45 citations · 47 across the 2 of their papers we have counts for
13 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…
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…
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…