OnRMap: An Online Radio Mapping Approach for Large Intelligent Surfaces
We introduce OnRMap, an online radio mapping (RMap) approach for the sensing and localization of active users (AUs), devices that are transmitting radio signals, and passive elements (PEs), elements that are in the environment and are illuminated by the AUs' radio signals. OnRMap processes the signals received by a large intelligent surface and produces a radio map (RM) of the environment based on signal processing techniques. The method then senses and locate the different elements without the need for offline scanning phases, which is important for environments with frequently changing spatial layouts. Empirical results demonstrate that OnRMap presents a higher localization accuracy than an offline method, but the price paid for being an online method is a moderate reduction in the detection rate.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Radio Localization and Mapping with Reconfigurable Intelligent Surfaces
5G radio at millimeter wave (mmWave) and beyond 5G concepts at 0.1-1 THz can exploit angle and delay measurements for localization, by the virtue of increased bandwidth and large antenna arrays but are limited in terms o…
Intelligent Word Embeddings of Free-Text Radiology Reports
Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challen…
Word EmbeddingsGeneralized Radio Environment Monitoring for Next Generation Wireless Networks
Enabling technologies of 5G and beyond wireless communication networks, such as millimeter-wave communication, beamforming, and multiple-input multiple-output (MIMO) antenna systems, are becoming increasingly dependent o…
Intelligent CommunicationRIS-Enabled and Access-Point-Free Simultaneous Radio Localization and Mapping
In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces~(RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propa…
In-Sensor Radio Frequency Computing for Energy-Efficient Intelligent Radar
Radio Frequency Neural Networks (RFNNs) have demonstrated advantages in realizing intelligent applications across various domains. However, as the model size of deep neural networks rapidly increases, implementing large-…