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Found 7 papers, 0 papers with code
Vision Reimagined: AI-Powered Breakthroughs in WiFi Indoor Imaging
This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image.
An Efficient Ratio Detector for Ambient Backscatter Communication
Different from original ratio detectors that use the magnitude ratio of the signals between two Reader antennas, in our proposed approach, we utilize the complex ratio so that phase information is preserved and propose an accurate linear channel model approximation.
Unrolled Optimization with Deep Learning-based Priors for Phaseless Inverse Scattering Problems
Such inverse problems can be highly non-linear and ill-posed under extremely strong scattering conditions such as when the objects have very high permittivity or are large in size.
Using Loaded N-port Structures to Achieve the Continuous-Space Electromagnetic Channel Capacity Bound
Simulation results of the channel capacity bounds achieved using our MIMO antenna design with one square wavelength size are provided.
Physics Assisted Deep Learning for Indoor Imaging using Phaseless Wi-Fi Measurements
Thus, our proposed method is the first inverse scattering-based deep learning framework which can image large scatterers with high permittivity and achieve accurate indoor RF imaging using phaseless Wi-Fi measurements.
Modeling and Architecture Design of Reconfigurable Intelligent Surfaces Using Scattering Parameter Network Analysis
Compared with the single connected reconfigurable impedance network, our group and fully connected reconfigurable impedance network can increase the received signal power by up to 62%, or maintain the same received signal power with a number of RIS elements reduced by up to 21%.
Enhancing Ambient Backscatter Communication Utilizing Coherent and Non-Coherent Space-Time Codes
To overcome this challenge, we propose the use of orthogonal space-time block codes (OSTBC) by incorporating multiple antennas at the Tag as well as at the Reader.
