相关论文: Big Data Analytics, Machine Learning and Artificia…

The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can…

Artificial intelligence (AI) has emerged as a transformative technology with immense potential to reshape the next-generation of wireless networks. By leveraging advanced algorithms and machine learning techniques, AI offers unprecedented…

The life of the modern world essentially depends on the work of the large artificial homogeneous networks, such as wired and wireless communication systems, networks of roads and pipelines. The support of their effective continuous…

The industry is satisfying the increasing demand for wireless bandwidth by densely deploying a large number of access points which are centrally managed, e.g. enterprise WiFi networks deployed in university campuses, companies, airports…

Recently, deep neural network (DNN) has been widely adopted in the design of intelligent communication systems thanks to its strong learning ability and low testing complexity. However, most current offline DNN-based methods still suffer…

In recent years, with the rapid enhancement of computing power, deep learning methods have been widely applied in wireless networks and achieved impressive performance. To effectively exploit the information of graph-structured data as well…

Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very…

Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet).…

Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types…

It is widely perceived that leveraging the success of modern machine learning techniques to mobile devices and wireless networks has the potential of enabling important new services. This, however, poses significant challenges, essentially…

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However,…

Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless…

In the wake of disruptive IoT technologies generating massive amounts of diverse data, Machine Learning (ML) will play a crucial role in bringing intelligence to Internet of Things (IoT) networks. This paper provides a comprehensive…

Resource management plays a pivotal role in wireless networks, which, unfortunately, leads to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning techniques, has recently emerged as a disruptive technology…

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and…

The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexity and the safety…

Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there…

Next-generation Multiple-Input Multiple-Output (MIMO) is expected to be intelligent and scalable. In this paper, we study Large Language Model (LLM)-enabled next-generation MIMO networks. Firstly, we provide an overview of the development,…

This article focuses on the application of artificial intelligence (AI) in non-orthogonal multiple-access (NOMA), which aims to achieve automated, adaptive, and high-efficiency multi-user communications towards next generation multiple…

High connectivity and robustness are critical requirements in distributed networks, as they ensure resilience, efficient communication, and adaptability in dynamic environments. Additionally, optimizing energy consumption is also paramount…