results: 该论文通过实验结果表明，提出的算法可以最大化系统吞吐率，同时保证系统公平性，并且在实际网络约束下（如能源消耗和延迟）下实现分布式实现。Abstract
Extended reality (XR) applications often perform resource-intensive tasks, which are computed remotely, a process that prioritizes the latency criticality aspect. To this end, this paper shows that through leveraging the power of the central cloud (CC), the close proximity of edge computers (ECs), and the flexibility of uncrewed aerial vehicles (UAVs), a UAV-aided hybrid cloud/mobile-edge computing architecture promises to handle the intricate requirements of future XR applications. In this context, this paper distinguishes between two types of XR devices, namely, strong and weak devices. The paper then introduces a cooperative non-orthogonal multiple access (Co-NOMA) scheme, pairing strong and weak devices, so as to aid the XR devices quality-of-user experience by intelligently selecting either the direct or the relay links toward the weak XR devices. A sum logarithmic-rate maximization problem is, thus, formulated so as to jointly determine the computation and communication resources, and link-selection strategy as a means to strike a trade-off between the system throughput and fairness. Subject to realistic network constraints, e.g., power consumption and delay, the optimization problem is then solved iteratively via discrete relaxations, successive-convex approximation, and fractional programming, an approach which can be implemented in a distributed fashion across the network. Simulation results validate the proposed algorithms performance in terms of log-rate maximization, delay-sensitivity, scalability, and runtime performance. The practical distributed Co-NOMA implementation is particularly shown to offer appreciable benefits over traditional multiple access and NOMA methods, highlighting its applicability in decentralized XR systems.
摘要
现实扩展（XR）应用程序通常执行资源密集的任务，这些任务通常在远程计算，以优先级顺序处理。为了实现这一目标，这篇论文提出了一种通过中央云（CC）、边缘计算（EC）和无人机（UAV）的 гибрид云/边缘计算架构来处理未来XR应用程序的复杂需求。在这个上下文中，这篇论文将XR设备分为两类：强设备和弱设备。论文然后引入了合作非对称多访问（Co-NOMA）方案，将强设备和弱设备相互协作，以提高XR设备用户体验质量。为了提高系统吞吐量和公平性，论文提出了一个总日志arithmic-rate最大化问题，以联合确定计算和通信资源，以及链接选择策略。充分考虑了现实网络约束，例如电力消耗和延迟，优化问题可以通过抽象relaxation、Successive-Convex Approximation和分数程序来解决。实际应用中，这种分布式Co-NOMA实现可以提供较高的日志率最大化、延迟敏感度、可扩展性和运行时性能。
Decomposition Based Interference Management Framework for Local 6G Networks
results: 对于两种基准算法，提议的序列-到-一个变换器模型显示了其 robustness 性。与基准方案相比，提议方案可以降低平均квадратиче差误差值（RMSE）值，最高降低55%。Abstract
Managing inter-cell interference is among the major challenges in a wireless network, more so when strict quality of service needs to be guaranteed such as in ultra-reliable low latency communications (URLLC) applications. This study introduces a novel intelligent interference management framework for a local 6G network that allocates resources based on interference prediction. The proposed algorithm involves an advanced signal pre-processing technique known as empirical mode decomposition followed by prediction of each decomposed component using the sequence-to-one transformer algorithm. The predicted interference power is then used to estimate future signal-to-interference plus noise ratio, and subsequently allocate resources to guarantee the high reliability required by URLLC applications. Finally, an interference cancellation scheme is explored based on the predicted interference signal with the transformer model. The proposed sequence-to-one transformer model exhibits its robustness for interference prediction. The proposed scheme is numerically evaluated against two baseline algorithms, and is found that the root mean squared error is reduced by up to 55% over a baseline scheme.
摘要
管理间细胞干扰是无线网络中的一个主要挑战，尤其是在需要保证严格的服务质量，如在超低延迟低功率通信（URLLC）应用中。本研究提出了一种新的智能干扰管理框架，用于本地6G网络资源分配。该算法包括一种高级的信号预处理技术known as empirical mode decomposition，然后使用序列到一转换器算法预测每个分解成分。预测的干扰功率然后用于估算未来信号干扰 plus noise ratio，并在保证URLLC应用所需的高可靠性的情况下分配资源。最后，基于预测的干扰信号，探讨了一种干扰抵消方案，使用转换器模型。提出的序列到一转换器模型在干扰预测中展现了其强健性。与两个基线算法进行比较，研究发现，使用该方案可以将根mean squared error降低到55%以下。
Computation-Limited Signals: A Channel Capacity Regime Constrained by Computational Complexity
results: 作者通过设置此指标为函数Channel资源，分类了一个给定的信号设计是comp-limited的。作者还提供了一个使用例子，表明无线OFDM传输器是comp-limited， Unless the lower-bound计算复杂度 of N-point DFT问题为 $\Omega(N)$，这是计算机科学中的一个开放问题。Abstract
In this letter, we introduce the computational-limited (comp-limited) signals, a communication capacity regime in which the signal time computational complexity overhead is the key constraint -- rather than power or bandwidth -- to the overall communication capacity. To relate capacity and time complexity, we propose a novel mathematical framework that builds on concepts of information theory and computational complexity. In particular, the algorithmic capacity stands for the ratio between the upper-bound number of bits modulated in a symbol and the lower-bound time complexity required to turn these bits into a communication symbol. By setting this ratio as function of the channel resources, we classify a given signal design as comp-limited if its algorithmic capacity nullifies as the channel resources grow. As a use-case, we show that an uncoded OFDM transmitter is comp-limited unless the lower-bound computational complexity of the N-point DFT problem verifies as $\Omega(N)$, which remains an open challenge in theoretical computer science.
摘要
文中，我们介绍了计算限制（comp-limited）信号，它是通信容量 Régime 中的一个条件，其中信号时间计算复杂度成本是主要的限制因素，而不是功率或带宽。为了将容量和时间复杂度相关联，我们提出了一个新的数学框架，基于信息理论和计算复杂度。具体来说，算法容量表示每个符号中模ulated的最高位数与转化这些位数为通信符号所需的最低时间复杂度之比。通过将这个比率设置为通道资源函数，我们可以将一个给定的信号设计分类为comp-limited。作为一个使用情况，我们显示了一个未编码的OFDM发送器是comp-limited， Unless the lower-bound computational complexity of the N-point DFT problem verifies as $\Omega(N)$, which remains an open challenge in theoretical computer science.Note: "计算限制" (comp-limited) is a term used to describe a communication system where the computational complexity of the signal processing is the primary limiting factor, rather than power or bandwidth.
Physical Layer Security in a Private 5G Network for Industrial and Mobility Application
paper_authors: Shivraj Hanumant Gonde, Christoph Frisch, Svetoslav Duhovnikov, Martin Kubisch, Thomas Meyerhoff, Dominic Schupke
for: This paper is written for organizations that operate Private 5G networks in industrial environments, particularly those that require secure communication between devices.
methods: The paper uses Physical Layer Key Generation (PLKG) to generate a symmetric secret key between two nodes in the presence of a potential passive eavesdropper.
results: The paper demonstrates the establishment of a long-term symmetric key between an aerial vehicle and IT infrastructure in a manufacturing environment, using the radio interface of the Private 5G network.Abstract
Cellular communication technologies such as 5G are deployed on a large scale around the world. Compared to other communication technologies such as WiFi, Bluetooth, or Ultra Wideband, the 5G communication standard describes support for a large variety of use cases, e.g., Internet of Things, vehicular, industrial, and campus-wide communications. An organization can operate a Private 5G network to provide connectivity to devices in their manufacturing environment. Physical Layer Key Generation (PLKG) is a method to generate a symmetric secret on two nodes despite the presence of a potential passive eavesdropper. To the best of our knowledge, this work is one of the first to implement PLKG in a real Private 5G network. Therefore, it highlights the possibility of integrating PLKG in the communication technology highly relevant for industrial applications. This paper exemplifies the establishment of a long-term symmetric key between an aerial vehicle and IT infrastructure both located in a manufacturing environment and communicating via the radio interface of the Private 5G network.
摘要
fifth-generation 无线通信技术（5G）在全球范围内大规模部署。相比其他通信技术，如 WiFi、蓝牙或超宽带，5G 通信标准支持各种使用场景，如物联网、交通、工业和校园通信。组织可以运行专用5G网络，以提供制造环境中设备的连接性。物理层密钥生成（PLKG）是一种生成两个节点之间的同步密钥，即使存在可能的潜在窃听者。根据我们所知，这是首次在实际专用5G网络中实现PLKG。因此，它高亮了在工业应用中集成PLKG的可能性。这篇论文示例了在制造环境中的空中车和信息基础设施之间通过专用5G网络的广播 интер脑界面建立长期同步密钥。
MEDUSA: Scalable Biometric Sensing in the Wild through Distributed MIMO Radars
results: 这个研究获得了20%的平均提升，相比于使用商业激光感知器的现有系统。这证明了MEDUSA的空间多标优点，包括目标和环境动态的监测在 familier和未知内部环境中。Abstract
Radar-based techniques for detecting vital signs have shown promise for continuous contactless vital sign sensing and healthcare applications. However, real-world indoor environments face significant challenges for existing vital sign monitoring systems. These include signal blockage in non-line-of-sight (NLOS) situations, movement of human subjects, and alterations in location and orientation. Additionally, these existing systems failed to address the challenge of tracking multiple targets simultaneously. To overcome these challenges, we present MEDUSA, a novel coherent ultra-wideband (UWB) based distributed multiple-input multiple-output (MIMO) radar system, especially it allows users to customize and disperse the $16 \times 16$ into sub-arrays. MEDUSA takes advantage of the diversity benefits of distributed yet wirelessly synchronized MIMO arrays to enable robust vital sign monitoring in real-world and daily living environments where human targets are moving and surrounded by obstacles. We've developed a scalable, self-supervised contrastive learning model which integrates seamlessly with our hardware platform. Each attention weight within the model corresponds to a specific antenna pair of Tx and Rx. The model proficiently recovers accurate vital sign waveforms by decomposing and correlating the mixed received signals, including comprising human motion, mobility, noise, and vital signs. Through extensive evaluations involving 21 participants and over 200 hours of collected data (3.75 TB in total, with 1.89 TB for static subjects and 1.86 TB for moving subjects), MEDUSA's performance has been validated, showing an average gain of 20% compared to existing systems employing COTS radar sensors. This demonstrates MEDUSA's spatial diversity gain for real-world vital sign monitoring, encompassing target and environmental dynamics in familiar and unfamiliar indoor environments.
摘要
采用雷达技术探测生命 Parameters 已经展示了不间断无接触的生命参数监测和医疗应用的搭建。然而，现实世界室内环境对现有生命参数监测系统带来了重大挑战。这些挑战包括雷达信号屏蔽（NLOS）情况下的信号干扰、人体活动的移动和位置和方向的变化。此外，现有系统无法同时跟踪多个目标。为了解决这些挑战，我们提出了MEDUSA，一种新的干扰频率ultra-wideband（UWB）基于分布式多输入多输出（MIMO）雷达系统。MEDUSA利用分布式 yet wirelessly synchronized MIMO数组的多样性优势，以实现robust生命参数监测在现实生活环境中， где人类目标在移动并围绕障碍物。我们开发了一种可扩展的自适应强化学习模型，该模型与我们的硬件平台集成了良好。每个注意力量在模型中对应于特定的天线对（Tx和Rx）。模型能够高效地提取生命参数波形，通过分解和相关处理混合接收信号，包括人体运动、 mobilicity、噪声和生命参数。经过了21名参与者和超过200小时的数据收集（总共3.75TB，其中1.89TB为静止目标和1.86TB为移动目标），MEDUSA的性能已经被验证，显示与现有系统使用商业雷达传感器相比，MEDUSA具有20%的平均提升。这表明MEDUSA在实际世界中具有空间多样性增强，包括目标和环境动态在 familiarn和未知室内环境中。
Affine Frequency Division Multiplexing With Index Modulation
results: 该论文通过closed-form的极限紧张upper bound来证明IM方案的性能，并通过计算机实验证明了该方案的优越性。results show that index bits have stronger diversity protection than modulated bits even when the full diversity condition of AFDM is not satisfied.Abstract
Affine frequency division multiplexing (AFDM) is a new multicarrier technique based on chirp signals tailored for high-mobility communications, which can achieve full diversity. In this paper, we propose an index modulation (IM) scheme based on the framework of AFDM systems, named AFDM-IM. In the proposed AFDM-IM scheme, the information bits are carried by the activation state of the subsymbols in discrete affine Fourier (DAF) domain in addition to the conventional constellation symbols. To efficiently perform IM, we divide the subsymbols in DAF domain into several groups and consider both the localized and distributed strategies. An asymptotically tight upper bound on the average bit error rate (BER) of the maximum-likelihood detection in the existence of channel estimation errors is derived in closed-form. Computer simulations are carried out to evaluate the performance of the proposed AFDM-IM scheme, whose results corroborate its superiority over the benchmark schemes in the linear time-varying channels. We also evaluate the BER performance of the index and modulated bits for the AFDM-IM scheme with and without satisfying the full diversity condition of AFDM. The results show that the index bits have a stronger diversity protection than the modulated bits even when the full diversity condition of AFDM is not satisfied.
摘要
“Affine频率分多普通方式”（AFDM）是一种基于滑动信号的新多个 carriers 技术，适用于高移动通信，可以实现全多态性。在这篇论文中，我们提出了一个基于 AFDM 系统框架的指标修征（IM）方案，称为 AFDM-IM。在我们的提案中，信息位元被传递到 AFDM 系统中的几个批次中，并且在这些批次中使用传统的折衣符号。为了有效地实现 IM，我们在 DAF 领域中分割 subsymbols 成多个群体，并考虑了本地化和分散的两种策略。我们 derive 了一个对应于最大可能性探测的对应几何率（BER）的封闭式上界，并将其与 computer simulations 进行评估。结果显示，我们的 AFDM-IM 方案在线性时间变化频率对应于更高的性能。我们还评估了 AFDM-IM 方案中的指标位元和修征位元的 BER 性能，并发现指标位元在 AFDM 的全多态性不满足时仍然具有更强的多态保护。
Waveform Design for MIMO-OFDM Integrated Sensing and Communication System: An Information Theoretical Approach
results: 优化结果通过Monte Carlo伪陷 simulations进行验证。本研究提供了有效的封闭式表达式，使得MIMO-OFDM ISAC系统能够实现平衡的感知和通信性能。Abstract
Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.
摘要
Integrated sensing and communication (ISAC) 被视为未来 fifth-generation advanced (5G-A) 和 sixth-generation (6G) 移动通信系统的关键技术。 ISAC 波形设计是 ISAC 系统的关键。然而，传感和通信性能的不同会对 ISAC 波形设计带来挑战。本文使用信息理论中的共聚性指标（MI）来度量传感和通信性能。在多个输入多个输出的orthogonal frequency division multiplexing (MIMO-OFDM) ISAC 系统中，我们首先计算传感和通信 MI 的相互关系。然后，我们提出了最佳波形设计，以最大化传感 MI、通信 MI 和权重总和传感和通信 MI。我们的工作提供了有效的关闭式表达式，使得 MIMO-OFDM ISAC 系统可以实现平衡的传感和通信性能。Note: Please note that the translation is in Simplified Chinese, and the word order and grammar may be different from the original text.
A Stochastic Particle Variational Bayesian Inference Inspired Deep-Unfolding Network for Non-Convex Parameter Estimation
results: 实验结果显示，LPSPVBI 算法在无线感知应用中的参数估计比现有方法高精度。Abstract
Future wireless networks are envisioned to provide ubiquitous sensing services, which also gives rise to a substantial demand for high-dimensional non-convex parameter estimation, i.e., the associated likelihood function is non-convex and contains numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and reasoning with prior information, but poses a long-standing challenge on computing intractable posteriori distributions. Most existing variational methods generally rely on assumptions about specific distribution families to derive closed-form solutions, and are difficult to apply in high-dimensional, non-convex scenarios. Given these challenges, firstly, we propose a parallel stochastic particle variational Bayesian inference (PSPVBI) algorithm. Thanks to innovations such as particle approximation, additional updates of particle positions, and parallel stochastic successive convex approximation (PSSCA), PSPVBI can flexibly drive particles to fit the posteriori distribution with acceptable complexity, yielding high-precision estimates of the target parameters. Furthermore, additional speedup can be obtained by deep-unfolding (DU) the PSPVBI algorithm. Specifically, superior hyperparameters are learned to dramatically reduce the number of algorithmic iterations. In this PSPVBI-induced Deep-Unfolding Networks, some techniques related to gradient computation, data sub-sampling, differentiable sampling, and generalization ability are also employed to facilitate the practical deployment. Finally, we apply the LPSPVBI to solve several important parameter estimation problems in wireless sensing scenarios. Simulations indicate that the LPSPVBI algorithm outperforms existing solutions.
摘要
将来的无线网络将提供 ubique 感知服务，导致高维非拟合参数估计的巨大需求，即相关的可能函数是非拟合的和含有多个局部最优点。基本 Bayesian 推理 (VB) 提供了模拟复杂估计问题和使用先验信息进行理据处理的强大工具，但计算不可靠的后验分布却成为了长期挑战。大多数现有的变量方法通常假设特定的分布家族，从而得到关闭式解决方案，而在高维、非拟合情况下困难应用。为解决这些挑战，我们首先提出了并行随机粒子变量 Bayesian 推理（PSPVBI）算法。因为增加了粒子方法、粒子位置更新和并行随机Successive Convex Approximation（PSSCA）等创新，PSPVBI可以灵活地使粒子适应 posteriori 分布，得到高精度的参数估计。此外，通过深度 unfolding（DU）的技术，我们可以进一步提高算法的速度。具体来说，我们通过学习超过参数，减少算法迭代数量，实现了在 PSPVBI 中的深度 unfolding。在 PSPVBI induced Deep-Unfolding Networks 中，我们还使用了一些相关的梯度计算、数据子抽样、可导采样和通用能力等技术，以便实际应用。最后，我们通过 LPSPVBI 算法解决了无线感知场景中的一些重要参数估计问题。 simulation 结果表明，LPSPVBI 算法在现有解决方案中具有优势。
Distortion-Aware Phase Retrieval Receiver for High-Order QAM Transmission with Carrierless Intensity-Only Measurements
paper_authors: Hanzi Huang, Haoshuo Chen, Qi Gao, Yetian Huang, Nicolas K. Fontaine, Mikael Mazur, Lauren Dallachiesa, Roland Ryf, Zhengxuan Li, Yingxiong Song
for: investigate high-order quadrature amplitude modulation (QAM) signals transmission with carrierless and intensity-only measurements, and improve precision of phase retrieval (PR) algorithm.
methods: propose distortion-aware PR scheme with training and reconstruction stages, estimate and emulate distortion caused by channel impairments, improve agreement between estimated and measured amplitudes.
results: experimentally demonstrate 50-GBaud 16QAM and 32QAM signals transmission over 40km and 80km SSMF spans, achieve BERs below 6.25% HD-FEC and 25% SD-FEC thresholds, and achieve post-FEC data rate of up to 140 Gb/s with optimal pilot symbol ratio of 20%.Abstract
We experimentally investigate transmitting high-order quadrature amplitude modulation (QAM) signals with carrierless and intensity-only measurements with phase retrieval (PR) receiving techniques. The intensity errors during measurement, including noise and distortions, are found to be a limiting factor for the precise convergence of the PR algorithm. To improve the PR reconstruction accuracy, we propose a distortion-aware PR scheme comprising both training and reconstruction stages. By estimating and emulating the distortion caused by various channel impairments, the proposed scheme enables enhanced agreement between the estimated and measured amplitudes throughout the PR iteration, thus resulting in improved reconstruction performance to support high-order QAM transmission. With the aid of proposed techniques, we experimentally demonstrate 50-GBaud 16QAM and 32QAM signals transmitting through a standard single-mode optical fiber (SSMF) span of 40 and 80 km, and achieve bit error rates (BERs) below the 6.25% hard decision (HD)-forward error correction (FEC) and 25% soft decision (SD)-FEC thresholds for the two modulation formats, respectively. By tuning the pilot symbol ratio and applying concatenated coding, we also demonstrate that a post-FEC data rate of up to 140 Gb/s can be achieved for both distances at an optimal pilot symbol ratio of 20%.
摘要
我们实验性地研究了在无载波和强度仅测量下传输高阶 quadrature amplitude modulation（QAM）信号。测量过程中的强度错误，包括噪声和扭曲，被发现是精度恢复 алгоритм的限制因素。为了提高恢复精度，我们提议了一种考虑到频率响应的扭曲恢复方案，包括训练和重建两个阶段。通过估算和模拟各种通道缺陷所引起的扭曲，该方案可以在PR迭代过程中实现更好的吻合，从而提高恢复性能，以支持高阶QAM传输。通过我们的技术，我们实验性地在标准单模光纤（SSMF） span 40和80公里上传输了50Gbps 16QAM和32QAM信号，并在这两种模ulation format中达到了Below the 6.25% hard decision（HD）forward error correction（FEC）和25% soft decision（SD）FEC的下限。通过调整示例符号比例和 concatenated coding，我们还示出了在这两个距离上达到140Gb/s的后FEC数据速率。