Large Language Models for Telecom: The Next Big Thing?

The evolution of generative artificial intelligence (GenAI) constitutes a turning point in reshaping the future of technology in different aspects. Wireless networks in particular, with the blooming of self-evolving networks, represent a rich field for exploiting GenAI and reaping several benefits that can fundamentally change the way how wireless networks are designed and operated nowadays. To be specific, large language models (LLMs), a subfield of GenAI, are envisioned to open up a new era of autonomous wireless networks, in which a multimodal large model trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for dedicated AI models for each task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks. In this article, we aim to unfold the opportunities that can be reaped from integrating LLMs into the Telecom domain. In particular, we aim to put a forward-looking vision on a new realm of possibilities and applications of LLMs in future wireless networks, defining directions for designing, training, testing, and deploying Telecom LLMs, and reveal insights on the associated theoretical and practical challenges

Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures

In this work, we study the problem of semantic communication and inference, in which a student agent (i.e. mobile device) queries a teacher agent (i.e. cloud sever) to generate higher-order data semantics living in a simplicial complex. Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations. For effective communication and inference, the teacher seeks minimally sufficient and invariant semantic structures prior to conveying information. These minimal simplicial structures are found via judiciously removing simplices selected by the Hodge Laplacians without compromising the inference query accuracy. Subsequently, the student locally runs its own set of queries based on a masked simplicial convolutional autoencoder (SCAE) leveraging both local and remote teacher's knowledge. Numerical results corroborate the effectiveness of the proposed approach in terms of improving inference query accuracy under different channel conditions and simplicial structures. Experiments on a coauthorship dataset show that removing simplices by ranking the Laplacian values yields a 85% reduction in payload size without sacrificing accuracy. Joint semantic communication and inference by masked SCAE improves query accuracy by 25% compared to local student based query and 15% compared to remote teacher based query. Finally, incorporating channel semantics is shown to effectively improve inference accuracy, notably at low SNR values

NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D Radar, thermal camera and IMU can work robustly. But only a few literature can be found. A major reason is the lack of related datasets, which seriously hinders the research. Even though some datasets are proposed based on 4D radar in past four years, they are mainly designed for object detection, rather than SLAM. Furthermore, they normally do not include thermal camera. Therefore, in this paper, NTU4DRadLM is presented to meet this requirement. The main characteristics are: 1) It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS. 2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth odometry and intentionally formulated loop closures. 3) Considered both low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered structured, unstructured and semi-structured environments. 5) Considered both middle- and large- scale outdoor environments, i.e., the 6 trajectories range from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be accessible from this link: https://github.com/junzhang2016/NTU4DRadLMComment: 2023 IEEE International Intelligent Transportation Systems Conference (ITSC 2023

Aerial base stations with opportunistic links for next generation emergency communications

Rapidly deployable and reliable mission-critical communication networks are fundamental requirements to guarantee the successful operations of public safety officers during disaster recovery and crisis management preparedness. The ABSOLUTE project focused on designing, prototyping, and demonstrating a high-capacity IP mobile data network with low latency and large coverage suitable for many forms of multimedia delivery including public safety scenarios. The ABSOLUTE project combines aerial, terrestrial, and satellites communication networks for providing a robust standalone system able to deliver resilience communication systems. This article focuses on describing the main outcomes of the ABSOLUTE project in terms of network and system architecture, regulations, and implementation of aerial base stations, portable land mobile units, satellite backhauling, S-MIM satellite messaging, and multimode user equipments

A robust and active hybrid catalyst for facile oxygen reduction in solid oxide fuel cells

The sluggish oxygen reduction reaction (ORR) greatly reduces the energy efficiency of solid oxide fuel cells (SOFCs). Here we report our findings in dramatically enhancing the ORR kinetics and durability of the state-of-the-art La[subscript 0.6]Sr[subscript 0.4]Co[subscript 0.2]Fe[subscript 0.8]O[subscript 3](LSCF) cathode using a hybrid catalyst coating composed of a conformal PrNi[subscript 0.5]Mn[subscript 0.5]O[subscript 3](PNM) thin film with exsoluted PrOxnanoparticles. At 750°C, the hybrid catalyst-coated LSCF cathode shows a polarization resistance of ∼0.022 Ω cm[superscript 2], about 1/6 of that for a bare LSCF cathode (∼0.134 Ω cm[superscript 2]). Further, anode-supported cells with the hybrid catalyst-coated LSCF cathode demonstrate remarkable peak power densities (∼1.21 W cm[superscript -2]) while maintaining excellent durability (0.7 V for ∼500 h). Near Ambient X-ray Photoelectron Spectroscopy (XPS) and Near Edge X-Ray Absorption Fine Structure (NEXAFS) analyses, together with density functional theory (DFT) calculations, indicate that the oxygen-vacancy-rich surfaces of the PrOxnanoparticles greatly accelerate the rate of electron transfer in the ORR whereas the thin PNM film facilitates rapid oxide-ion transport while drastically enhancing the surface stability of the LSCF electrode

Atomic structures of Coxsackievirus A6 and its complex with a neutralizing antibody

手足口病是一种由人肠道病毒引起的全球性传染病，主要发生于5岁以下的婴幼儿，严重危害公众健康。根据获得的手足口病流行病学和病原学调查数据，目前认为CVA6与EV71和CVA16一样应作为优先的手足口病疫苗预防对象，亟需研制有效的预防和治疗方法。然而令人遗憾的是，目前对于CVA6的基础病毒学特别是结构生物学知识均缺乏足够了解，严重制约了相关研究的有效开展。 夏宁邵教授团队研究首次揭示了手足口病重要病原体柯萨奇病毒A组6型（CVA6）的病毒颗粒及其与中和抗体复合物的精确三维结构，为新型疫苗和治疗药物的研制提供了重要的理论基础。这项研究发现并精确描绘了CVA6的病毒颗粒及其与优势中和抗体的结构特征，首次完成了对CVA6的高精度“成像”，为新型疫苗和治疗药物研制提供了关键基础。 该研究工作在厦门大学分子疫苗学和分子诊断学国家重点实验室、国家传染病诊断试剂与疫苗工程技术研究中心科研平台完成。夏宁邵教授、颜晓东博士、程通副教授为该研究论文的共同通讯作者。颜晓东博士来自美国加州大学圣地亚哥分校，同时受聘为我校双聘教授。共同第一作者为徐龙发博士生、郑清炳工程师和李少伟教授。【Abstract】Coxsackievirus A6 (CVA6) has recently emerged as a major cause of hand, foot and mouth disease in children worldwide but no vaccine is available against CVA6 infections. Here, we demonstrate the isolation of two forms of stable CVA6 particles-procapsid and A-particle-with excellent biochemical stability and natural antigenicity to serve as vaccine candidates. Despite the presence (in A-particle) or absence (in procapsid) of capsid-RNA interactions, the two CVA6 particles have essentially identical atomic capsid structures resembling the uncoating intermediates of other enteroviruses. Our near-atomic resolution structure of CVA6 A-particle complexed with a neutralizing antibody maps an immune-dominant neutralizing epitope to the surface loops of VP1. The structure-guided cell-based inhibition studies further demonstrate that these loops could serve as excellent targets for designing anti-CVA6 vaccines.This work was supported by a grant from the National Natural Science Foundation of China (No. 31670933 and 81401669), the National Science and Technology Major Projects for Major New Drugs Innovation and Development (No. 2017ZX09101005-005-003), the National Science and Technology Major Project of Infectious Diseases (No. 2017ZX10304402-002-003) and the Natural Science Foundation of Fujian Province (No. 2015J05073). This work was also supported in part by funding to T.S.B. from the National Institutes of Health (Grant R37-GM33050). 研究工作也得到了国际病毒结构生物学权威专家美国加州大学洛杉矶分校周正洪教授的大力支持和帮助，获得了国家自然科学基金、新药创制国家科技重大专项、传染病防治国家科技重大专项和福建省自然科学基金的资助