这篇论文作为量子编码和解码电路设计和仿真的教程，我们提出了五个量子比特编码和斯特恩代码的编码和解码电路，并使用IBM Qiskit进行验证。此外，我们还提供了最近邻居兼容的编码和解码电路 для五个量子比特编码。
Collaborative Fault-Identification & Reconstruction in Multi-Agent Systems
methods: 基于sequential convex programming (SCP) 和 alternating direction method of multipliers (ADMM) 优化方法，实现分布式多 Agent FDIR算法。
results: 可以处理多 Agent 间测量（包括距离、方向、相对速度和夹角），确定faulty Agent 和重建其真实状态。Abstract
The conventional solutions for fault-detection, identification, and reconstruction (FDIR) require centralized decision-making mechanisms which are typically combinatorial in their nature, necessitating the design of an efficient distributed FDIR mechanism that is suitable for multi-agent applications. To this end, we develop a general framework for efficiently reconstructing a sparse vector being observed over a sensor network via nonlinear measurements. The proposed framework is used to design a distributed multi-agent FDIR algorithm based on a combination of the sequential convex programming (SCP) and the alternating direction method of multipliers (ADMM) optimization approaches. The proposed distributed FDIR algorithm can process a variety of inter-agent measurements (including distances, bearings, relative velocities, and subtended angles between agents) to identify the faulty agents and recover their true states. The effectiveness of the proposed distributed multi-agent FDIR approach is demonstrated by considering a numerical example in which the inter-agent distances are used to identify the faulty agents in a multi-agent configuration, as well as reconstruct their error vectors.
摘要
传统的瑕点检测、识别和重建（FDIR）解决方案通常需要中央决策机制，这些机制通常是 combinatorial 的性质，需要设计一种高效的分布式 FDIR 机制，适用于多机器人应用。为此，我们开发了一种高效地重建 sparse vector 在感知网络上被观察的框架。该框架基于 sequential convex programming (SCP) 和 alternating direction method of multipliers (ADMM) 优化方法来设计分布式多机器人 FDIR 算法。该算法可以处理多机器人之间的各种测量数据（包括距离、方向、相对速度和 agents 之间的夹角）来识别瑕点机器人并重建其真实状态。我们通过一个数学示例来证明提出的分布式多机器人 FDIR 方法的效果，在这个示例中，利用了机器人之间的距离测量来识别瑕点机器人和重建其错误向量。
Enhancing the SEFDM Performance in High-Doppler Channels
results: 该研究发现，使用 SEFDM 技术可以在移动通信频率延迟和Doppler偏移的环境中实现可靠和高质量的通信，并且可以保持传统通信系统的 spectral efficiency。Abstract
In this paper, we propose the use of Spectrally Efficient Frequency Division Multiplexing (SEFDM) with additional techniques such as Frequency Domain Cyclic Prefix (FDCP) and Modified Non-Linear (MNL) acceleration for efficient handling of the impact of delay and Doppler shift in mobile communication channels. Our approach exhibits superior performance and spectral efficiency in comparison to traditional communication systems, while maintaining low computational cost. We study a model of the SEFDM communication system and investigate the impact of MNL acceleration with soft and hard decision Inverse System on the performance of SEFDM detection in the AWGN channel. We also analyze the effectiveness of FDCP in compensating for the impact of Doppler shift, and report BER detection figures using Regularized Sphere Decoding in various simulation scenarios. Our simulations demonstrate that it is possible to achieve acceptable performance in Doppler channels while maintaining the superiority of SEFDM over OFDM in terms of spectral efficiency. The results suggest that our proposed approach can tackle the effects of delay and Doppler shift in mobile communication networks, guaranteeing dependable and high-quality communication even in extremely challenging environments.
摘要
在这篇论文中，我们提议使用具有频率分配多普雷斯特（SEFDM）的spectrally efficient frequency division multiplexing技术，并采用频域循环 prefix（FDCP）和修改非线性（MNL）加速技术来有效地处理移动通信频道中的延迟和Doppler偏移的影响。我们的方法在比较 tradicional communication systems的情况下表现出较高的性能和频率效率，同时保持低的计算成本。我们研究了SEFDM通信系统的模型，并investigate MNL加速器在SOFT和HARD decision inverse system中的影响。我们还分析了FDCP在补做Doppler偏移的效果，并report了在不同的 simulate scenario中的BER检测数据。我们的Simulations表明，可以在Doppler频道中实现可接受的性能，同时保持SEFDM在OFDM方面的优势。结果表明，我们提议的方法可以在移动通信网络中抵御延迟和Doppler偏移的影响，保证高质量和可靠的通信，even in extremely challenging environments。
for: 这篇论文专门针对受限制的量化 Communication system 中的orthogonal time frequency space (OTFS) 技术，以实现成本和功率的最优化。
methods: 论文使用了coarse quantization 和 signal recovery 算法，包括原始的approximate message passing (AMP) 和 generalized expectation consistent for signal recovery (GEC-SR)。
results: 论文提出了一种低复杂度的算法，即将 GEC-SR 算法与快速归一化的 quasi-banded matrices 结合，从而降低了计算复杂度从立方体积到线性积，保持了性能水平。Abstract
This paper explicitly models a coarse and noisy quantization in a communication system empowered by orthogonal time frequency space (OTFS) for cost and power efficiency. We first point out, with coarse quantization, the effective channel is imbalanced and thus no longer able to circularly shift the transmitted symbols along the delay-Doppler domain. Meanwhile, the effective channel is non-isotropic, which imposes a significant loss to symbol detection algorithms like the original approximate message passing (AMP). Although the algorithm of generalized expectation consistent for signal recovery (GEC-SR) can mitigate this loss, the complexity in computation is prohibitively high, mainly due to an dramatic increase in the matrix size of OTFS. In this context, we propose a low-complexity algorithm that incorporates into the GEC-SR a quick inversion of quasi-banded matrices, reducing the complexity from a cubic order to a linear order while keeping the performance at the same level.
摘要
In the system, the effective channel is imbalanced and non-isotropic due to coarse quantization, which leads to a significant loss in symbol detection algorithms such as the original approximate message passing (AMP). The GEC-SR algorithm can mitigate this loss, but the high computational complexity prohibits its use. The proposed algorithm addresses this issue by reducing the computational complexity while maintaining the performance.The key idea of the proposed algorithm is to incorporate a quick inversion of quasi-banded matrices into the GEC-SR method. This allows for a significant reduction in computational complexity, from a cubic order to a linear order, while maintaining the same performance. The proposed algorithm is designed to address the issues of coarse and noisy quantization in OTFS-based communication systems, and it has important implications for cost and power efficiency.
Systematic Design and Optimization of Quantum Circuits for Stabilizer Codes
results: 本文通过使用IBM Qiskit进行验证，提出了一种优化的八个量子比特（qubit）编码器，其中使用了18个CNOT门和4个 Hadamard门，相比之下，在先前的工作中只用了14个单量子门、33个二量子门和6个CCNOT门。此外，本文还提出了优化的斯坦内码编码器和13个量子比特编码器，以降低门数。Abstract
Quantum computing is an emerging technology that has the potential to achieve exponential speedups over their classical counterparts. To achieve quantum advantage, quantum principles are being applied to fields such as communications, information processing, and artificial intelligence. However, quantum computers face a fundamental issue since quantum bits are extremely noisy and prone to decoherence. Keeping qubits error free is one of the most important steps towards reliable quantum computing. Different stabilizer codes for quantum error correction have been proposed in past decades and several methods have been proposed to import classical error correcting codes to the quantum domain. However, formal approaches towards the design and optimization of circuits for these quantum encoders and decoders have so far not been proposed. In this paper, we propose a formal algorithm for systematic construction of encoding circuits for general stabilizer codes. This algorithm is used to design encoding and decoding circuits for an eight-qubit code. Next, we propose a systematic method for the optimization of the encoder circuit thus designed. Using the proposed method, we optimize the encoding circuit in terms of the number of 2-qubit gates used. The proposed optimized eight-qubit encoder uses 18 CNOT gates and 4 Hadamard gates, as compared to 14 single qubit gates, 33 2-qubit gates, and 6 CCNOT gates in a prior work. The encoder and decoder circuits are verified using IBM Qiskit. We also present optimized encoder circuits for Steane code and a 13-qubit code in terms of the number of gates used.
摘要
量子计算是一种emerging技术，它可以实现对于类传统计算机的快速增长。为了实现量子优势，量子原理被应用到通信、信息处理和人工智能等领域。然而，量子计算机面临一个fundamental问题，那就是量子比特（qubits）具有极高的噪声和失去稳定性。保持qubits错误自由是量子计算的重要步骤。过去几十年，有多种稳定码为量子错误 corrections proposed，但 formal方法 towards the design and optimization of circuits for these quantum encoders and decoders have not been proposed.在这篇论文中，我们提出了一种系统的建构方法 для普通的稳定码编码电路。这种方法用于设计编码和解码电路 для八个量子比特的代码。然后，我们提出了一种系统的优化方法，用于优化所设计的编码电路。使用这种方法，我们优化了编码电路，使其使用的两个量子比特门的数量减少为18个CNOT门和4个 Hadamard门，与之前的14个单量子比特门、33个二量子比特门和6个CCNOT门相比。我们使用IBM Qiskit验证了编码和解码电路。此外，我们还提出了优化后的八个量子比特编码电路、Steane代码和13个量子比特代码的优化结果。
Deep Learning Meets Swarm Intelligence for UAV-Assisted IoT Coverage in Massive MIMO
for: This study is written for UAV-assisted multi-user massive multiple-input multiple-output (MU-mMIMO) systems, specifically for Internet-of-Things (IoT) users.
methods: The study uses a joint optimization problem of hybrid beamforming (HBF), UAV relay positioning, and power allocation (PA) to maximize the total achievable rate (AR) for multiple IoT users. The study also adopts a geometry-based millimeter-wave (mmWave) channel model for both links and proposes three different swarm intelligence (SI)-based algorithmic solutions to optimize.
results: The study shows that the proposed algorithmic solutions can attain higher capacity and reduce average delay for delay-constrained transmissions in a UAV-assisted MU-mMIMO IoT systems. Additionally, the proposed J-HBF-DLLPA can closely approach the optimal capacity while significantly reducing the runtime by 99%, which makes the DL-based solution a promising implementation for real-time online applications in UAV-assisted MU-mMIMO IoT systems.Here is the result in Simplified Chinese text:
results: 研究表明，提出的算法解决方案可以在UAV协助MU-mMIMO IoT系统中实现更高的容量和减少延迟。此外，提出的J-HBF-DLLPA可以准确地预测UAV的位置和优化的功率值，以实现最大化AR。Abstract
This study considers a UAV-assisted multi-user massive multiple-input multiple-output (MU-mMIMO) systems, where a decode-and-forward (DF) relay in the form of an unmanned aerial vehicle (UAV) facilitates the transmission of multiple data streams from a base station (BS) to multiple Internet-of-Things (IoT) users. A joint optimization problem of hybrid beamforming (HBF), UAV relay positioning, and power allocation (PA) to multiple IoT users to maximize the total achievable rate (AR) is investigated. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and proposes three different swarm intelligence (SI)-based algorithmic solutions to optimize: 1) UAV location with equal PA; 2) PA with fixed UAV location; and 3) joint PA with UAV deployment. The radio frequency (RF) stages are designed to reduce the number of RF chains based on the slow time-varying angular information, while the baseband (BB) stages are designed using the reduced-dimension effective channel matrices. Then, a novel deep learning (DL)-based low-complexity joint hybrid beamforming, UAV location and power allocation optimization scheme (J-HBF-DLLPA) is proposed via fully-connected deep neural network (DNN), consisting of an offline training phase, and an online prediction of UAV location and optimal power values for maximizing the AR. The illustrative results show that the proposed algorithmic solutions can attain higher capacity and reduce average delay for delay-constrained transmissions in a UAV-assisted MU-mMIMO IoT systems. Additionally, the proposed J-HBF-DLLPA can closely approach the optimal capacity while significantly reducing the runtime by 99%, which makes the DL-based solution a promising implementation for real-time online applications in UAV-assisted MU-mMIMO IoT systems.
摘要
To solve this optimization problem, the study proposes three different swarm intelligence (SI)-based algorithmic solutions:1. UAV location with equal PA2. PA with fixed UAV location3. Joint PA with UAV deploymentThe radio frequency (RF) stages are designed to reduce the number of RF chains based on slow time-varying angular information, while the baseband (BB) stages are designed using reduced-dimension effective channel matrices.Furthermore, a novel deep learning (DL)-based low-complexity joint hybrid beamforming, UAV location, and power allocation optimization scheme (J-HBF-DLLPA) is proposed. This scheme consists of an offline training phase and an online prediction of UAV location and optimal power values to maximize AR.The illustrative results show that the proposed algorithmic solutions can achieve higher capacity and reduce average delay for delay-constrained transmissions in a UAV-assisted MU-mMIMO IoT system. Additionally, the proposed J-HBF-DLLPA can closely approach the optimal capacity while significantly reducing the runtime by 99%, making it a promising implementation for real-time online applications in UAV-assisted MU-mMIMO IoT systems.
Resource Allocation for Semantic-Aware Mobile Edge Computing Systems
results: 通过对非对称的原始问题进行几何编程变换，并使用交互优化算法解决，得到了最优解。此外，closed-form的语义提取因子的优化解也是 derive。对比 benchmark algorithm without semantic-aware allocation，提出的算法可以减少最大执行延迟达37.10%。同时，在大任务大小和Poor channel condition下，小语义提取因子被首选。Abstract
In this paper, a semantic-aware joint communication and computation resource allocation framework is proposed for mobile edge computing (MEC) systems. In the considered system, each terminal device (TD) has a computation task, which needs to be executed by offloading to the MEC server. To further decrease the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the large-size raw data. An optimization problem of joint semantic-aware division factor, communication and computation resource management is formulated. The problem aims to minimize the maximum execution delay of all TDs while satisfying energy consumption constraints. The original non-convex problem is transformed into a convex one based on the geometric programming and the optimal solution is obtained by the alternating optimization algorithm. Moreover, the closed-form optimal solution of the semantic extraction factor is derived. Simulation results show that the proposed algorithm yields up to 37.10% delay reduction compared with the benchmark algorithm without semantic-aware allocation. Furthermore, small semantic extraction factors are preferred in the case of large task sizes and poor channel conditions.
摘要
在本文中，一种基于 semantics 的集成通信和计算资源分配框架被提出用于移动边缘计算（MEC）系统。系统中每个终端设备（TD）都有一个计算任务，需要通过卸载到 MEC 服务器进行执行。为了进一步减少传输负担，每个 TD 将小型的抽取 semantic 信息发送到服务器，而不是大量的原始数据。一个协调semantic-aware分配因子、通信和计算资源管理的优化问题被形ulated。该问题的目标是 minimize 所有 TD 的执行延迟最大值，同时满足能量消耗限制。原始的非泛合函数问题被转化为一个卷积函数问题，并通过卷积编程得到了优化解决方案。此外，closed-form 优化解决方案的 semantic 抽取因子也被 derivation。 simulation 结果表明，提出的算法可以减少最多 37.10% 的延迟，相比 Without semantic-aware 分配算法。此外，小的 semantic 抽取因子在大任务大小和差annels 条件下被首选。
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network
paper_authors: Wei-En Yu, Jinwei Sun, Shiping Zhang, Xiaoge Zhang, Jing-Xiao Liao for: 这个论文旨在提高深度学习方法对故障诊断中的表现，特别是在面临高度不均衡或长尾数据时。methods: 该论文提出了一种监督对比学习方法，使用类 weights 对决策函数进行调整，从而提高神经网络对故障诊断的特征提取能力。results: 实验结果表明，与 State-of-the-Art 方法相比，CCQNet 在面临高度不均衡或长尾数据时表现明显更好，可以更好地识别故障。Abstract
Deep learning has achieved remarkable success in bearing fault diagnosis. However, its performance oftentimes deteriorates when dealing with highly imbalanced or long-tailed data, while such cases are prevalent in industrial settings because fault is a rare event that occurs with an extremely low probability. Conventional data augmentation methods face fundamental limitations due to the scarcity of samples pertaining to the minority class. In this paper, we propose a supervised contrastive learning approach with a class-aware loss function to enhance the feature extraction capability of neural networks for fault diagnosis. The developed class-weighted contrastive learning quadratic network (CCQNet) consists of a quadratic convolutional residual network backbone, a contrastive learning branch utilizing a class-weighted contrastive loss, and a classifier branch employing logit-adjusted cross-entropy loss. By utilizing class-weighted contrastive loss and logit-adjusted cross-entropy loss, our approach encourages equidistant representation of class features, thereby inducing equal attention on all the classes. We further analyze the superior feature extraction ability of quadratic network by establishing the connection between quadratic neurons and autocorrelation in signal processing. Experimental results on public and proprietary datasets are used to validate the effectiveness of CCQNet, and computational results reveal that CCQNet outperforms SOTA methods in handling extremely imbalanced data substantially.
摘要
深度学习在滤波器疾病诊断中实现了很大的成功。然而，它在面临高度不均衡或长尾数据时表现不佳，这些情况在工业场景中却很普遍，因为疾病是一种非常罕见的事件，发生概率非常低。传统的数据扩展方法受到罕见类样本的缺乏的限制。在这篇论文中，我们提出了一种Supervised Contrastive Learning方法，使得神经网络在疾病诊断中提高特征提取能力。我们的方法包括一个quadratic convolutional residual network底层、一个使用类Weighted Contrastive Loss的对比学分支、以及一个使用Logit-adjusted Cross-Entropy Loss的分类分支。通过使用类Weighted Contrastive Loss和Logit-adjusted Cross-Entropy Loss，我们的方法促进了类别特征之间的等距耦合，从而使神经网络对所有类型的特征具有平等的注意力。我们还分析了quadratic neuron的特点，并将其与自相关函数的应用相连接，以证明quadratic neuron在信号处理中的优势。实验结果表明，CCQNet在面临高度不均衡数据时表现出了显著的优势，与SOTA方法相比，CCQNet在执行滤波器疾病诊断方面具有显著的改进。