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Future 6G networks envision ubiquitous connectivity through space-air-ground integrated networks (SAGINs), where high-altitude platform stations (HAPSs) and satellites complement terrestrial systems to provide wide-area, low-latency…

Full-duplex (FD) is an attractive technology that can significantly boost the throughput of wireless communications. However, it is limited by the severe self-interference (SI) from the transmitter to the local receiver. In this paper, we…

Increasing concerns on intelligent spectrum sensing call for efficient training and inference technologies. In this paper, we propose a novel federated learning (FL) framework, dubbed federated spectrum learning (FSL), which exploits the…

The increasing demand for reliable connectivity in industrial environments necessitates effective spectrum utilization strategies, especially in the context of shared spectrum bands. However, the dynamic spectrum-sharing mechanisms often…

As sixth-generation (6G) wireless communication networks evolve, privacy concerns are expected due to the transmission of vast amounts of security-sensitive private information. In this context, a reconfigurable intelligent surface (RIS)…

The emergent technology of Reconfigurable Intelligent Surfaces (RISs) has the potential to transform wireless environments into controllable systems, through programmable propagation of information-bearing signals. Techniques stemming from…

Deep learning (DL) has emerged as a transformative technology with immense potential to reshape the sixth-generation (6G) wireless communication network. By utilizing advanced algorithms for feature extraction and pattern recognition, DL…

In state-of-the-art wireless networks, the radio environment is beyond the control of the operators which leads to several issues. For example, signal attenuation limits radio connectivity, multi-path propagation results in fading phenomena…

The sixth-generation (6G) wireless technology recognizes the potential of reconfigurable intelligent surfaces (RIS) as an effective technique for intelligently manipulating channel paths through reflection to serve desired users.…

Classical pixel-based Visual Servoing (VS) approaches offer high accuracy but suffer from a limited convergence area due to optimization nonlinearity. Modern deep learning-based VS methods overcome traditional vision issues but lack…

User location is a piece of critical information for network management and control. However, location uncertainty is unavoidable in certain settings leading to localization errors. In this paper, we consider the user location uncertainty…

This letter considers the secure communication in a reconfigurable intelligent surface (RIS) aided full duplex (FD) system. An FD base station (BS) serves an uplink (UL) user and a downlink (DL) user simultaneously over the same…

As the wireless research community moves toward shaping the vision of sixth-generation (6G) networks, reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for controlling the propagation environment. Although RIS…

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and…

In this letter, we study the reconfigurable intelligent surfaces (RIS) aided full-duplex (FD) communication system. By jointly designing the active beamforming of two multi-antenna sources and passive beamforming of RIS, we aim to maximize…

The rapid growth of data across fields of science and industry has increased the need to improve the performance of end-to-end data transfers while using the resources more efficiently. In this paper, we present a dynamic, multiparameter…

The study and benchmarking of Deep Reinforcement Learning (DRL) models has become a trend in many industries, including aerospace engineering and communications. Recent studies in these fields propose these kinds of models to address…

Deep reinforcement learning (DRL) has emerged as a pervasive and potent methodology for addressing artificial intelligence challenges. Due to its substantial potential for autonomous self-learning and self-improvement, DRL finds broad…

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical…

Radio frequency (RF) wireless power transfer (WPT) is promising for promoting sustainability in future wireless systems, but its low end-to-end power transfer efficiency is a critical challenge. For this, reconfigurable intelligent surfaces…