在本文中，提出了用于文本数据传输的语义通信框架。在研究的模型中，基站（BS）从文本数据中提取语义信息，并将其传输到每个用户。语义信息由由一组语义三元组组成的知识图（kg）建模。收到语义信息后，每个用户都使用图形到文本生成模型恢复原始文本。为了衡量所考虑的语义通信框架的性能，提出了共同捕获恢复文本的语义准确性和完整性的语义相似性（MSS）的指标。由于无线资源限制，BS可能无法将整个语义信息传输给每个用户并满足传输延迟约束。因此，BS必须为每个用户选择适当的资源块，并确定和将一部分语义信息传输给用户。因此，我们制定了一个优化问题，其目标是通过共同优化资源分配策略并确定要传输的部分语义信息来最大化总MSS。为了解决这个问题，提出了与注意力网络集成的基于近端优化的强化增强学习（RL）算法。所提出的算法可以使用注意网络在语义信息中评估每个三重组的重要性，然后在语义信息中三元组的重要性分布与总MSS之间建立关系。与传统的RL算法相比，所提出的算法可以动态调整其学习率，从而确保收敛到本地最佳解决方案。

In this paper, a semantic communication framework for image transmission is developed. In the investigated framework, a set of servers cooperatively transmit images to a set of users utilizing semantic communication techniques. To evaluate the performance of studied semantic communication system, a multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem aiming to minimize each server's transmission latency while reaching the ISS requirement. To solve this problem, a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) is proposed, which enables servers to coordinate for training and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL, the proposed RL improves the valuable action exploration of servers and the probability of finding a globally optimal RB allocation policy based on local observation. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% compared to traditional multi-agent RL.

Along with the springing up of semantics-empowered communication (SemCom) researches, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present corresponding ecosystems, including theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content \& channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., reliable and goal-oriented communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate the future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.

迄今为止，通信系统主要旨在可靠地交流位序列。这种方法提供了有效的工程设计，这些设计对消息的含义或消息交换所旨在实现的目标不可知。但是，下一代系统可以通过将消息语义和沟通目标折叠到其设计中来丰富。此外，可以使这些系统了解进行交流交流的环境，从而为新颖的设计见解提供途径。本教程总结了迄今为止的努力，从早期改编，语义意识和以任务为导向的通信开始，涵盖了基础，算法和潜在的实现。重点是利用信息理论提供基础的方法，以及学习在语义和任务感知通信中的重要作用。

本文考虑通过模型量化提高联邦学习（FL）的无线通信和计算效率。在提出的Bitwidth FL方案中，Edge设备将其本地FL模型参数的量化版本训练并传输到协调服务器，从而将它们汇总为量化的全局模型并同步设备。目的是共同确定用于本地FL模型量化的位宽度以及每次迭代中参与FL训练的设备集。该问题被视为一个优化问题，其目标是在每卷工具采样预算和延迟要求下最大程度地减少量化FL的训练损失。为了得出解决方案，进行分析表征，以显示有限的无线资源和诱导的量化误差如何影响所提出的FL方法的性能。分析结果表明，两个连续迭代之间的FL训练损失的改善取决于设备的选择和量化方案以及所学模型固有的几个参数。给定基于线性回归的这些模型属性的估计值，可以证明FL训练过程可以描述为马尔可夫决策过程（MDP），然后提出了基于模型的增强学习（RL）方法来优化动作的方法选择迭代。与无模型RL相比，这种基于模型的RL方法利用FL训练过程的派生数学表征来发现有效的设备选择和量化方案，而无需强加其他设备通信开销。仿真结果表明，与模型无RL方法和标准FL方法相比，提出的FL算法可以减少29％和63％的收敛时间。

作为Shannon Paradigm的突破的语义通信旨在成功传输由源传送的语义信息，而不是每种单个符号或位的准确接收，而不管其含义如何。本文提供了关于语义通信的概述。在简要审查Shannon信息理论之后，我们讨论了深入学习的理论，框架和系统设计的语义通信。不同于用于测量传统通信系统的符号/误码率，还讨论了语义通信的新性能度量。这篇文章由几个开放问题结束。

未来的互联网涉及几种新兴技术，例如5G和5G网络，车辆网络，无人机（UAV）网络和物联网（IOT）。此外，未来的互联网变得异质并分散了许多相关网络实体。每个实体可能需要做出本地决定，以在动态和不确定的网络环境下改善网络性能。最近使用标准学习算法，例如单药强化学习（RL）或深入强化学习（DRL），以使每个网络实体作为代理人通过与未知环境进行互动来自适应地学习最佳决策策略。但是，这种算法未能对网络实体之间的合作或竞争进行建模，而只是将其他实体视为可能导致非平稳性问题的环境的一部分。多机构增强学习（MARL）允许每个网络实体不仅观察环境，还可以观察其他实体的政策来学习其最佳政策。结果，MAL可以显着提高网络实体的学习效率，并且最近已用于解决新兴网络中的各种问题。在本文中，我们因此回顾了MAL在新兴网络中的应用。特别是，我们提供了MARL的教程，以及对MARL在下一代互联网中的应用进行全面调查。特别是，我们首先介绍单代机Agent RL和MARL。然后，我们回顾了MAL在未来互联网中解决新兴问题的许多应用程序。这些问题包括网络访问，传输电源控制，计算卸载，内容缓存，数据包路由，无人机网络的轨迹设计以及网络安全问题。

Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images into 3D virtual world scenes is computationally intensive and requires computation offloading. The disparity in transmitted scene dimension (2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL). To ensure the reliability and low latency of the system, we consider an asynchronous joint UL-DL scenario where in the UL stage, the smaller data size of the physical world scenes captured by multiple extended reality users (XUs) will be uploaded to the Metaverse Console (MC) to be construed and rendered. In the DL stage, the larger-size 3D virtual world scenes need to be transmitted back to the XUs. The decisions pertaining to computation offloading and channel assignment are optimized in the UL stage, and the MC will optimize power allocation for users assigned with a channel in the UL transmission stage. Some problems arise therefrom: (i) interactive multi-process chain, specifically Asynchronous Markov Decision Process (AMDP), (ii) joint optimization in multiple processes, and (iii) high-dimensional objective functions, or hybrid reward scenarios. To ensure the reliability and low latency of the system, we design a novel multi-agent reinforcement learning algorithm structure, namely Asynchronous Actors Hybrid Critic (AAHC). Extensive experiments demonstrate that compared to proposed baselines, AAHC obtains better solutions with preferable training time.

协作深度加强学习（CDRL）算法，其中多个代理可以在无线网络上协调是一种有希望的方法，以便在复杂的动态环境中依赖实时决策的未来智能和自主系统。尽管如此，在实际情况下，CDRL由​​于代理的异质性及其学习任务，不同环境，学习时间限制以及无线网络的资源限制，因此CDRL面临着许多挑战。为了解决这些挑战，在本文中，提出了一种新颖的语义感知CDRL方法，以使一组异构未经训练的代理具有语义连接的DRL任务，以在资源受限无线蜂窝网络上有效地协作。为此，提出了一种新的异构联邦DRL（HFDRL）算法，以选择用于协作的语义相关DRL代理的最佳子集。然后，该方法将共同优化合作选定代理的训练损失和无线带宽分配，以便在其实时任务的时间限制内培训每个代理。仿真结果表明，与最先进的基线相比，所提出的算法的卓越性能。

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

Terahertz频段（0.1---10 THZ）中的无线通信被视为未来第六代（6G）无线通信系统的关键促进技术之一，超出了大量多重输入多重输出（大量MIMO）技术。但是，THZ频率的非常高的传播衰减和分子吸收通常限制了信号传输距离和覆盖范围。从最近在可重构智能表面（RIS）上实现智能无线电传播环境的突破，我们为多跳RIS RIS辅助通信网络提供了一种新型的混合波束形成方案，以改善THZ波段频率的覆盖范围。特别是，部署了多个被动和可控的RIS，以协助基站（BS）和多个单人体用户之间的传输。我们通过利用最新的深钢筋学习（DRL）来应对传播损失的最新进展，研究了BS在BS和RISS上的模拟光束矩阵的联合设计。为了改善拟议的基于DRL的算法的收敛性，然后设计了两种算法，以初始化数字波束形成和使用交替优化技术的模拟波束形成矩阵。仿真结果表明，与基准相比，我们提出的方案能够改善50 \％的THZ通信范围。此外，还表明，我们提出的基于DRL的方法是解决NP-固定光束形成问题的最先进方法，尤其是当RIS辅助THZ通信网络的信号经历多个啤酒花时。

事物互联网的蓬勃发展使得能够将其计算和存储能力扩展到计算空中系统中的任务，其中云和边缘协作，特别是对于基于深度学习（DL）的人工智能（AI）任务。收集大量图像/视频数据，无人驾驶飞行器（UAV）由于其存储和计算能力有限，只能将智能分析任务切换到后端移动边缘计算（MEC）服务器。如何有效地传输AI模型的最相关信息是一个具有挑战性的主题。灵感来自近年来的任务型沟通，我们提出了一个新的空中图像传输范例，用于场景分类任务。在前端UAV上开发了轻量级模型，用于语义块传输，具有对图像和信道条件的看法。为了实现传输延迟和分类准确性之间的权衡，深增强学习（DRL）用于探索在各种信道条件下对后端分类器具有最佳贡献的语义块。实验结果表明，与固定传输策略和传统的内容感知方法相比，该方法可以显着提高分类准确性。

Recent technological advancements in space, air and ground components have made possible a new network paradigm called "space-air-ground integrated network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, the real-world deployment of a SAGIN becomes a major barrier for realizing such SAGINs. Compared to the space and terrestrial components, UAVs are expected to meet performance requirements with high flexibility and dynamics using limited resources. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. In this paper, we provide a comprehensive review of recent learning-based algorithmic approaches. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit (MAB), particle swarm optimization (PSO) and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, which can be applicable to various UAV-assisted missions on a SAGIN using these algorithms. We simulate users and environments according to real-world scenarios and compare the learning-based and PSO-based methods in terms of throughput, load, fairness, computation time, etc. We also implement and evaluate the 2-dimensional (2D) and 3-dimensional (3D) variations of these algorithms to reflect different deployment cases. Our simulation suggests that the $3$D satisfaction-based learning algorithm outperforms the other approaches for various metrics in most cases. We discuss some open challenges at the end and our findings aim to provide design guidelines for algorithm selections while optimizing the deployment of UAV-assisted SAGINs.

Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G), particularly in the context of connecting the unconnected and ultraconnecting the connected. Such digital inclusion thrive makes resource management problems, especially those accounting for load-balancing considerations, of particular interest. The conventional model-based optimization methods, however, often fail to meet the real-time processing and quality-of-service needs, due to the high heterogeneity of the space-air-ground networks, and the typical complexity of the classical algorithms. Given the premises of artificial intelligence at automating wireless networks design and the large-scale heterogeneity of non-terrestrial networks, this paper focuses on showcasing the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications. The paper first overviews the most relevant state-of-the art in the context of machine learning applications to the resource allocation problems, with a dedicated attention to space-air-ground networks. The paper then proposes, and shows the benefit of, one specific use case that uses ensembling deep neural networks for optimizing the user scheduling policies in integrated space-high altitude platform station (HAPS)-ground networks. Finally, the paper sheds light on the challenges and open issues that promise to spur the integration of machine learning in space-air-ground networks, namely, online HAPS power adaptation, learning-based channel sensing, data-driven multi-HAPSs resource management, and intelligent flying taxis-empowered systems.

随着数据生成越来越多地在没有连接连接的设备上进行，因此与机器学习（ML）相关的流量将在无线网络中无处不在。许多研究表明，传统的无线协议高效或不可持续以支持ML，这创造了对新的无线通信方法的需求。在这项调查中，我们对最先进的无线方法进行了详尽的审查，这些方法是专门设计用于支持分布式数据集的ML服务的。当前，文献中有两个明确的主题，模拟的无线计算和针对ML优化的数字无线电资源管理。这项调查对这些方法进行了全面的介绍，回顾了最重要的作品，突出了开放问题并讨论了应用程序方案。

The deployment flexibility and maneuverability of Unmanned Aerial Vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc. In many critical applications, UAVs capture images and other sensory data and then send the captured data to remote servers for inference and data processing tasks. However, this approach is not always practical in real-time applications due to the connection instability, limited bandwidth, and end-to-end latency. One promising solution is to divide the inference requests into multiple parts (layers or segments), with each part being executed in a different UAV based on the available resources. Furthermore, some applications require the UAVs to traverse certain areas and capture incidents; thus, planning their paths becomes critical particularly, to reduce the latency of making the collaborative inference process. Specifically, planning the UAVs trajectory can reduce the data transmission latency by communicating with devices in the same proximity while mitigating the transmission interference. This work aims to design a model for distributed collaborative inference requests and path planning in a UAV swarm while respecting the resource constraints due to the computational load and memory usage of the inference requests. The model is formulated as an optimization problem and aims to minimize latency. The formulated problem is NP-hard so finding the optimal solution is quite complex; thus, this paper introduces a real-time and dynamic solution for online applications using deep reinforcement learning. We conduct extensive simulations and compare our results to the-state-of-the-art studies demonstrating that our model outperforms the competing models.

预计下一代（NEVERG）网络将支持苛刻的触觉互联网应用，例如增强现实和连接的自动车辆。虽然最近的创新带来了更大的联系能力的承诺，它们对环境的敏感性以及不稳定的性能无视基于传统的基于模型的控制理由。零触摸数据驱动的方法可以提高网络适应当前操作条件的能力。诸如强化学习（RL）算法等工具可以仅基于观察历史来构建最佳控制策略。具体而言，使用深神经网络（DNN）作为预测器的深RL（DRL）已经被示出，即使在复杂的环境和高维输入中也能够实现良好的性能。但是，DRL模型的培训需要大量数据，这可能会限制其对潜在环境的不断发展统计数据的适应性。此外，无线网络是固有的分布式系统，其中集中式DRL方法需要过多的数据交换，而完全分布的方法可能导致较慢的收敛速率和性能下降。在本文中，为了解决这些挑战，我们向DRL提出了联合学习（FL）方法，我们指的是联邦DRL（F-DRL），其中基站（BS）通过仅共享模型的重量协作培训嵌入式DNN而不是训练数据。我们评估了两个不同版本的F-DRL，价值和策略，并显示出与分布式和集中式DRL相比实现的卓越性能。

在本文中，我们旨在改善干扰限制的无线网络中超级可靠性和低延迟通信（URLLC）的服务质量（QoS）。为了在通道连贯性时间内获得时间多样性，我们首先提出了一个随机重复方案，该方案随机将干扰能力随机。然后，我们优化了每个数据包的保留插槽数量和重复数量，以最大程度地减少QoS违规概率，该概率定义为无法实现URLLC的用户百分比。我们构建了一个级联的随机边缘图神经网络（REGNN），以表示重复方案并开发一种无模型的无监督学习方法来训练它。我们在对称场景中使用随机几何形状分析了QoS违规概率，并应用基于模型的详尽搜索（ES）方法来找到最佳解决方案。仿真结果表明，在对称方案中，通过模型学习方法和基于模型的ES方法实现的QoS违规概率几乎相同。在更一般的情况下，级联的Regnn在具有不同尺度，网络拓扑，细胞密度和频率重复使用因子的无线网络中很好地概括了。在模型不匹配的情况下，它的表现优于基于模型的ES方法。

6G时代的语义沟通被认为是一个有希望的沟通范式，可以突破传统通信的瓶颈。但是，其在多用户方案中的应用程序，尤其是广播案例，仍未探索。为了有效利用语义沟通启用的好处，在本文中，我们提出了一个一对一的语义通信系统。具体而言，我们建议使用一个启用的深神经网络（DNN），称为MR \ _DeepSc。通过为不同用户的语义功能利用语义功能，基于预训练的模型即Distilbert的语义识别器是为了区分不同用户的。此外，采用转移学习来加快新接收器网络的培训。仿真结果表明，在不同的通道条件下，提出的MR \ _DeepSc可以比其他基准测试获得最佳性能，尤其是在低信噪比（SNR）方面。

随着移动网络的增殖，我们正在遇到强大的服务多样化，这需要从现有网络的更大灵活性。建议网络切片作为5G和未来网络的资源利用解决方案，以解决这种可怕需求。在网络切片中，动态资源编排和网络切片管理对于最大化资源利用率至关重要。不幸的是，由于缺乏准确的模型和动态隐藏结构，这种过程对于传统方法来说太复杂。在不知道模型和隐藏结构的情况下，我们将问题作为受约束的马尔可夫决策过程（CMDP）制定。此外，我们建议使用Clara解决问题，这是一种基于钢筋的基于资源分配算法。特别是，我们分别使用自适应内部点策略优化和投影层分析累积和瞬时约束。评估表明，Clara明显优于资源配置的基线，通过服务需求保证。