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Found 13 papers, 1 papers with code
Neural 5G Indoor Localization with IMU Supervision
We propose practical algorithms for IMU double integration and training of the localization system.
Vision-Assisted Digital Twin Creation for mmWave Beam Management
In the context of communication networks, digital twin technology provides a means to replicate the radio frequency (RF) propagation environment as well as the system behaviour, allowing for a way to optimize the performance of a deployed system based on simulations.
Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems
Localization services for wireless devices play an increasingly important role in our daily lives and a plethora of emerging services and applications already rely on precise position information.
Benchmarking Learnt Radio Localisation under Distribution Shift
Deploying radio frequency (RF) localisation systems invariably entails non-trivial effort, particularly for the latest learning-based breeds.
Probabilistic 5G Indoor Positioning Proof of Concept with Outlier Rejection
In this work, we introduce an iterative positioning method that reweights the time of arrival (ToA) and angle of arrival (AoA) measurements originating from multiple locators in order to efficiently remove outliers.
Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence
Deep learning has revolutionised computer vision but has had limited application to radio perception tasks, in part due to lack of systematic datasets and benchmarks dedicated to the study of the performance and promise of radio sensing.
Self-Supervised Radio-Visual Representation Learning for 6G Sensing
In future 6G cellular networks, a joint communication and sensing protocol will allow the network to perceive the environment, opening the door for many new applications atop a unified communication-perception infrastructure.
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state information (CSI) databases that are passively collected at infrastructure base stations or access points.
Deep Inertial Navigation using Continuous Domain Adaptation and Optimal Transport
(2) We propose neural architectures and algorithms to assimilate knowledge from an indexed set of sensor positions in order to enhance the robustness and generalisability of robotic inertial tracking in the field.
A Computationally Efficient 2D MUSIC Approach for 5G and 6G Sensing Networks
Future cellular networks are intended to have the ability to sense the environment by utilizing reflections of transmitted signals.
Massive MIMO Channel Measurements and Achievable Rates in a Residential Area
In this paper we present a measurement set-up for massive MIMO channel sounding that shows very good long-term phase stability.
Towards Practical Indoor Positioning Based on Massive MIMO Systems
We showcase the practicability of an indoor positioning system (IPS) solely based on Neural Networks (NNs) and the channel state information (CSI) of a (Massive) multiple-input multiple-output (MIMO) communication system, i. e., only build on the basis of data that is already existent in today's systems.
Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction
A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding.
