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。

随着深度学习（DL）的发展，自然语言处理（NLP）使我们可以分析和理解大量语言文本。因此，在NLP的帮助下，我们可以在联合语义源和噪声频道上进行联合语义源和信道进行语义通信。然而，实现这一目标的现有方法是使用NLP的固定变压器，同时忽略每个句子中包含的语义信息的差异。为了解决这个问题，我们提出了一种基于通用变压器的新语义通信系统。与传统变压器相比，在通用变压器中引入了自适应循环机制。通过引入循环机制，新的语义通信系统可以更灵活地传输具有不同语义信息的句子，并在各种信道条件下实现更好的端到端性能。

The current optical communication systems minimize bit or symbol errors without considering the semantic meaning behind digital bits, thus transmitting a lot of unnecessary information. We propose and experimentally demonstrate a semantic optical fiber communication (SOFC) system. Instead of encoding information into bits for transmission, semantic information is extracted from the source using deep learning. The generated semantic symbols are then directly transmitted through an optical fiber. Compared with the bit-based structure, the SOFC system achieved higher information compression and a more stable performance, especially in the low received optical power regime, and enhanced the robustness against optical link impairments. This work introduces an intelligent optical communication system at the human analytical thinking level, which is a significant step toward a breakthrough in the current optical communication architecture.

6G无线网络可以预见，以加快物理和网络世界的融合，并以我们部署和利用通信网络的方式实现范式换档。机器学习，尤其是深度学习（DL），将通过提供具有高水平智能的网络的新范式来成为6G的关键技术推动力之一。在本文中，我们介绍了一种新兴的DL体系结构，称为Transformer，并讨论了其对6G网络设计的潜在影响。我们首先讨论变压器和经典DL体系结构之间的差异，并强调变压器的自我发挥机制和强大的代表能力，这使其在应对无线网络设计的各种挑战方面特别有吸引力。具体而言，我们提出了基于变压器的解决方案，用于大规模多输入多输出（MIMO）系统和6G网络中的各种语义通信问题。最后，我们讨论了基于变压器的解决方案中的关键挑战和开放问题，并确定未来在智能6G网络中部署的研究方向。

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。

尽管语义通信对大量任务表现出令人满意的性能，但语义噪声和系统的鲁棒性的影响尚未得到很好的研究。语义噪声是指预期的语义符号和接收到的语义符号之间的误导性，从而导致任务失败。在本文中，我们首先提出了一个框架，用于稳健的端到端语义通信系统来对抗语义噪声。特别是，我们分析了样品依赖性和样本无关的语义噪声。为了打击语义噪声，开发了具有重量扰动的对抗训练，以在训练数据集中纳入带有语义噪声的样品。然后，我们建议掩盖一部分输入，在该输入中，语义噪声经常出现，并通过噪声相关的掩蔽策略设计蒙版vector量化量化的量化自动编码器（VQ-VAE）。我们使用发射器共享的离​​散代码簿和接收器用于编码功能表示。为了进一步提高系统鲁棒性，我们开发了一个功能重要性模块（FIM），以抑制与噪声相关和任务无关的功能。因此，发射器只需要在代码簿中传输这些重要的任务相关功能的索引即可。仿真结果表明，所提出的方法可以应用于许多下游任务，并显着提高针对语义噪声的鲁棒性，并显着减少了传输开销。

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.

Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of $10^{-7}$ when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.

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。

基于深度学习的渠道代码设计最近引起了人们的兴趣，可以替代传统的编码算法，尤其是对于现有代码不提供有效解决方案的渠道。通过反馈渠道进行的沟通就是一个这样的问题，最近通过采用各种深度学习体系结构来获得有希望的结果。在本文中，我们为反馈渠道介绍了一种新颖的学习辅助代码设计，称为广义块注意反馈（GBAF）代码，i）使用模块化体系结构，可以使用不同的神经网络体系结构实现；ii）与现有设计相比，错误的可能性提高了误顺序；iii）可以以所需的代码速率传输。

现有的深度学习的语义通信系统通常依赖于包含经验数据及其相关语义信息的发射器和接收器之间的共同背景知识。实际上，语义信息是由接收器的务实任务定义的，发射器不能知道。发射机上的实际可观察​​数据也可以具有与共享背景知识库中的经验数据相同的分布。为了解决这些实际问题，本文提出了一个新的基于神经网络的语义通信系统，用于图像传输，该任务在发射器上不知道，并且数据环境是动态的。该系统由两个主要部分组成，即语义编码（SC）网络和数据适应（DA）网络。 SC网络学习如何使用接收器领导训练过程提取和传输语义信息。通过使用传输学习的域适应技术，DA网络学习了如何将观察到的数据转换为SC网络可以在不进行重新验证的情况下进行处理的类似形式的经验数据。数值实验表明，所提出的方法可以适应可观察的数据集，同时在数据恢复和任务执行方面保持高性能。

尽管模拟语义通信系统在文献中受到了很大的关注，但在数字语义通信系统上的工作较少。在本文中，我们开发了一个深度学习（DL）启用的矢量量化（VQ）语义通信系统，用于图像传输，名为VQ-Deepsc。具体而言，我们提出了一个基于卷积的神经网络（CNN）的收发器来提取图像的多尺度语义特征，并引入多尺度语义嵌入空间以执行语义特征量化，从而使数据与数字通信系统兼容。此外，我们通过引入Patchgan歧视者来采用对抗训练来提高接收图像的质量。实验结果表明，根据SSIM，所提出的VQ-Deepsc优于传统图像传输方法。

最近，基于深层神经网络（DNN）的物理层通信技术引起了极大的兴趣。尽管模拟实验已经验证了它们增强通信系统和出色性能的潜力，但对理论分析的关注很少。具体而言，物理层中的大多数研究都倾向于专注于DNN模型在无线通信问题上的应用，但理论上不了解DNN在通信系统中的工作方式。在本文中，我们旨在定量分析为什么DNN可以在物理层中与传统技术相比，并在计算复杂性方面提高其成本。为了实现这一目标，我们首先分析基于DNN的发射器的编码性能，并将其与传统发射器进行比较。然后，我们理论上分析了基于DNN的估计器的性能，并将其与传统估计器进行比较。第三，我们调查并验证在信息理论概念下基于DNN的通信系统中如何播放信息。我们的分析开发了一种简洁的方式，可以在物理层通信中打开DNN的“黑匣子”，可用于支持基于DNN的智能通信技术的设计，并有助于提供可解释的性能评估。

基于深度学习（DL）的联合源通道编码（DEEPJSCC）的最新进展导致了语义通信的新范式。基于DEEPJSCC的语义通信的两个显着特征是直接从源信号中对语义感知功能的开发以及这些功能的离散时间模拟传输（DTAT）。与传统的数字通信相比，与DEEPJSCC的语义通信在接收器上提供了出色的重建性能，并具有较高的频道质量降解，但在传输信号中也表现出较大的峰值功率比（PAPR）。一个空旷的问题是，DeepJSCC的收益是否来自高PAPR连续振幅信号带来的额外自由。在本文中，我们通过在图像传输的应用中探索三种PAPR还原技术来解决这个问题。我们确认，基于DEEPJSCC的语义通信的出色图像重建性能可以保留，而传输的PAPR被抑制至可接受的水平。该观察是在实用语义通信系统中实施DEEPJSCC的重要一步。

在多输入多输出（MIMO）系统中使用深度自动码器（DAE）进行端到端通信，是一种具有重要潜力的新概念。在误码率（BER）方面，已示出DAE-ADED MIMO以占地识别的奇异值分解（SVD）为基础的预编码MIMO。本文提出将信道矩阵的左右奇异矢量嵌入到DAE编码器和解码器中，以进一步提高MIMO空间复用的性能。 SVD嵌入式DAE主要优于BER的理论线性预编码。这是显着的，因为它表明所提出的DAES通过将通信系统视为单个端到端优化块来超出当前系统设计的极限。基于仿真结果，在SNR = 10dB，所提出的SVD嵌入式设计可以实现近10美元，并将BER减少至少10次，而没有SVD，相比增长了18倍的增长率最高18倍具有理论线性预编码。我们将这一点归因于所提出的DAE可以将输入和输出与具有有限字母输入的自适应调制结构匹配。我们还观察到添加到DAE的剩余连接进一步提高了性能。

正交频分复用（OFDM）已广泛应用于当前通信系统。人工智能（AI）addm接收器目前被带到最前沿替换和改进传统的OFDM接收器。在这项研究中，我们首先比较两个AI辅助OFDM接收器，即数据驱动的完全连接的深神经网络和模型驱动的COMNet，通过广泛的仿真和实时视频传输，使用5G快速原型制作系统进行跨越式-Air（OTA）测试。我们在离线训练和真实环境之间的频道模型之间的差异差异导致的模拟和OTA测试之间找到了性能差距。我们开发一种新颖的在线培训系统，称为SwitchNet接收器，以解决此问题。该接收器具有灵活且可扩展的架构，可以通过在线训练几个参数来适应真实频道。从OTA测试中，AI辅助OFDM接收器，尤其是SwitchNet接收器，对真实环境具有鲁棒，并且对未来的通信系统有前途。我们讨论了本文初步研究的潜在挑战和未来的研究。

Most semantic communication systems leverage deep learning models to provide end-to-end transmission performance surpassing the established source and channel coding approaches. While, so far, research has mainly focused on architecture and model improvements, but such a model trained over a full dataset and ergodic channel responses is unlikely to be optimal for every test instance. Due to limitations on the model capacity and imperfect optimization and generalization, such learned models will be suboptimal especially when the testing data distribution or channel response is different from that in the training phase, as is likely to be the case in practice. To tackle this, in this paper, we propose a novel semantic communication paradigm by leveraging the deep learning model's overfitting property. Our model can for instance be updated after deployment, which can further lead to substantial gains in terms of the transmission rate-distortion (RD) performance. This new system is named adaptive semantic communication (ASC). In our ASC system, the ingredients of wireless transmitted stream include both the semantic representations of source data and the adapted decoder model parameters. Specifically, we take the overfitting concept to the extreme, proposing a series of ingenious methods to adapt the semantic codec or representations to an individual data or channel state instance. The whole ASC system design is formulated as an optimization problem whose goal is to minimize the loss function that is a tripartite tradeoff among the data rate, model rate, and distortion terms. The experiments (including user study) verify the effectiveness and efficiency of our ASC system. Notably, the substantial gain of our overfitted coding paradigm can catalyze semantic communication upgrading to a new era.

Motivated by recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has received considerable attention. It breaks with the classic design paradigm of Shannon by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact copy and thus allows for savings in channel uses or information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics from logical to probabilistic entailment relations between meaning and messages. Thus, we model semantics by means of a hidden random variable and define the task of semantic communication as transmission of messages over a communication channel such that semantics is best preserved. We formulate the semantic communication design either as an Information Maximization or as an Information Bottleneck optimization problem. Finally, we propose the ML-based semantic communication system SINFONI for a distributed multipoint scenario: SINFONI communicates the meaning behind multiple messages that are observed at different senders to a single receiver for semantic retrieval. We analyze SINFONI by processing images as an example of messages. Numerical results reveal a tremendous rate normalized SNR shift up to 20 dB compared to classically designed communication systems.

State-of-the-art performance for many emerging edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location and time sensitive, and the parameters of a specific DNN must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. In this paper, we introduce AirNet, a novel training and transmission method that allows efficient wireless delivery of DNNs under stringent transmit power and latency constraints. We first train the DNN with noise injection to counter the wireless channel noise. Then we employ pruning to reduce the network size to the available channel bandwidth, and perform knowledge distillation from a larger model to achieve satisfactory performance, despite pruning. We show that AirNet achieves significantly higher test accuracy compared to digital alternatives under the same bandwidth and power constraints. The accuracy of the network at the receiver also exhibits graceful degradation with channel quality, which reduces the requirement for accurate channel estimation. We further improve the performance of AirNet by pruning the network below the available bandwidth, and using channel expansion to provide better robustness against channel noise. We also benefit from unequal error protection (UEP) by selectively expanding more important layers of the network. Finally, we develop an ensemble training approach, which trains a whole spectrum of DNNs, each of which can be used at different channel condition, resolving the impractical memory requirements.

Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL receiver outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.