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Found 17 papers, 1 papers with code
Semi-Passive Intelligent Reflecting Surface Enabled Sensing Systems
Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS).
Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems
Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem.
Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing
Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT.
Channel Tracking and Prediction for IRS-aided Wireless Communications
Based on the proposed transmission protocol, we propose a two-stage channel tracking and prediction (2SCTP) scheme to obtain the direct and reflected channels with low channel training overhead, which is achieved by exploiting the temporal correlation of the time-varying channels.
Channel Estimation for IRS-aided Multiuser Communications with Reduced Error Propagation
Intelligent reflecting surface (IRS) has emerged as a promising paradigm to improve the capacity and reliability of a wireless communication system by smartly reconfiguring the wireless propagation environment.
Low-Complexity Joint Power Allocation and Trajectory Design for UAV-Enabled Secure Communications with Power Splitting
An unmanned aerial vehicle (UAV)-aided secure communication system is conceived and investigated, where the UAV transmits legitimate information to a ground user in the presence of an eavesdropper (Eve).
A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection
Linear Programming (LP) is an important decoding technique for binary linear codes.
ADMM-based Decoder for Binary Linear Codes Aided by Deep Learning
Inspired by the recent advances in deep learning (DL), this work presents a deep neural network aided decoding algorithm for binary linear codes.
Through-Wall Human Mesh Recovery Using Radio Signals
This paper presents RF-Avatar, a neural network model that can estimate 3D meshes of the human body in the presence of occlusions, baggy clothes, and bad lighting conditions.
Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Understanding people's actions and interactions typically depends on seeing them.
Ranked #1 on
RF-based Pose Estimation
on RF-MMD
Learned Conjugate Gradient Descent Network for Massive MIMO Detection
Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is integrated into the LcgNet to smartly quantize the aforementioned step-sizes.
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling
We consider the problem of inferring the values of an arbitrary set of variables (e. g., risk of diseases) given other observed variables (e. g., symptoms and diagnosed diseases) and high-dimensional signals (e. g., MRI images or EEG).
RF-based 3D skeletons
It maintains this accuracy even in the presence of multiple people, and in new environments that it has not seen in the training set.
Through-Wall Human Pose Estimation Using Radio Signals
Yet, unlike vision-based pose estimation, the radio-based system can estimate 2D poses through walls despite never trained on such scenarios.
Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks
In this paper we consider nonconvex optimization and learning over a network of distributed nodes.
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture
We focus on predicting sleep stages from radio measurements without any attached sensors on subjects.
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework.
