Related papers: Machine Learning for Future Wireless Communication…
In this article, we discuss some of the recent developments in applying machine learning (ML) techniques to nonlinear dynamical systems. In particular, we demonstrate how to build a suitable ML framework for addressing two specific…
Accurate channel estimation is crucial for the improvement of signal processing performance in wireless communications. However, traditional model-based methods frequently experience difficulties in dynamic environments. Similarly,…
Due to the advancement in technologies, the next-generation wireless network will be very diverse, complicated, and according to the changed demands of the consumers. The current network operator methodologies and approaches are traditional…
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of…
Channel estimation is crucial in wireless communications. However, in many papers neural networks are frequently tested by training and testing on one example channel or similar channels. This is because data-driven methods often degrade on…
The primary focus of Artificial Intelligence/Machine Learning (AI/ML) integration within the wireless technology is to reduce capital expenditures, optimize network performance, and build new revenue streams. Replacing traditional…
Distributed massive MIMO is considered a key advancement for improving the performance of next-generation wireless telecommunication systems. However, its efficacy in scenarios involving user mobility is limited due to channel aging. To…
Optical Wireless Communication (OWC) has gained significant attention due to its high-speed data transmission and throughput. Optical wireless channels are often assumed to be flat, but we evaluate frequency selective channels to consider…
Knowing a user's next cell allows more efficient resource allocation and enables new location-aware services. To anticipate the cell a user will hand-over to, we introduce a new machine learning based prediction system. Therein, we…
Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for…
Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which…
In this paper, a novel machine learning (ML) framework is proposed for enabling a predictive, efficient deployment of unmanned aerial vehicles (UAVs), acting as aerial base stations (BSs), to provide on-demand wireless service to cellular…
In a wireless network, gathering information at the base station about mobile users based only on uplink channel measurements is an interesting challenge. Indeed, accessing the users locations and predicting their downlink channels would be…
Channel knowledge map (CKM) has emerged as a pivotal technology for environment-aware wireless communications and sensing, which provides a priori location-specific channel knowledge to facilitate network optimization. Efficient CKM…
Machine learning (ML) applications for wireless communications have gained momentum on the standardization discussions for 5G advanced and beyond. One of the biggest challenges for real world ML deployment is the need for labeled signals…
Enterprise Wi-Fi networks can greatly benefit from Artificial Intelligence and Machine Learning (AI/ML) thanks to their well-developed management and operation capabilities. At the same time, AI/ML-based traffic/load prediction is one of…
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key technology for next-generation wireless communication systems. By deploying significantly more antennas than conventional massive MIMO systems, XL-MIMO promises…
The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to…
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems. However,…
Intelligent communication is gradually considered as the mainstream direction in future wireless communications. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has…