45 citations · 166 across the 16 of their papers we have counts for
18 papers
Benchmarking Learnt Radio Localisation under Distribution Shift
Maximilian Arnold, Mohammed Alloulah
Deploying radio frequency (RF) localisation systems invariably entails non-trivial effort, particularly for the latest learning-based breeds. There has been little prior work on ch…
Probabilistic 5G Indoor Positioning Proof of Concept with Outlier Rejection
Marcus Henninger, Traian E. Abrudan, Silvio Mandelli +6
The continuously increasing bandwidth and antenna aperture available in wireless networks laid the foundation for developing competitive positioning solutions relying on communicat…
Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence
Mohammed Alloulah, Maximilian Arnold
Next generation cellular networks will implement radio sensing functions alongside customary communications, thereby enabling unprecedented worldwide sensing coverage outdoors. Dee…
MaxRay: A Raytracing-based Integrated Sensing and Communication Framework
M. Arnold, M. Bauhofer, S. Mandelli +4
Integrated Sensing And Communication (ISAC)forms a symbiosis between the human need for communication and the need for increasing productivity, by extracting environmental informat…
Self-Supervised Radio-Visual Representation Learning for 6G Sensing
Mohammed Alloulah, Akash Deep Singh, Maximilian Arnold
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 uni…
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer
Brian Rappaport, Emre Gönültaş, Jakob Hoydis +3
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state informa…
Deep Inertial Navigation using Continuous Domain Adaptation and Optimal Transport
Mohammed Alloulah, Maximilian Arnold, Anton Isopoussu
In this paper, we propose a new strategy for learning inertial robotic navigation models. The proposed strategy enhances the generalisability of end-to-end inertial modelling, and…
Indoor positioning systems: Smart fusion of a variety of sensor readings
M. Arnold, F. Schaich
Robust and versatile localization techniques are key to the success of the next industrial revolution. Yet, it is uncertain which combination of sensors will be the most robust and…
Massive-MIMO Iterative Channel Estimation and Decoding (MICED) in the Uplink
Daniel Verenzuela, Emil Björnson, Xiaojie Wang +2
Massive MIMO uses a large number of antennas to increase the spectral efficiency (SE) through spatial multiplexing of users, which requires accurate channel state information. It i…
A Computationally Efficient 2D MUSIC Approach for 5G and 6G Sensing Networks
Marcus Henninger, Silvio Mandelli, Maximilian Arnold +1
Future cellular networks are intended to have the ability to sense the environment by utilizing reflections of transmitted signals. Multi-dimensional sensing brings along the cruci…
Massive MIMO Channel Measurements and Achievable Rates in a Residential Area
Marc Gauger, Maximilian Arnold, Stephan ten Brink
In this paper we present a measurement set-up for massive MIMO channel sounding that shows very good long-term phase stability. Initial measurements were performed in a residential…
Towards Practical Indoor Positioning Based on Massive MIMO Systems
Mark Widmaier, Maximilian Arnold, Sebastian Dörner +2
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 mul…
Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction
Maximilian Arnold, Sebastian Dörner, Sebastian Cammerer +3
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…
Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning
Maximilian Arnold, Jakob Hoydis, Stephan ten Brink
With a significant increase in area throughput, Massive MIMO has become an enabling technology for fifth generation (5G) wireless mobile communication systems. Although prototypes…
Subcarrier-Interlaced FDD for Faster-than-TDD Channel Tracking in Massive MIMO Systems
Maximilian Arnold, Xiaojie Wang, Stephan ten Brink
Canonical Massive MIMO uses time division duplex (TDD) to exploit channel reciprocity within the coherence time, avoiding feedback of channel state information (CSI), as is require…
Area Rate Evaluation based on Spatial Clustering of massive MIMO Channel Measurements
Maximilian Arnold, Johannes Pfeiffer, Stephan ten Brink
Channel models for massive MIMO are typically based on matrices with complex Gaussian entries, extended by the Kronecker and Weichselberger model. One reason for observing a gap be…
Evaluating Massive MIMO Precoding based on 3D-Channel Measurements with a Spider Antenna
Maximilian Arnold, Marc Gauger, Stephan ten Brink
Massive Multiple-Input Multiple-Output (MIMO)communications uses a large number of antennas at the base station to increase the data rate and user density in future wireless system…
On Deep Learning-based Massive MIMO Indoor User Localization
Maximilian Arnold, Sebastian Dörner, Sebastian Cammerer +1
We examine the usability of deep neural networks for multiple-input multiple-output (MIMO) user positioning solely based on the orthogonal frequency division multiplex (OFDM) compl…