paper_authors: Adarsha M, S. Malathi, Santosh Kumar for: 这种研究旨在调查固定格式网络在不同的调制格式下的性能，以估算系统的容量和spectral efficiency。methods: 该研究使用了光学干扰 quadrature modulator结构建立固定格式网络调制，并使用了homodyne探测方法。数据多路复用使用了极化分多路复用技术。results: 在这些情况下，使用不同的调制格式实现了100Gbps、150Gbps和200Gbps的数据速率。使用现代数字信号处理系统，对于PM-QPSK、PM-8QAM和PM-16QAM等不同的调制方式，分别实现了2、3和4位/s/Hz的spectrum efficiency。不同的调制方式每种系统容量分别为8-9、12-13.5和16-18Tbps，可以在3000、1300和700公里的传输距离上实现接受比特错误率低于等于2*10-3。Abstract
In this article, the performance of a fixed grid network is examined for various modulation formats to estimate the system's capacity and spectral efficiency. The optical In-phase Quadrature Modulator structure is used to build a fixed grid network modulation, and the homodyne detection approach is used for the receiver. Data multiplexing is accomplished using the Polarization Division Multiplexed technology. 100 Gbps, 150 Gbps, and 200 Gbps data rates are transmitted under these circumstances utilizing various modulation formats. Various pre-processing and signal recovery steps are explained by using modern digital signal processing systems. The achieved spectrum efficiencies for PM-QPSK, PM-8 QAM, and PM-16 QAM, respectively, were 2, 3, and 4 bits/s/Hz. Different modulation like PM-QPSK, PM-8-QAM, and PM-16-QAM each has system capacities of 8-9, 12-13.5, and 16-18 Tbps and it reaches transmission distances of 3000, 1300, and 700 kilometers with acceptable Bit Error Rate less than equal to 2*10-3 respectively. Peak optical power for received signal detection and full width at half maximum is noted for the different modulations under a fixed grind network.
摘要
在这篇文章中，我们研究了固定格网络的性能，用以估算系统的容量和 spectral efficiency。我们使用了光学干扰 quadrature modulator 结构来建立固定格网络的模ulation，并使用了 homodyne 探测方法来接收器。数据多路复用使用了极化分 multiplexing 技术。在这些情况下，我们将100Gbps、150Gbps和200Gbps的数据速率传输，使用不同的模ulation format。我们还介绍了使用现代数字信号处理系统来实现预处理和信号恢复步骤。我们测得了 PM-QPSK、PM-8QAM 和 PM-16QAM 模ulation的spectrum efficiency分别为2、3和4 bits/s/Hz。不同的模ulation各自有系统容量为8-9、12-13.5和16-18 Tbps，可以在3000、1300和700公里的传输距离上实现可接受的 Bit Error Rate 低于等于2*10-3。我们还注意到了不同模ulation的接收信号检测的峰值光学功率和半宽度。
Enhancing Secrecy in UAV RSMA Networks: Deep Unfolding Meets Deep Reinforcement Learning
paper_authors: Abuzar B. M. Adam, Mohammed A. M. Elhassan
for: 这 paper 是研究多架空无人机（UAV）网络中的秘密率最大化问题。
methods: 这 paper 使用了一个组合的射频、速率分配和无人机轨迹优化问题，这个问题是非核心的。因此，它被转换为一个Markov问题，并使用了一个新的多代理深度学习（DRL）框架，称为DUN-DRL。
results: 该 paper 的结果显示，DUN-DRL 比其他 literatura 中的 DRL 方法表现更好，并且可以实现更高的秘密率。Abstract
In this paper, we consider the maximization of the secrecy rate in multiple unmanned aerial vehicles (UAV) rate-splitting multiple access (RSMA) network. A joint beamforming, rate allocation, and UAV trajectory optimization problem is formulated which is nonconvex. Hence, the problem is transformed into a Markov decision problem and a novel multiagent deep reinforcement learning (DRL) framework is designed. The proposed framework (named DUN-DRL) combines deep unfolding to design beamforming and rate allocation, data-driven to design the UAV trajectory, and deep deterministic policy gradient (DDPG) for the learning procedure. The proposed DUN-DRL have shown great performance and outperformed other DRL-based methods in the literature.
摘要
在这篇论文中，我们考虑了多架空航天器（UAV）率分访问（RSMA）网络中的最大秘密率增加问题。我们将这个问题转化为一个非核心的Markov决策问题，并提出了一种基于多代理深度学习（DRL）的新框架（名为DUN-DRL）。该框架结合深度嵌入设计灵敏谱和率分配，基于数据驱动设计UAV轨迹，并使用深度确定策略梯度（DDPG）进行学习过程。我们的DUN-DRL方法在文献中表现出色，并比其他基于DRL的方法表现更好。
RIS-aided Near-Field MIMO Communications: Codebook and Beam Training Design
results: 比较研究表明，提出的扫描方案可以实现近似优化性，同时减少培训负担；相比于只考虑角度信息，将距离信息包含在扫描过程中可以明显提高可得率。Abstract
Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information contained in the received signals, 1) a distance-based codebook is designed for near-field MIMO channels, based on which a hierarchical beam training scheme is proposed to reduce the training overhead; 2) a combined angular-distance codebook is designed for mixed-far-near-field MIMO channels, based on which a two-stage beam training scheme is proposed to achieve alignment in the angular and distance domains separately. For maximizing the achievable rate while reducing the complexity, an alternating optimization algorithm is proposed to carry out the joint optimization iteratively. Specifically, the RIS coefficient matrix is optimized through the beam training process, the optimal combining matrix is obtained from the closed-form solution for the mean square error (MSE) minimization problem, and the active beamforming matrix is optimized by exploiting the relationship between the achievable rate and MSE. Numerical results reveal that: 1) the proposed beam training schemes achieve near-optimal performance with a significantly decreased training overhead; 2) compared to the angular-only far-field channel model, taking the additional distance information into consideration will effectively improve the achievable rate when carrying out beam design for near-field communications.
摘要
下链接智能表面（RIS）助enciphered多输入多输出（MIMO）系统被考虑，包括远场、近场和混合远近场通道。根据接收信号中的角度或距离信息，提出了以下方案：1. 基于近场MIMO通道的距离编码本是基于近场MIMO通道的距离编码本，提出了层次扫描训练方案，以减少训练负担。2. 基于混合远近场MIMO通道的混合角度距离编码本是基于混合远近场MIMO通道的混合角度距离编码本，提出了两stage扫描训练方案，以在angular和距离域分别实现对alignment。3. 为了最大化可达速率而减少复杂性，提出了alternating optimization算法，以iteratively进行共享优化。具体来说，RIS减少系数矩阵通过扫描训练过程进行优化，可能性最小化问题中的closed-form解决方案中得到最佳组合矩阵，并通过利用可达速率和MSE之间的关系来优化活动扩散矩阵。数据示出：1. 提出的扫描训练方案可以实现近似最佳性，同时减少训练负担；2. 在进行 beam设计时，考虑到距离信息的情况下，与angular-only远场通道模型相比，能够更有效地提高可达速率。
RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications
results: 论文提供了 RIS-aided CF mMIMO 无线通信系统的全面检讨，包括系统体系和应用场景、通信协议和channel模型等方面的概述，以及系统操作和资源分配等方面的深入分析。Abstract
An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.
摘要
sixth-generation（6G）网络面临更高的要求和挑战，如高频率效率和能效性、超低延迟和超高可靠性。各种技术，如无绳（CF）大量多输入多输出（mMIMO）和智能反射表面（RIS），也被认为是应对这些前所未有的挑战的两种有前途的技术。 Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.
Identifying Distribution Network Faults Using Adaptive Transition Probability
paper_authors: Xinliang Ma, Weihua Liu, Bingying Jin
for: 提高分布网络中故障检测精度
methods: combines adaptive probability learning和波形分解，用于优化特征相似性
results: 实验结果表明，该方法在使用适应学习条件时，能够超越常用的分类模型，如卷积神经网络、支持向量机和k-最近邻居分类器，特别是在限制样本大小的情况下。Abstract
A novel approach is suggested for improving the accuracy of fault detection in distribution networks. This technique combines adaptive probability learning and waveform decomposition to optimize the similarity of features. Its objective is to discover the most appropriate linear mapping between simulated and real data to minimize distribution differences. By aligning the data in the same feature space, the proposed method effectively overcomes the challenge posed by limited sample size when identifying faults and classifying real data in distribution networks. Experimental results utilizing simulated system data and real field data demonstrate that this approach outperforms commonly used classification models such as convolutional neural networks, support vector machines, and k-nearest neighbors, especially under adaptive learning conditions. Consequently, this research provides a fresh perspective on fault detection in distribution networks, particularly when adaptive learning conditions are employed.
摘要
一种新的方法建议用于改进分布网络中故障探测精度。这种技术结合适应概率学习和波形分解，以优化特征相似性。其目标是找到最适合的线性映射，使得实际数据和模拟数据在同一个特征空间内进行对应。通过对数据进行对应，该方法有效地超越了有限样本大小的挑战，使得在分布网络中探测FAULTS和分类实际数据时更加准确。实验结果表明，该方法在使用适应学习条件时，可以超越常用的分类模型，如卷积神经网络、支持向量机和k-最近邻居等，尤其是在适应学习条件下。因此，这种研究提供了一个新的视角，用于分布网络中的故障探测，特别是在使用适应学习条件时。
Bayesian Approach for Adaptive EMG Pattern Classification Via Semi-Supervised Sequential Learning
results: 实验结果显示，提案的方法可以降低时间的类别精度下降，并且比传统方法高效。这些结果证明了提案的方法的有效性，并适用于实际应用中的 EMG 基本控制系统。Abstract
Intuitive human-machine interfaces may be developed using pattern classification to estimate executed human motions from electromyogram (EMG) signals generated during muscle contraction. The continual use of EMG-based interfaces gradually alters signal characteristics owing to electrode shift and muscle fatigue, leading to a gradual decline in classification accuracy. This paper proposes a Bayesian approach for adaptive EMG pattern classification using semi-supervised sequential learning. The proposed method uses a Bayesian classification model based on Gaussian distributions to predict the motion class and estimate its confidence. Pseudo-labels are subsequently assigned to data with high-prediction confidence, and the posterior distributions of the model are sequentially updated within the framework of Bayesian updating, thereby achieving adaptive motion recognition to alterations in signal characteristics over time. Experimental results on six healthy adults demonstrated that the proposed method can suppress the degradation of classification accuracy over time and outperforms conventional methods. These findings demonstrate the validity of the proposed approach and its applicability to practical EMG-based control systems.
摘要
人机界面可能会被开发为使用模式识别来估算执行人类动作的电omyogram (EMG) 信号。这些信号在长期使用EMG-based interface时会逐渐改变特征，这是由于电极移动和肌肉疲劳引起的，导致分类精度逐渐下降。本文提出了一种使用 Bayesian 方法的可靠 EMG 模式分类。这个方法使用 Bayesian 分类模型，根据 Gaussian 分布来预测动作类型和其信度。然后将高预测信度的数据分配为 pseudo-label，并在 Bayesian 更新框架中逐渐更新模型的 posterior 分布，以达到适应性的动作识别。实验结果显示，提案的方法可以抑制分类精度逐渐下降的现象，并且比传统方法表现出色。这些成果证明了提案的方法的有效性，并且适用于实际的 EMG-based 控制系统。