6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

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

A Survey on Fundamental Limits of Integrated Sensing and Communication

The integrated sensing and communication (ISAC), in which the sensing and communication share the same frequency band and hardware, has emerged as a key technology in future wireless systems due to two main reasons. First, many important application scenarios in fifth generation (5G) and beyond, such as autonomous vehicles, Wi-Fi sensing and extended reality, requires both high-performance sensing and wireless communications. Second, with millimeter wave and massive multiple-input multiple-output (MIMO) technologies widely employed in 5G and beyond, the future communication signals tend to have high-resolution in both time and angular domain, opening up the possibility for ISAC. As such, ISAC has attracted tremendous research interest and attentions in both academia and industry. Early works on ISAC have been focused on the design, analysis and optimization of practical ISAC technologies for various ISAC systems. While this line of works are necessary, it is equally important to study the fundamental limits of ISAC in order to understand the gap between the current state-of-the-art technologies and the performance limits, and provide useful insights and guidance for the development of better ISAC technologies that can approach the performance limits. In this paper, we aim to provide a comprehensive survey for the current research progress on the fundamental limits of ISAC. Particularly, we first propose a systematic classification method for both traditional radio sensing (such as radar sensing and wireless localization) and ISAC so that they can be naturally incorporated into a unified framework. Then we summarize the major performance metrics and bounds used in sensing, communications and ISAC, respectively. After that, we present the current research progresses on fundamental limits of each class of the traditional sensing and ISAC systems. Finally, the open problems and future research directions are discussed

Boosting transducer matrix sensitivity for 3D large field ultrasound localization microscopy using a multi-lens diffracting layer: a simulation study

Mapping blood microflows of the whole brain is crucial for early diagnosis of cerebral diseases. Ultrasound localization microscopy (ULM) was recently applied to map and quantify blood microflows in 2D in the brain of adult patients down to the micron scale. Whole brain 3D clinical ULM remains challenging due to the transcranial energy loss which significantly reduces the imaging sensitivity. Large aperture probes with a large surface can increase both resolution and sensitivity. However, a large active surface implies thousands of acoustic elements, with limited clinical translation. In this study, we investigate via simulations a new high-sensitive 3D imaging approach based on large diverging elements, combined with an adapted beamforming with corrected delay laws, to increase sensitivity. First, pressure fields from single elements with different sizes and shapes were simulated. High directivity was measured for curved element while maintaining high transmit pressure. Matrix arrays of 256 elements with a dimension of 10x10 cm with small ( $\lambda$ /2), large (4 $\lambda$ ), and curved elements (4 $\lambda$ ) were compared through point spread functions analysis. A large synthetic microvessel phantom filled with 100 microbubbles per frame was imaged using the matrix arrays in a transcranial configuration. 93% of the bubbles were detected with the proposed approach demonstrating that the multi-lens diffracting layer has a strong potential to enable 3D ULM over a large field of view through the bones