相关论文: TiRE-GAN: Task-Incentivized Generative Learning fo…
Outdoor radio map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) and cellular systems. Radio map describes spatial signal strength distribution and provides network coverage…
In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually…
The increasing demand for high-speed and reliable wireless networks has driven advancements in technologies such as millimeter-wave and 5G radios, which requires efficient planning and timely deployment of wireless access points. A critical…
In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain to construct a smooth radio frequency map (RFMap) and then perform…
Radio Environment Maps (REMs) are crucial for numerous applications in Telecom. The construction of accurate Radio Environment Maps (REMs) has become an important and challenging topic in recent decades. In this paper, we present a method…
Enriching information of spectrum coverage, radiomap plays an important role in many wireless communication applications, such as resource allocation and network optimization. To enable real-time, distributed spectrum management,…
Radio-frequency coverage maps (RF maps) are extensively utilized in wireless networks for capacity planning, placement of access points and base stations, localization, and coverage estimation. Conducting site surveys to obtain RF maps is…
Radio maps provide radio frequency metrics, such as the received signal strength, at every location of a geographic area. These maps, which are estimated using a set of measurements collected at multiple positions, find a wide range of…
Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and are important tools for network planning in modern wireless…
Radio frequency (RF) map is a promising technique for capturing the characteristics of multipath signal propagation, offering critical support for channel modeling, coverage analysis, and beamforming in wireless communication networks. This…
The radio map, serving as a visual representation of electromagnetic spatial characteristics, plays a pivotal role in assessment of wireless communication networks and radio monitoring coverage. Addressing the issue of low accuracy existing…
To gain panoramic awareness of spectrum coverage in complex wireless environments, data-driven learning approaches have recently been introduced for radio map estimation (RME). While existing deep learning based methods conduct RME given…
High-speed railway tunnel communication systems require reliable radio wave propagation prediction to ensure operational safety. However, conventional simulation methods face challenges of high computational complexity and inability to…
In wireless networks, radio-frequency (RF) maps are critical for tasks such as capacity planning, coverage estimation, and localization. Traditional approaches for obtaining RF maps, including site surveys and ray-tracing simulations, are…
As a revolutionary generative paradigm of deep learning, generative adversarial networks (GANs) have been widely applied in various fields to synthesize realistic data. However, it is challenging for conventional GANs to synthesize raw…
Radio map construction based on extensive measurements is accurate but expensive and time-consuming, while environment-aware radio map estimation reduces the costs at the expense of low accuracy. Considering accuracy and costs, a…
Modeling radio propagation is essential for wireless network design and performance optimization. Traditional methods rely on physics models of radio propagation, which can be inaccurate or inflexible. In this work, we propose using graph…
Radio map in general refers to the geographical signal power spectrum density, formed by the superposition of concurrent wireless transmissions, as a function of location, frequency and time. It contains rich and useful information…
Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks. While deep learning (DL) techniques have emerged as an efficient alternative to conventional ray-tracing for radio map estimation…
The radio map represents the spatial distribution of spectrum resources within a region, supporting efficient resource allocation and interference mitigation. However, it is difficult to construct a dense radio map as a limited number of…