arXiv subjects

Achiel Colpaert

Publications and source records attributed to Achiel Colpaert.

17 recordsLinked to original sources

arXiv2026

Depthwise Separable CNN for D-MIMO Indoor Localization with Data Reduction

Indoor localization using Distributed Multiple-Input Multiple-Output (D-MIMO) and machine learning (ML) achieves sub-centimeter accuracy but faces midhaul capacity bottlenecks when transmitting raw Channel State Information (CSI) in Open Radio Access Networks (O-RAN) architectures. To address this, we propose a lightweight, distributed ML framework that shifts initial processing to the network edge. By deploying localized models as dApps on Distributed Units (DUs), each requiring just 1.39 MB of memory and 1.96 MFLOPs, the system performs CSI feature extraction and reduction on the edge. The reduced low-dimensional features are transmitted to the Central Unit (CU), where another dApp is deployed for location estimation. Evaluated on a high-density dataset, this framework reduces midhaul traffic by 100x while maintaining an average error of 8.5 mm, even with half the deployed Radio Units (RUs), providing a scalable blueprint for practical D-MIMO localization.

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arXiv2026

LITE: Lightweight Channel Gain Estimation with Reduced X-Haul CSI Signaling in O-RAN

Cell-Free Massive Multiple-Input Multiple-Output (CF-MaMIMO) in Open Radio Access Network (O-RAN) promises high spectral efficiency but is limited by frequent Channel State Information (CSI) exchanges, which strain fronthaul/midhaul/backhaul (X-haul) bandwidth and exceed the capabilities of existing approaches relying on uncompressed CSI or heavy predictors. To overcome these constraints, we propose LITE, a lightweight pipeline combining a 1-D convolutional Autoencoder (AE) at the O-RAN Distributed Unit (O-DU) with a Squeeze-and-Excitation (SE)-enhanced Bidirectional Long Short-Term Memory (BiLSTM) predictor at the Near-Real-Time RAN Intelligent Controller (Near-RT-RIC), enabling short-horizon trajectory-unaware forecasting under strict transport and processing budgets. LITE applies 50% CSI compression and an asymmetric SE-BiLSTM, reducing model complexity by 83.39% while improving accuracy by 5% relative to a baseline BiLSTM. With compression-aware training, the Lightweight Intelligent Trajectory Estimator (LITE) incurs only 6% accuracy loss versus the BiLSTM baseline, outperforming independent and end-to-end strategies. A TensorRT-optimized implementation achieves 147k Queries per Second (QPS), a 4.6x throughput gain. These results demonstrate that LITE delivers X-haul-efficient, low-latency, and deployment-ready channel-gain prediction compatible with O-RAN splits.

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arXiv2026

Dualband OFDM Delay Estimation for Multi-Target Localization

Integrated localization and communication systems aim to reuse communication waveforms for simultaneous data transmission and localization, but delay resolution is fundamentally limited by the available bandwidth. In practice, large contiguous bandwidths are difficult to obtain due to hardware constraints and spectrum fragmentation. Aggregating non-contiguous narrow bands can increase the effective frequency span, but a non-contiguous frequency layout introduces challenges such as elevated sidelobes and ambiguity in delay estimation. This paper introduces a point-spread-function (PSF)-centric framework for dual-band OFDM delay estimation. We model the observed delay profile as the convolution of the true target response with a PSF determined by the dual-band subcarrier selection pattern, explicitly linking band configuration to resolution and ambiguity. To suppress PSF-induced artifacts, we adapt the RELAX algorithm for dual-band multi-target delay estimation. Simulations demonstrate improved robustness and accuracy in dual-band scenarios, supporting ILC under fragmented spectrum.

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arXiv2026

Experimental Validation of SBFD ISAC in an FR3 Distributed SIMO Testbed

Integrated sensing and communication (ISAC) is a key enabler for future radio networks. This paper presents a sub-band full-duplex (SBFD) ISAC system that assigns non-overlapping OFDM subbands to sensing and communication, enabling simultaneous operation with minimal interference. A distributed testbed with three SIMO nodes is implemented using USRP X410 devices operating at 6.8 GHz with 20 MHz bandwidth per channel. A total of 2048 OFDM subcarriers are partitioned into three subbands: two for sensing using Zadoff-Chu sequences and one for communication using QPSK. Each USRP transmits one subband while receiving signals across all three, forming a 1 x 3 SIMO node. Time synchronization is achieved through host-server coordination without external clock distribution. Indoor measurements, validated against MOCAP ground truth, confirm the feasibility of the SBFD ISAC system. The results demonstrate monostatic sensing with a velocity resolution of 0.145 m/s, and communication under NLoS conditions with a BER of 3.63e-3. Compared with a multiband benchmark requiring three times more spectrum, the SBFD configuration achieves comparable velocity estimation accuracy while conserving resources. The sensing and communication performance trade-off is determined by subcarrier allocation strategy rather than mutual interference.

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arXiv2026

Inter-patient ECG Arrhythmia Classification with LGNs and LUTNs

Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) are demonstrated to be suitable for the automatic classification of electrocardiograms (ECGs) using the inter-patient paradigm. The methods are benchmarked using the MIT-BIH arrhythmia data set, achieving up to 94.28% accuracy and a $j\kappa$ index of 0.683 on a four-class classification problem. Our models use between 2.89k and 6.17k FLOPs, including preprocessing and readout, which is three to six orders of magnitude less compared to SOTA methods. A novel preprocessing method is utilized that attains superior performance compared to existing methods for both the mixed-patient and inter-patient paradigms. In addition, a novel method for training the Lookup Tables (LUTs) in LUTNs is devised that uses the Boolean equation of a multiplexer (MUX). Additionally, rate coding was utilized for the first time in these LGNs and LUTNs, enhancing the performance of LGNs. Furthermore, it is the first time that LGNs and LUTNs have been benchmarked on the MIT-BIH arrhythmia dataset using the inter-patient paradigm. Using an Artix 7 FPGA, between 2000 and 2990 LUTs were needed, and between 5 to 7 mW (i.e. 50 pJ to 70 pJ per inference) was estimated for running these models. The performance in terms of both accuracy and $j\kappa$-index is significantly higher compared to previous LGN results. These positive results suggest that one can utilize LGNs and LUTNs for the detection of arrhythmias at extremely low power and high speeds in heart implants or wearable devices, even for patients not included in the training set.

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arXiv2025

Trajectory-Aware Air-to-Ground Channel Characterization for Low-Altitude UAVs Using MaMIMO Measurements

This paper presents a comprehensive measurement-based trajectory-aware characterization of low-altitude Air-to-Ground (A2G) channels in a suburban environment. A 64-element Massive Multi-Input Multi-Output (MaMIMO) array was used to capture channels for three trajectories of an Uncrewed Aerial Vehicle (UAV), including two horizontal zig-zag flights at fixed altitudes and one vertical ascent, chosen to emulate AUE operations and to induce controlled azimuth and elevation sweeps for analyzing geometry-dependent propagation dynamics. We examine large-scale power variations and their correlation with geometric features, such as elevation, azimuth, and 3D distance, followed by an analysis of fading behavior through distribution fitting and Rician K-factor estimation. Furthermore, temporal non-stationarity is quantified using the Correlation Matrix Distance (CMD), and angular stationarity spans are utilized to demonstrate how channel characteristics change with the movement of the UAV. We also analyze Spectral Efficiency (SE) in relation to K-factor and Root Mean Square (RMS) delay spread, highlighting their combined influence on link performance. The results show that the elevation angle is the strongest predictor of the received power, with a correlation of more than 0.77 for each trajectory, while the Nakagami model best fits the small-scale fading. The K-factor increases from approximately 5 dB at low altitudes to over 15 dB at higher elevations, indicating stronger LoS dominance. Non-stationarity patterns are highly trajectory- and geometry-dependent, with azimuth most affected in horizontal flights and elevation during vertical flight. These findings offer valuable insights for modeling and improving UAV communication channels in 6G Non-Terrestrial Networks (NTNs).

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arXiv2025

CASH: Context-Aware Smart Handover for Reliable UAV Connectivity on Aerial Corridors

Urban Air Mobility (UAM) envisions aerial corridors for Unmanned Aerial Vehicles (UAVs) to reduce ground traffic congestion by supporting 3D mobility, such as air taxis. A key challenge in these high-mobility aerial corridors is ensuring reliable connectivity, where frequent handovers can degrade network performance. To resolve this, we present a Context-Aware Smart Handover (CASH) protocol that uses a forward-looking scoring mechanism based on UAV trajectory to make proactive handover decisions. We evaluate the performance of the proposed CASH against existing handover protocols in a custom-built simulator. Results show that CASH reduces handover frequency by up to 78% while maintaining low outage probability. We then investigate the impact of base station density and safety margin on handover performance, where their optimal setups are empirically obtained to ensure reliable UAM communication.

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arXiv2025

Evaluation of 3D Terrestrial and Aerial Spectrum Sharing with Massive MIMO Systems

Connecting aerial and terrestrial users with a single base station (BS) is increasingly challenging due to the rising number of aerial users like unmanned aerial vehicles (UAVs). Traditional BSs, designed with down-tilted beams, focus mainly on ground users, but massive MIMO (mMIMO) systems can significantly enhance coverage in low-altitude airspace. This paper analyzes how a mMIMO BS serves both aerial and terrestrial users in a 3D spectrum-sharing scheme. Using Semi-orthogonal User Selection (SUS) and random scheduling, we assess the spectral efficiency and performance limits of these systems. Results reveal that mMIMO effectively supports more terrestrial users, influenced by channel characteristics and user scheduling strategies, providing key insights for future 3D aerial-terrestrial networks.

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arXiv2024

Building a real-time physical layer labeled data logging facility for 6G research

This work describes the architecture and vision of designing and implementing a new test infrastructure for 6G physical layer research at KU Leuven. The Testbed is designed for physical layer research and experimentation following several emerging trends, such as cell-free networking, integrated communication, sensing, open disaggregated Radio Access Networks, AI-Native design, and multiband operation. The software is almost entirely based on free and open-source software, making contributing and reusing any component easy. The open Testbed is designed to provide real-time and labeled data on all parts of the physical layer, from raw IQ data to synchronization statistics, channel state information, or symbol/bit/packet error rates. Real-time labeled datasets can be collected by synchronizing the physical layer data logging with a positioning and motion capture system. One of the main goals of the design is to make it open and accessible to external users remotely. Most tests and data captures can easily be automated, and experiment code can be remotely deployed using standard containers (e.g., Docker or Podman). Finally, the paper describes how the Testbed can be used for our research on joint communication and sensing, over-the-air synchronization, distributed processing, and AI in the loop.

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arXiv2023

CSI Measurements and Initial Results for Massive MIMO to UAV Communication

Non-Terrestrial Network (NTN) has been envisioned as a key component of the sixth-generation (6G) mobile communication system. Meanwhile, unmanned aerial vehicles (UAVs) play an important role in enabling and deploying NTNs. In this paper, we focus on massive multi-input multi-output (MaMIMO) supported UAV communications, where channel state information (CSI) was measured considering different heights and trajectories of a rotary-wing drone. To characterize the propagation channel for this air-to-ground link, some initial results were analyzed, such as stationary distance. To investigate the impact of channels on communication performance, we analyzed spectral efficiency (SE) by using Maximum Ratio Combining (MRC). This study shows that the presented space-time-frequency channel dataset facilitates channel correlation analysis and supports performance evaluation for MaMIMO-UAV communications.

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arXiv2023

3D Non-Stationary Channel Measurement and Analysis for MaMIMO-UAV Communications

Unmanned aerial vehicles (UAVs) have gained popularity in the communications research community because of their versatility in placement and potential to extend the functions of communication networks. However, there remains still a gap in existing works regarding detailed and measurement-verified air-to-ground (A2G) Massive Multi-Input Multi-Output (MaMIMO) channel characteristics which play an important role in realistic deployment. In this paper, we first design a UAV MaMIMO communication platform for channel acquisition. We then use the testbed to measure uplink Channel State Information (CSI) between a rotary-wing drone and a 64-element MaMIMO base station (BS). For characterization, we focus on multidimensional channel stationarity which is a fundamental metric in communication systems. Afterward, we present measurement results and analyze the channel statistics based on power delay profiles (PDPs) considering space, time, and frequency domains. We propose the stationary angle (SA) as a supplementary metric of stationary distance (SD) in the time domain. We analyze the coherence bandwidth and RMS delay spread for frequency stationarity. Finally, spatial correlations between elements are analyzed to indicate the spatial stationarity of the array. The space-time-frequency channel stationary characterization will benefit the physical layer design of MaMIMO-UAV communications.

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arXiv2023

Path Loss Analysis for Low-Altitude Air-to-Air Millimeter-Wave Channel in Built-Up Area

Communications between unmanned aerial vehicles (UAVs) play an important role in deploying aerial networks. Although some studies reveal that drone-based air-to-air (A2A) channels are relatively clear and thus can be modeled as free-space propagation, such an assumption may not be applicable to drones flying in low altitudes of built-up environments. In practice, low-altitude A2A channel modeling becomes more challenging in urban scenarios since buildings can obstruct the line-of-sight (LOS) path, and multipaths from buildings lead to additional losses. Therefore, we herein focus on modeling low-altitude A2A channels considering a generic urban deployment, where we introduce the evidence of the small-size first Fresnel zone at the millimeter-wave (mmWave) band to approximately derive the LOS probability. Then, the path loss under different propagation conditions is investigated to obtain an integrated path loss model. In addition, we incorporate the impact of imperfect beam alignment on the path loss, where the relation between path loss fluctuation and beam misalignment level is modeled as an exponential form. Finally, comparisons with the 3GPP model show the effectiveness of the proposed analytical model. Numerical simulations in different environments and heights provide practical deployment guidance for aerial networks.

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arXiv2022

Massive MIMO Channel Measurement Data Set for Localization and Communication

Channel state information (CSI) needs to be estimated for reliable and efficient communication, however, location information is hidden inside and can be further exploited. This article presents a detailed description of a Massive Multi-Input Multi-Output (MaMIMO) testbed and provides a set of experimental location-labelled CSI data. In this article, we focus on the design of the hardware and software of a MaMIMO testbed for gathering multiple CSI data sets. We also show this data can be used for learning-based localization and enhanced communication research. The data set presented in this work is made fully available to the research community. We show a CSI-based joint communication and sensing processing pipeline can be evaluated and designed based on the collected data set. Specifically, the localization output obtained by a convolutional neural network (CNN) trained on the data sets is used to schedule users for improving the spectral efficiency (SE) of the communication system. Finally, we pose promising directions on further exploiting this data set and creating future data sets.

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arXiv2022

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the recent technological advancements and manufacturing cost reduction opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem. We discuss important features of NTNs integration into TNs and the synergies by delving into the new range of services and use cases, various architectures, technological enablers, and higher layer aspects pertinent to NTNs integration. Moreover, we review the corresponding challenges arising from the technical peculiarities and the new approaches being adopted to develop efficient integrated ground-air-space (GAS) networks. Our survey further includes the major progress and outcomes from academic research as well as industrial efforts representing the main industrial trends, field trials, and prototyping towards the 6G networks.

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arXiv2022

Drone delivery: Reliable Cellular UAV Communication Using Multi-Operator Diversity

The market size of Unmanned Aerial Vehicles (UAVs, a.k.a drones) can reach up to 10% of the global market value. In particular, drone delivery is one of the most attractive applications. The growing number of drones requires appropriate traffic management systems that will rely on cellular networks. However, it has been shown in the literature that these networks cannot provide reliable communication due to low coverage probability and frequent handovers. This article presents a potential solution targeting these problems while requiring no modifications of the existing infrastructure. Namely, equipping the UAV with multiple cellular modems to connect to different providers' networks introduces network diversity resulting in 98% coverage probability at the flight altitude of 100 meters. In contrast, one network ensures only 80% coverage. At the same time, the size of the outage zones becomes up to ten times smaller and the frequency of harmful handovers is reduced to zero. The results are obtained with a physical-layer simulator utilizing a real urban 3D environment, cellular network parameters (e.g., site locations, antenna orientation and gains), and specific aerial channel models.

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arXiv2020

3D beamforming and handover analysis for UAV networks

In future drone applications fast moving unmanned aerial vehicles (UAVs) will need to be connected via a high throughput ultra reliable wireless link. MmWave communication is assumed to be a promising technology for UAV communication, as the narrow beams cause little interference to and from the ground. A challenge for such networks is the beamforming requirement, and the fact that frequent handovers are required as the cells are small. In the UAV communication research community, mobility and especially handovers are often neglected, however when considering beamforming, antenna array sizes start to matter and the effect of azimuth and elevation should be studied, especially their impact on handover rate and outage capacity. This paper aims to fill some of this knowledge gap and to shed some light on the existing problems. This work will analyse the performance of 3D beamforming and handovers for UAV networks through a case study of a realistic 5G deployment using mmWave. We will look at the performance of a UAV flying over a city utilizing a beamformed mmWave link.

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arXiv2020

Fixed mmWave Multi-User MIMO: Performance Analysis and Proof-of-Concept Architecture

In this paper, we present a fixed mmWave Multi-User Multiple-Input Multiple-Output (MIMO) system for fixed wireless access with a unique architecture. A digital MIMO system is combined with an analog multi-beam antenna array which uses a high-dimension 16x16 Butler matrix to obtain 16 orthogonal beams. A system model of this architecture is presented and used to simulate its performance comparing to the performance of common-used patch antennas. Several MIMO precoding techniques are considered and compared with basic analog beamforming. To verify these results, a prototype is built and a dedicated measurement campaign is performed. The results show that the system model is a good approximation and that the use of the multi-beam antenna array is a good alternative to patch antennas for a large number of users.

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