arXiv subjects

Vassilis Paliouras

Publications and source records attributed to Vassilis Paliouras.

11 recordsLinked to original sources

arXiv2026

RIS Beamforming under Element-Level Variations: Statistical Characterization and Robust Design

In this paper, a novel analytical framework to characterize the impact of element-level variations on the radiation characteristics of reconfigurable intelligent surfaces (RISs) is introduced. Specifically, a statistical model is proposed to capture the effects of varactor capacitance fluctuations on the RIS reflection coefficients, and, subsequently, on the resulting power radiation pattern; both low- and large-variance independent perturbation scenarios, are investigated. Leveraging the proposed statistical model, a low complexity greedy optimization methodology is presented, having the goal to optimize the expected RIS radiation power, thereby, generating inherently robust configurations. Furthermore, the analytical proposed model serves as an efficient alternative to computationally expensive Monte Carlo simulations, enabling the quantification of element sensitivity to manufacturing and operational tolerances. As demonstrated, optimizing the mean power pattern significantly enhances system performance under element-level variations. For typical RIS sizes (e.g., 32x32 or 64x64), a main lobe gain exceeding 2 dB and a sidelobe suppression of approximately 10 dB are achieved.

eess.SP↗

arXiv2026

Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems

Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffective. Conventional compressed-sensing (CS) mitigation exhibits high sequential latency and leaves structured, non-Gaussian residuals that cause log-likelihood ratio (LLR) unreliability, decoder saturation, and severe error floors when employing classical Gaussian demappers. We resolve this pipeline mismatch using a unified deep learning framework for joint NBI cancellation and robust soft demodulation. First, NBI-CNet employs a physics-informed convolutional architecture to estimate NBI parameters and remove multi-tone interference in a single forward pass. Without requiring prior knowledge of the active interferer count, NBI-CNet reduces computational complexity by up to 60% ($N{=}2048, Q{=}64$) compared to the state-of-the-art EOMP-IDS algorithm. Second, LLR-CNet acts as a structural whitener by mapping non-Gaussian post-mitigation residuals onto well-calibrated soft metrics. Simulations demonstrate that this joint framework eliminates the error floors inherent to traditional baselines across dense grids. Under severe interference ($\text{SIR}{=}{-}10$ dB), the pipeline operates within a $0.2$ to $0.5$ dB SNR margin of the optimal iterative baseline at a target block error rate (BLER) of $10^{-4}$. Under mild interference ($\text{SIR}{=}10$ dB) with heavy spectral overlap ($Q{=}12$), where classical greedy algorithms erroneously subtract valid data components and corrupt the payload, NBI-CNet avoids signal-peak confusion to deliver a coding gain exceeding $3$ dB. Finally, the architecture circumvents the $2{\times}10^{-4}$ error floor triggered by interferer-estimation errors, while its scale-invariant design enables robust generalization across arbitrary FFT sizes without retraining.

cs.LG↗

arXiv2026

A Reconfigurable and Representation-Adaptive ISA-Based Architecture for Efficient DNN Acceleration

Domain-specific hardware accelerators provide significantly higher performance and energy efficiency for deep neural network (DNN) workloads than general-purpose processors, but often lack adaptability to evolving model architectures. In contrast, general-purpose ISA-based solutions, such as RISC-V-based accelerators, improve programmability at the cost of efficiency. This work addresses this tradeoff by introducing a machine-learning-oriented instruction set architecture (ISA) and a reconfigurable hardware platform that combine high efficiency with flexibility. The proposed ISA enables fine-grained control over data movement, dynamic precision, and decoupled execution across data-fetching, tensor processing, and post-processing domains. The corresponding architecture employs lightweight programmable cores and SIMD units to maintain high processing-element utilization with low control overhead, while remaining independent of the underlying numerical representation. We demonstrate the approach using a Residue Number System (RNS) instantiation supporting 3-8-bit dynamic precision. A 22-nm implementation achieves 5.12-10.47 TOPS/W for a typical workload and up to 1.2x higher energy efficiency than its fixed-point counterpart, while preserving model accuracy. It also outperforms state-of-the-art and mixed-precision accelerators. These results show that the proposed design effectively bridges the gap between efficiency and programmability in modern DNN accelerators.

cs.AR↗

arXiv2026

Threat Detection and Resilience Techniques in PRS-Assisted OTDOA 5G Positioning Systems

Precise positioning is a key enabler for emerging 5G applications, from autonomous transport to industrial automation. Yet the open physical layer (PL) leaves standard positioning reference signals (PRSs) vulnerable to manipulation. This work addresses the security of downlink observed time difference of arrival positioning (DL-OTDOA) through three contributions. First, we introduce VeriLoc, an open-source system-level simulator designed for realistic channel modeling and PL threat injection. Second, we propose three novel security techniques to enhance resilience and threat detection: encrypted PRS to prevent adversarial waveform synthesis, angular-based source authentication (ABSA), and a cross-layer downlink-uplink handshaking protocol to detect attacks that cannot be mitigated by encryption. Third, utilizing VeriLoc, we evaluate the proposed techniques alongside position tracking and a PRS authentication scheme, which extends the original hash-based message authentication code (HMAC) scheme design to support digital signatures. Simulation results demonstrate that while encryption, authentication schemes, and tracking robustly counter selective PRS spoofing and jamming, the proposed spatial and cross-layer mechanisms are essential for detecting meaconing, collectively maintaining attack detection rates in excess of 90% while keeping false alarm rates minimal.

eess.SP↗

arXiv2025

RIS-Aided Localization and Sensing

High-precision localization and environmental sensing are essential for a new wave of applications, ranging from industrial automation and autonomous systems to augmented reality and remote healthcare. Conventional wireless methods, however, often face limitations in accuracy, reliability, and coverage, especially in complex non-line-of-sight (NLoS) environments. Reconfigurable Intelligent Surfaces (RISs) have emerged as a key enabling technology, offering dynamic control over the radio propagation environment to overcome these challenges. This chapter provides a comprehensive overview of RIS-aided localization and sensing, bridging fundamental theory with practical implementation. The core principles of the RIS technology are first described detailing how programmable metasurfaces can intelligently combat blockages, enhance signal diversity, and create virtual line-of-sight (LoS) links. The chapter then reviews a range of application scenarios where RISs can offer significant improvements. A significant portion of the chapter is dedicated to algorithmic methodologies, covering beam sweeping protocols, codebook-based techniques, and advanced optimization and machine learning strategies for both localization and sensing. To validate the theoretical concepts in real-world conditions, recent experimental results using an RIS prototype are detailed, showcasing the technology's efficacy and illustrating key performance trade-offs.

eess.SP↗

arXiv2025

Optimizing Indoor RIS-Aided Physical-Layer Security: A Codebook-Generation Methodology and Measurement-Based Analysis

Sixth-Generation (6G) wireless networks aim to support innovative Internet-of-Things (IoT) applications that demand faster and more secure data transmission. While higher Open Systems Interconnection (OSI) layers employ measures like encryption and secure protocols to address data security, Physical-Layer Security (PLS) focuses on preventing information leakage to EavesDroppers (EDs) and mitigating the effects of jammers and spoofing attacks. In this context, the emerging technology of Reconfigurable Intelligent Surfaces (RISs) can play an instrumental role, enhancing PLS by intelligently reflecting electromagnetic waves to benefit Legitimate Users (LUs) while obstructing EDs. This paper presents practical indoor measurements to evaluate the capability of an RIS to enhance PLS, focusing on a varactor-based RIS technology designed for the FR1 band at 3.55 GHz. A comparative analysis of state-of-the-art RIS-aided secrecy optimization algorithms together with a novel approach designed in this paper, which relies on a newly generated RIS phase configuration codebook, highlight the potential of RISs to improve both data rates for LUs as well as secrecy against EDs in real-world indoor multipath environments. The results also demonstrate the frequency selectivity of the RIS, proviging practical insights on the optimization of the technology.

eess.SP↗

arXiv2025

Theoretical and Experimental Evaluation of AoA Estimation in Single-Anchor 5G Uplink Positioning

As we move towards 6G, the demand for high-precision, cost-effective positioning solutions becomes increasingly critical. Single-anchor positioning offers a promising alternative to traditional multi-anchor approaches, particularly in complex propagation environments where infrastructure costs and deployment constraints present significant challenges. This paper provides a comprehensive evaluation of key algorithmic choices in the development of a single-anchor 5G uplink positioning testbed. Our developed testbed uses angle of arrival (AoA) estimation combined with range measurements from an ultra-wideband pair, to derive the position. The simulations conducted assess the impact of the selected algorithms on channel order and AoA estimation, while the influence of antenna calibration errors on AoA estimation is also examined. Finally, we compare simulations and results obtained from our developed platform.

eess.SP↗

arXiv2025

Evaluating Beam Sweeping for AoA Estimation with an RIS Prototype: Indoor/Outdoor Field Trials

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology to enhance wireless communication systems by enabling dynamic control over the propagation environment. However, practical experiments are crucial towards the validation of the theoretical potential of RISs while establishing their real-world applicability, especially since most studies rely on simplified models and lack comprehensive field trials. In this paper, we present an efficient method for configuring a $1$-bit RIS prototype at sub-$6$ GHz, resulting in a codebook oriented for beam sweeping; an essential protocol for initial access and Angle of Arrival (AoA) estimation. The measured radiation patterns of the RIS validate the theoretical model, demonstrating consistency between the experimental results and the predicted beamforming behavior. Furthermore, we experimentally prove that RIS can alter channel properties and by harnessing the diversity it provides, we evaluate beam sweeping as an AoA estimation technique. Finally, we investigate the frequency selectivity of the RIS and propose an approach to address indoor challenges by leveraging the geometry of environment.

cs.IT↗

arXiv2024

Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenarios

This paper presents a comprehensive exploration of Angle of Arrival (AoA) estimation techniques in 5G environments, using the Sounding Reference Signal (SRS) in Uplink scenarios both in simulations and with actual measurements. Leveraging 5G capabilities, we investigate AoA algorithms for single-base station positioning. The study includes simulations and practical tests on a developed dedicated testbed featuring a base station equipped with a three-element Uniform Linear Array (ULA), considering Line of Sight conditions in an open environment. The testbed, employing Ettus E312 as the transmitter and Ettus N310 as the receiver, details waveform structures and reception processes. Additionally, our study examines the performance of Angle of Arrival (AoA) estimation algorithms, such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT), and Joint Angle and Delay Estimation (JADE) ESPRIT. A MATLAB ray tracing propagation model of the environment where the measurements are conducted, has been constructed. Simulation results using this model are presented, along with the actual measurements. The obtained results affirm the effectiveness of our implementation.

eess.SP↗

arXiv2024

USRP-Based Single Anchor Positioning: AoA with 5G Uplink Signals, and UWB Ranging

This paper presents a novel testbed designed for 5th-Generation (5G) positioning using Universal Software Radio Peripherals (USRPs). The testbed integrates multiple units: an Operation Unit for test management, a User Unit equipped with an Ettus E312 USRP, and a Station Unit featuring an Ettus N310 USRP equipped with a three-element Uniform Linear Array for Angle of Arrival estimation. Alongside ultra wideband ranging, the testbed estimates the user's position relative to the base station. Signal processing algorithms are executed in a dedicated processing unit. Key challenges addressed include phase misalignment between RX channel pairs due to different Local Oscillators in the Ettus N310, necessitating real-time calibration for precise signal alignment. High sampling rates (up to 61.44 MSps) result in large IQ sample files, managed efficiently using a snapshot technique to optimize storage without compromising testbed positioning capabilities. The testbed synchronizes angular measurements with ranging estimates allowing consistent performance evaluation for real-life cases of dynamic users (e.g. pedestrian). Experimental results demonstrate the testbed's effectiveness in achieving accurate pedestrian user localization.

eess.SP↗

arXiv2024

Evaluation of RIS-Enabled B5G/6G Indoor Positioning and Mapping using Ray Tracing Models

A Reconfigurable Intelligent Surface (RIS) can significantly enhance network positioning and mapping, acting as an additional anchor point in the reference system and improving signal strength and measurement diversity through the generation of favorable scattering conditions and virtual line-of-sight paths. In this paper, we present a comprehensive framework aimed at user localization and scatterer position estimation in an indoor environment with multipath effects. Our approach leverages beam sweeping through codebook-based beamforming at an 1-bit RIS to scan the environment, applies signal component extraction mechanisms, and utilizes a super-resolution algorithm for angle-based positioning of both connected users and scatterers. To validate the system's effectiveness, accurate 3D ray tracing models are employed, ensuring the robustness and effectiveness of the proposed approach in practical scenarios.

cs.IT↗