arXiv subjects

Sofie Pollin

Publications and source records attributed to Sofie Pollin.

At least 37 records · Page 2Linked to original sources

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 Characterization of ISAC Channel Mapping and Environment Awareness

In the context of integrated sensing and communications (ISAC), this paper presents an experimental investigation of the relationship between monostatic sensing and naturally bistatic communication channels in an indoor millimeter-wave environment. We characterize the propagation channel in the joint delay--angle domain, extract dominant multipath components (MPCs) and associate them with physical scatterers in the environment, and demonstrate how communication MPCs can be explicitly recovered from sensing channels. Finally, the radar cross-sections (RCSs) of two key scatterers, namely the wall and metal plate, are obtained based on calibrated channel power and reconstructed propagation distances.

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arXiv2026

Approximation of the Range Ambiguity Function in Near-field Sensing Systems

This paper investigates the range ambiguity function of near-field systems where bandwidth and near-field beamfocusing jointly determine the resolution. First, the general matched filter ambiguity function is derived and the near-field array factors of different antenna array geometries are introduced. Next, the near-field ambiguity function is approximated as a product of the range-dependent near-field array factor and the ambiguity function due to the utilized waveform and bandwidth. An approximation criterion based on the aperture-bandwidth product is formulated, and its accuracy is examined. Finally, the improvements to the ambiguity function offered by the near-field beamfocusing, as compared to the far-field case, are presented. The performance gains are evaluated in terms of resolution improvement offered by beamfocusing, peak-to-sidelobe and integrated-sidelobe level improvement for a few popular array geometries. The gains offered by the near-field regime are shown to be range-dependent and substantial only in close proximity to the array.

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arXiv2026

Efficient Channel Autoencoders for Wideband Communications leveraging Walsh-Hadamard interleaving

This paper investigates how end-to-end (E2E) channel autoencoders (AEs) can achieve energy-efficient wideband communications by leveraging Walsh-Hadamard (WH) interleaved converters. WH interleaving enables high sampling rate analog-digital conversion with reduced power consumption using an analog WH transformation. We demonstrate that E2E-trained neural coded modulation can transparently adapt to the WH-transceiver hardware without requiring algorithmic redesign. Focusing on the short block length regime, we train WH-domain AEs and benchmark them against standard neural and conventional baselines, including 5G Polar codes. We quantify the system-level energy tradeoffs among baseband compute, channel signal-to-noise ratio (SNR), and analog converter power. Our analysis shows that the proposed WH-AE system can approach conventional Polar code SNR performance within 0.14dB while consuming comparable or lower system power. Compared to the best neural baseline, WH-AE achieves, on average, 29% higher energy efficiency (in bit/J) for the same reliability. These findings establish WH-domain learning as a viable path to energy-efficient, high-throughput wideband communications by explicitly balancing compute complexity, SNR, and analog power consumption.

cs.IT↗

arXiv2026

Robust Localization in OFDM-Based Massive MIMO through Phase Offset Calibration

Accurate localization in Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) systems depends critically on phase coherence across subcarriers and antennas. However, practical systems suffer from frequency-dependent and (spatial) antenna-dependent phase offsets, degrading localization accuracy. This paper analytically studies the impact of phase incoherence on localization performance under a static User Equipment (UE) and Line-of-Sight (LoS) scenario. We use two complementary tools. First, we derive the Cramér-Rao Lower Bound (CRLB) to quantify the theoretical limits under phase offsets. Then, we develop a Spatial Ambiguity Function (SAF)-based model to characterize ambiguity patterns. Simulation results reveal that spatial phase offsets severely degrade localization performance, while frequency phase offsets have a minor effect in the considered system configuration. To address this, we propose a robust Channel State Information (CSI) calibration framework and validate it using real-world measurements from a practical massive MIMO testbed. The experimental results confirm that the proposed calibration framework significantly improves the localization Root Mean Squared Error (RMSE) from 5 m to 1.2 cm, aligning well with the theoretical predictions.

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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-Cell Interference Rejection Based on Ultrawideband Walsh-Domain Wireless Autoencoding

This paper proposes a novel technique for rejecting partial-in-band inter-cell interference (ICI) in ultrawideband communication systems. We present the design of an end-to-end wireless autoencoder architecture that jointly optimizes the transmitter and receiver encoding/decoding in the Walsh domain to mitigate interference from coexisting narrower-band 5G base stations. By exploiting the orthogonality and self-inverse properties of Walsh functions, the system distributes and learns to encode bit-words across parallel Walsh branches. Through analytical modeling and simulation, we characterize how 5G CPOFDM interference maps into the Walsh domain and identify optimal ratios of transmission frequencies and sampling rate where the end-to-end autoencoder achieves the highest rejection. Experimental results show that the proposed autoencoder achieves up to 12 dB of ICI rejection while maintaining a low block error rate (BLER) for the same baseline channel noise, i.e., baseline Signal-to-Noise-Ratio (SNR) without the interference.

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arXiv2026

Machine Learning on the Edge for Sustainable IoT Networks: A Systematic Literature Review

The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges, such as high energy consumption and inefficient resource management. Addressing these issues is critical for the long-term viability of IoT networks. Machine learning (ML), with its proven success across various domains, offers promising solutions for optimizing IoT operations. ML algorithms can learn directly from raw data, uncovering hidden patterns and optimizing processes in dynamic environments. Executing ML at the edge of IoT networks can further enhance sustainability by reducing bandwidth usage, enabling real-time decision-making, and improving data privacy. Additionally, testing ML models on actual hardware is essential to ensure satisfactory performance under real-world conditions, as it captures the complexities and constraints of real-world IoT deployments. Combining ML at the edge and actual hardware testing, therefore, increases the reliability of ML models to effectively improve the sustainability of IoT systems. The present systematic literature review explores how ML can be utilized to enhance the sustainability of IoT networks, examining current methodologies, benefits, challenges, and future opportunities. Through our analysis, we aim to provide insights that will drive future innovations in making IoT networks more sustainable.

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arXiv2026

Frequency-Adaptive Multi-Band Architecture for Upper Mid-Band MIMO Systems

FR3 ($\approx$7-24 GHz), also referred to as the upper mid-band, has recently emerged as promising spectrum for 6G; however, its propagation and MIMO characteristics vary significantly with frequency and environment, and spectrum availability may be intermittent due to incumbents. Using site-specific ray tracing (Sionna RT) in representative indoor and outdoor scenarios, we evaluate 7, 10, 14, 20, and 24 GHz under SISO and MIMO configurations. The results show that FR3 exhibits propagation characteristics intermediate between sub-6 GHz and mmWave bands while supporting meaningful spatial multiplexing, albeit with strong site dependence. Motivated by these findings, we propose a fully digital frequency-adaptive multi-band MIMO architecture that repurposes ADCs/DACs and baseband processing resources across FR3 subbands via switching, enabling dynamic trade-offs between bandwidth (spectrum gain) and antenna consolidation (MIMO gain) under availability and channel constraints. Simulation results demonstrate that exploiting additional spectrum is often optimal, while adaptive resource repurposing becomes beneficial when subbands are unavailable or when multiplexing gains are concentrated at specific frequencies.

cs.IT↗

arXiv2026

Sizing Antenna Arrays for Near-field Communication and Sensing

This paper presents key performance metrics for near-field communication and sensing systems and their scaling behavior as a function of the antenna array aperture. Analytical expressions are derived for several standard array geometries to ease the design of the large antenna arrays under given system requirements. First, the near-field beam focusing is analyzed and the minimum beamdepth is observed to rapidly saturate to a low asymptotic limit as the array aperture increases. In contrast, the near-field region span is shown to scale quadratically with the array aperture. Based on these two metrics, the maximum number of resolvable beamspots at 3 dB separation is derived analytically, exhibiting a linear dependence on the array aperture. Moreover, when considering a region where the beamfocusing resolution does not exceed a specified threshold, the extent of the region is also shown to scale linearly with the array size. Finally, the number of significant singular values of a channel observed at the array's broadside is estimated, showing a power-law dependence on the aperture. The resulting expressions provide practical design guidelines for evaluating aperture requirements in near-field communication and sensing applications.

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arXiv2026

Site-Specific and Frequency-Dependent Channel Characterization and MIMO Performance in FR3

Next-generation wireless systems aim to enable on-demand connectivity through dynamic spectrum utilization. Motivated by this vision, this paper investigates the propagation characteristics and MIMO performance of the upper mid-band, spanning approximately 7-24 GHz and unofficially referred to as FR3. Using site-specific ray-tracing (RT) simulations based on the Sionna framework, we analyze indoor and outdoor environments at representative frequencies across FR1, FR3, and FR2, including 3.5, 7, 10, 14, 20, 24, and 28 GHz, under both single-antenna and multi-antenna configurations. The results show that FR3 exhibits intermediate propagation behavior between sub-6 GHz and millimeter-wave bands while sustaining effective spatial multiplexing and favorable spectral efficiency. Furthermore, large-array analysis indicates that performance gains in FR3 are closely tied to antenna scaling, highlighting the importance of large-size or large-aperture MIMO architectures for practical deployments.

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arXiv2025

Rethinking Signaling Design for ISAC: From Pilot-Based to Payload-Based Sensing

Integrated Sensing and Communications (ISAC) is emerging as a key enabler for 6G networks, with signaling design at the core of its evolution. This paper reviews the paradigm shift of ISAC signaling designs from pilot-aided sensing to data payload-based approaches, with a particular focus on how these techniques can be realized within existing 5G NR structures. We commence with the reuse of pilots and reference signals that exploit existing 5G New Radio (NR) structures for sensing. Then, we extend to more advanced approaches that integrate the data payload through novel constellation shaping, modulation bases, and pulse shaping filters. We highlight the opportunities and tradeoffs that arise when extending sensing from sparse pilot and reference signal resources to the full communication frame, emphasizing how constellation properties and modulation choices directly determine sensing performance. To illustrate practical feasibility, a case study on sensing-assisted NR Vehicle-to-Infrastructure (V2I) networks demonstrates how exploiting both reference signals and payload echoes can reduce signaling overhead and enable proactive beam management and handover.

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arXiv2025

Spatially Consistent Air-to-Ground Channel Modeling and Simulation via 3D Shadow Projections

We present an approach for spatially-consistent semi-deterministic Air-to-Ground (A2G) channel modeling in Unmanned Aerial Vehicle-assisted networks. We use efficient 3D building shadow projections to determine Line-of-Sight (LOS) regions, enabling fast generation of LOS maps. By integrating LOS-aware deterministic path loss with stochastic shadow fading, the approach produces spatially consistent A2G radio maps suitable for environment- and mobility-aware channel evaluation and performance prediction. Simulation results in ITU-compliant Manhattan grid environments demonstrate the model's ability to reflect key urban propagation characteristics, such as LOS blockage patterns and outage behavior. The proposed approach provides an efficient alternative to ray tracing or fully stochastic models, with particular relevance for user mobility, link planning, and radio map generation in 6G non-terrestrial networks.

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arXiv2025

Frequency Diverse Array OFDM Transmit System with Partial Overlap in Frequency

A frequency-diverse array (FDA) is an alternative array architecture in which each antenna is preceded by a mixer instead of a phase shifter. The mixers introduce a frequency offset between signals transmitted by each antenna, resulting in a time-varying beam pattern. However, time-dependent beamforming is not desirable for communication or sensing. In this paper, the FDA is combined with orthogonal frequency-division multiplexing (OFDM) modulation. The proposed beamforming method partitions the OFDM symbol transmitted by all antennas into subcarrier blocks, which carry the same data but are precoded differently. The frequency offset between the antennas is equal to the subcarrier block width. Consequently, each antenna transmits a differently precoded subcarrier block at the center frequency, resulting in overlap and coherent summation of the blocks. Proposed architecture enables fully digital beamforming over a single block while requiring only a single digital-to-analog converter. The system's performance and tradeoffs are investigated in the context of joint communication and sensing.

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arXiv2025

Location-Informed Interference Suppression Precoding Methods for Distributed Massive MIMO Systems

The evolution of mobile networks towards user-centric cell-free distributed Massive MIMO configurations requires the development of novel signal processing techniques. More specifically, digital precoding algorithms have to be designed or adopted to enable distributed operation. Future deployments are expected to improve coexistence between cellular generations, and between mobile networks and incumbent services such as radar. In dense cell-free deployments, it might also not be possible to have full channel state information for all users at all antennas. To leverage location information in a dense deployment area, we suggest and investigate several algorithmic alterations on existing precoding methods, aimed at location-informed interference suppression, for usage in existing and emerging systems where user locations are known. The proposed algorithms are derived using a theoretical channel model and validated and numerically evaluated using an empirical dataset containing channel measurements from an indoor distributed Massive MIMO testbed. When dealing with measured CSI, the impact of the hardware, in addition to the location-based channel, needs to be compensated for. We propose a method to calibrate the hardware and achieve measurement-based evaluation of our location-based interference suppression algorithms. The results demonstrate that the proposed methods allow location-based interference suppression without explicit CSI knowledge at the transmitter, under certain realistic network conditions.

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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

Near-Field Spatial non-Stationary Channel Estimation: Visibility-Region-HMM-Aided Polar-Domain Simultaneous OMP

This work focuses on channel estimation in extremely large aperture array (ELAA) systems, where near-field propagation and spatial non-stationarity introduce complexities that hinder the effectiveness of traditional estimation techniques. A physics-based hybrid channel model is developed, incorporating non-binary visibility region (VR) masks to simulate diffraction-induced power variations across the antenna array. To address the estimation challenges posed by these channel conditions, a novel algorithm is proposed: Visibility-Region-HMM-Aided Polar-Domain Simultaneous Orthogonal Matching Pursuit (VR-HMM-P-SOMP). The method extends a greedy sparse recovery framework by integrating VR estimation through a hidden Markov model (HMM), using a novel emission formulation and Viterbi decoding. This allows the algorithm to adaptively mask steering vectors and account for spatial non-stationarity at the antenna level. Simulation results demonstrate that the proposed method enhances estimation accuracy compared to existing techniques, particularly in low-SNR and sparse scenarios, while maintaining a low computational complexity. The algorithm presents robustness across a range of design parameters and channel conditions, offering a practical solution for ELAA systems.

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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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