Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables semi-JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks.
In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks with rate splitting is investigated. In the considered model, a base station (BS) first extracts semantic information from its large-scale data, and then transmits the small-sized semantic information to each user which recovers the original data based on its local common knowledge. At the BS side, the probability graph is used to extract multi-level semantic information. In the downlink transmission, a rate splitting scheme is adopt…
SemantIC: Semantic Interference Cancellation Towards 6G Wireless Communications
School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）；IMT-Atlantique（法国大西洋电信学院）；Japan Advanced Institute of Science Technology（日本先进科学技术研究所）；University of Oulu（奥卢大学）
The concept of a fiber aided wireless network architecture (FAWNA) is introduced in [Ray et al., Allerton Conference 2005], which allows high-speed mobile connectivity by leveraging the speed of optical networks. In this paper, we consider a single-input, multiple-output (SIMO) FAWNA, which consists of a SIMO wireless channel and an optical fiber channel, connected through wireless-optical interfaces. We propose a scheme where the received wireless signal at each interface is quantized and sent over the fiber. Though our architecture is similar to that of the classical CEO problem, our problem…
Semantic communication is an increasingly popular framework for wireless image transmission due to its high communication efficiency. With the aid of the joint-source-and-channel (JSC) encoder implemented by neural network, semantic communication directly maps original images into symbol sequences containing semantic information. Compared with the traditional separate source and channel coding design used in bitlevel communication systems, semantic communication systems are known to be more efficient and accurate especially in the low signal-to-the-noise ratio (SNR) regime. This thus prompts a…
University of Colorado（科罗拉多大学）；Virginia Tech（弗吉尼亚理工学院）
Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach to enable future intelligent and autonomous systems that rely on real-time decision-making in complex dynamic environments. Nonetheless, in practical scenarios, CDRL faces many challenges due to the heterogeneity of agents and their learning tasks, different environments, time constraints of the learning, and resource limitations of wireless networks. To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enab…
Deakin University（迪肯大学）；Yonsei University（延世大学）；University of Oulu（奥卢大学）
Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements. Towards enabling this wireless metaverse, in this article we propose a novel semantic communication (SC) framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs). An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI). To improve com…
This article investigates the exploitation of semantic communications in multi-user networks. We propose a novel heterogeneous semantic and bit multi-user framework for providing flawless, customized, and intelligent information transmission. We discuss both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) for the proposed heterogeneous framework, with an emphasis on investigating the attractive interplay between semantic communications and NOMA, namely NOMA enabled semantic communications and semantic communications enhanced NOMA. 1) For NOMA enabled semantic communi…
Physical-layer security (PLS) for industrial indoor terahertz (THz) wireless communication applications is considered. We use a similar model as being employed for additive white Gaussian noise (AWGN) wireless communication channels. A cell communication and a directed communication scenario are analyzed to illustrate the achievable semantic security guarantees for a wiretap channel with finite-blocklength THz-wireless communication links. We show that weakly directed transmitter (Alice) antennas, which allow cell-type communication with multiple legitimate receivers (Bobs) without adaptation …
Hybrid Bit and Semantic Communications for UAV-Enabled Wireless Power Transfer Networks: A Decision-Assisted Deep Reinforcement Learning Approach
Southwest Jiaotong University（西南交通大学）；University of Surrey（萨里大学）；Sun Yat-sen University（中山大学）；University of Nottingham Ningbo China（诺丁汉大学宁波校区）；University of Miami（迈阿密大学）
Multi-connectivity offers diversity in terms of multiple interfaces through which the data can be sent, thereby improving simultaneously the overall reliability and latency. This makes interface diversity a natural candidate for supporting Ultra-Reliable Low Latency Communications (URLLC). This work investigates how the packet error statistics from different interfaces impacts the overall reliability-latency characteristics. We use the simple Gilbert-Elliott model for burst errors and estimate its parameters based on experimental measurement traces from LTE and {Wi-Fi} packet transmissions col…
Beijing University of Posts and Telecommunications（北京邮电大学）；Peng Cheng Laboratory（鹏城实验室）
In this paper, we propose a new class of high-efficiency semantic coded transmission methods for end-to-end speech transmission over wireless channels. We name the whole system as deep speech semantic transmission (DSST). Specifically, we introduce a nonlinear transform to map the speech source to semantic latent space and feed semantic features into source-channel encoder to generate the channel-input sequence. Guided by the variational modeling idea, we build an entropy model on the latent space to estimate the importance diversity among semantic feature embeddings. Accordingly, these semant…
Beijing University of Posts and Telecommunications（北京邮电大学）；Peng Cheng Laboratory（鹏城实验室）
In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context…
Beijing University of Posts and Telecommunications（北京邮电大学）；Peng Cheng Laboratory（鹏城实验室）
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Previous works build upon convolutional neural networks (CNNs), which are inefficient in capturing global dependencies, resulting in degraded end-to-end transmission performance especially for high-resolution images. To tackle this, the proposed WITT employs Swin Transformers as a more capable backbone to extract long-range information. Different from ViTs in image classification tasks, WITT is highly optimized for image…
Distributionally Robust Wireless Semantic Communication with Large AI Models
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；School of Computer Science and Engineering, International University-VNUHCM（国际大学-胡志明市分校计算机科学与工程学院）；Bradley Department of Electrical and Computer Engineering, Virginia Tech（弗吉尼亚理工大学电气与计算机工程系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Electrical and Computer Engineering, University of Houston（休斯顿大学电气与计算机工程系）；Department of Computer Science and Engineering, School of Computing, Kyung Hee University（庆北大学计算机科学与工程系）；School of Engineering and Applied Science, Princeton University（普林斯顿大学工程与应用科学学院）
Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC
Zhejiang University（浙江大学）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Provincial Key Laboratory of Info. Proc., Commun. & Netw. (IPCAN)（浙江省信息处理、通信与网络重点实验室）；King’s College London（伦敦国王学院）；Department of Engineering, King’s College London（伦敦国王学院工程系）；University of Miami（迈阿密大学）；Institute for Data Science and Computing, University of Miami（迈阿密大学数据科学与计算研究所）；The University of Hong Kong（香港大学）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子电气工程系）
Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications
School of Integrated Technology, Yonsei University（延世大学整合技术学院）；Department of Electrical and Computer Engineering, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）
The recently commercialized fifth-generation (5G) wireless communication networks achieved many improvements, including air interface enhancement, spectrum expansion, and network intensification by several key technologies, such as massive multiple-input multiple-output (MIMO), millimeter-wave communications, and ultra-dense networking. Despite the deployment of 5G commercial systems, wireless communications is still facing many challenges to enable connected intelligence and a myriad of applications such as industrial Internet-of-things, autonomous systems, brain-computer interfaces, digital …
Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the "semantics" of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response to changing conditions in the environment. This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system that integrates spike-based sensing, processing, and com…
Adaptive Unequal Error Protection for Semantic Split Learning over Wireless Channels
University of Novi Sad（诺维萨德大学）；Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研发研究所）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
Adaptive Resource Allocation for Semantic Communication Networks
Nanjing University of Posts and Telecommunications（南京邮电大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Zhejiang Lab（之江实验室）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）
The first result is on the separability of the unicast capacity of stationary multi-channel multi-radio wireless networks, i.e., whether the capacity of such a network is equal to the sum of the capacities of the corresponding single-channel single-radio wireless networks. For both the Arbitrary Network model and the Random Network model, given a channel assignment, the separability property does not always hold. However, if the number of radio interfaces at each node is equal to the number of channels, the separability property holds. The second result is on the impact of multi-channel routin…
Beijing University of Posts and Telecommunications（北京邮电大学）；Peng Cheng Laboratory（鹏城实验室）
As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source-channel coding (deep JSCC) method is emerging in both deep learning and communication communities. However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM). But for low transmission rates, due to the imperfect wireless channel, these distortion metrics lose significance as they favor pixel-wise preservation. To account for human visual pe…
To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange. However, the current design of separating EL deployment and communication optimization does not yet reap the promised benefits of distributed signal processing, and sometimes suffers from excessive signalling overhead, long processing delay, and unstable learning convergence. In…
Game-Theoretic Latent Space Alignment for Multi-user Semantic MIMO Communications
CEA Leti（CEA列蒂）；University Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Sapienza University of Rome（罗马第一大学）；Department of Information Engineering, Electronics, and Telecommunications (DIET)（信息与电信工程电子系）；CNIT（意大利国家信息技术研究中心）
