相关论文: GPS-Aided Deep Learning for Beam Prediction and Tr…

Communication at millimeter wave (mmWave) bands is expected to become a key ingredient of next generation (5G) wireless networks. Effective mmWave communications require fast and reliable methods for beamforming at both the User Equipment…

Accurate beam alignment is essential for beam-based millimeter wave communications. Conventional beam sweeping solutions often have large overhead, which is unacceptable for mobile applications like vehicle-to-everything. Learning-based…

This paper introduces a novel method for predicting blockages in millimeter-wave (mmWave) communication systems towards enabling reliable connectivity. It employs a self-supervised learning approach to label radio frequency (RF) data with…

In this paper, millimeter wave (mmWave) wireless channel characteristics (Doppler spread and path loss modeling) for Unmanned Aerial Vehicles (UAVs) assisted communication is analyzed and studied by emulating the real UAV motion using a…

Millimeter wave (mmWave) beam-tracking based on machine learning enables the development of accurate tracking policies while obviating the need to periodically solve beam-optimization problems. However, its applicability is still arguable…

Beam prediction is critical for reducing beam-training overhead in millimeter-wave (mmWave) systems, especially in high-mobility vehicular scenarios. This paper presents a BEV-Fusion based framework that unifies camera, LiDAR, radar, and…

In this paper, we investigate the problem of beam alignment in millimeter wave (mmWave) systems, and design an optimal algorithm to reduce the overhead. Specifically, due to directional communications, the transmitter and receiver beams…

Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications. To establish the IA between the base station (e.g., gNodeB)…

Beam alignment (BA) in modern millimeter wave standards such as 5G NR and WiGig (802.11ay) is based on exhaustive and/or hierarchical beam searches over pre-defined codebooks of wide and narrow beams. This approach is slow and…

Millimeter-wave (mmWave) networks offer the potential for high-speed data transfer and precise localization, leveraging large antenna arrays and extensive bandwidths. However, these networks are challenged by significant path loss and…

The cell-free massive multi-input multi-output (CF-mMIMO) is a promising technology for the six generation (6G) communication systems. Channel prediction will play an important role in obtaining the accurate CSI to improve the performance…

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services.…

The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway (HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel…

This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The datasets we have obtained from the beam profiling and the…

Millimeter-wave (mmWave) communication systems, particularly those leveraging multi-user multiple-input and multiple-output (MU-MIMO) with hybrid beamforming, face challenges in optimizing user throughput and minimizing latency due to the…

To accomplish various tasks, safe and smooth control of unmanned aerial vehicles (UAVs) needs to be guaranteed, which cannot be met by existing ultra-reliable low latency communications (URLLC). This has attracted the attention of the…

Deep Reinforcement Learning (DRL) is gaining attention as a potential approach to design trajectories for autonomous unmanned aerial vehicles (UAV) used as flying access points in the context of cellular or Internet of Things (IoT)…

Traditional machine learning techniques have achieved great success in improving data-rate performance and reducing latency in millimeter wave (mmWave) communications. However, these methods still face two key challenges: (i) their reliance…

Codebook-based beam selection is one approach for configuring millimeter wave communication links. The overhead required to reconfigure the transmit and receive beam pair, though, increases in highly dynamic vehicular communication systems.…

The challenging propagation environment, combined with the hardware limitations of mmWave systems, gives rise to the need for accurate initial access beam alignment strategies with low latency and high achievable beamforming gain. Much of…