Related papers: ReVeal-MT: A Physics-Informed Neural Network for M…
Physics-informed neural networks (PINNs) provide a promising framework for solving inverse problems governed by partial differential equations (PDEs) by integrating observational data and physical constraints in a unified optimization…
Reconfigurable Intelligent Surface (RIS) has becoming a useful tool in future wireless communication systems for close-distance communication network. This paper we use Reconfigurable Intelligent Surface (RIS) for downlink multi-user…
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum…
Wireless power transfer (WPT) is a promising new solution to provide convenient and perpetual energy supplies to wireless networks. In practice, WPT is implementable by various technologies such as inductive coupling, magnetic resonate…
Physics-Informed Neural Network (PINN) is a novel multi-task learning framework useful for solving physical problems modeled using differential equations (DEs) by integrating the knowledge of physics and known constraints into the…
While Physics-Informed Neural Networks (PINNs) offer a mesh-free approach to solving PDEs, standard point-wise residual minimization suffers from convergence pathologies in topologically complex domains like Triply Periodic Minimal Surfaces…
As spectrum sharing becomes increasingly vital to meet rising wireless demands in the future, spectrum monitoring and transmitter identification are indispensable for enforcing spectrum usage policy, efficient spectrum utilization, and…
Wireless underground sensor networks play an important role in underground sensing such as climate-smart agriculture and underground infrastructure monitoring. Existing works consider a static underground environment, which is not practical…
Efficient spectrum use in wireless sensor networks through spatial reuse requires effective models of packet reception at the physical layer in the presence of interference. Despite recent progress in analytic and simulations research into…
Reconfigurable intelligent surfaces (RISs) are widely considered a promising technology for future wireless communication systems. As an important indicator of RIS-assisted communication systems in green wireless communications, energy…
Motivated by massive deployment of low data rate Internet of things (IoT) and ehealth devices with requirement for highly reliable communications, this paper proposes receive beamforming techniques for the uplink of a single-input…
The reconfigurable intelligent surface is a promising technology for the manipulation and control of wireless electromagnetic signals. In particular, it has the potential to provide significant performance improvements for wireless…
Standard Physics-Informed Neural Networks (PINNs) often face challenges when modeling parameterized dynamical systems with sharp regime transitions, such as bifurcations. In these scenarios, the continuous mapping from parameters to…
Geometric environment information aids future distributed radio infrastructures in providing services, such as ultra-reliable communication, positioning, and wireless power transfer (WPT). An a priori known environment model cannot always…
Reconfigurable intelligent surface (RIS) is a promising reflective radio technology for improving the coverage and rate of future wireless systems by reconfiguring the wireless propagation environment. The current work mainly focuses on the…
Physics-informed neural networks (PINNs) have emerged as a promising deep learning method, capable of solving forward and inverse problems governed by differential equations. Despite their recent advance, it is widely acknowledged that…
Reconfigurable intelligent surface (RIS) is very promising for wireless networks to achieve high energy efficiency, extended coverage, improved capacity, massive connectivity, etc. To unleash the full potentials of RIS-aided communications,…
We introduce, design, and evaluate a set of universal receiver beamforming techniques. Our approach and system DEFORM, a Deep Learning (DL) based RX beamforming achieves significant gain for multi antenna RF receivers while being agnostic…
Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect…
We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithm is based on physics informed neural networks (PINNs), which are trained by minimizing the residual of the underlying radiative tranfer equations.…