arXiv subjects

Aydin Sezgin

Publications and source records attributed to Aydin Sezgin.

At least 19 recordsLinked to original sources

arXiv2026

From Prompt to Prototype: Towards a Frontier LLM Driven RF Engineering Workflow

Agentic coding environments give a frontier large language model (LLM) direct access to a workstation's terminal, file system, and software. This work demonstrates they extend to professional RF hardware design: an active GNSS L1-band antenna - a circularly polarized patch, surface acoustic wave (SAW) prefilter, and two-stage low-noise amplifier (LNA) on one printed circuit board (PCB) - was designed, optimized, and made manufacturing-ready. The LLM agent autonomously operated CST Studio Suite, Keysight ADS, and KiCad via scripting interfaces. Engineer input was limited to the specification, trade-off decisions, and design reviews. Workflow, results, and the RF engineer's evolving role are discussed.

eess.SY↗

arXiv2026

Self-Supervised Noise2Noise-Enhanced Denoising for Continuous-Scan Air-Plasma THz Spectroscopy

Terahertz time-domain spectroscopy (THz-TDS) based on air-plasma generation and balanced air-biased coherent detection offers gap-free broadband coverage, but individual continuous-scan traces are strongly affected by pulse-to-pulse fluctuations and electronic noise. Reaching a useful signal-to-noise ratio therefore requires averaging multiple traces, which directly increases measurement time. We propose a learned denoising approach that recovers high-quality THz waveforms from as few as one complete continuous delay sweep, referred to here as a single-scan trace. A compact one-dimensional residual U-Net is trained using two complementary strategies: a reference-supervised baseline that maps individual noisy traces to long-average reference waveforms, and a Noise2Noise approach that learns from pairs of independently acquired noisy traces without requiring a clean training target. Averaging the predictions of both models reduces systematic bias and yields a trace-reduction factor of approximately $5.4\times$ at $K=1$, meaning that one denoised trace achieves the reconstruction accuracy of averaging approximately five raw traces. The Noise2Noise model alone achieves $4.9\times$, outperforming both the reference-supervised baseline ($4.6\times$) and classical Wiener filtering ($3.2\times$). These results show that self-supervised learning from repeated noisy measurements can support faster continuous-scan THz-TDS without hardware modification.

eess.SP↗

arXiv2026

Design and Practical Validation of a Novel Modulation Scheme for RIS Detection and Identification

The reconfigurable intelligent surfaces detection and identification (RISs-ID) is a critical process that enables a base station (BS) to adaptively assign the appropriate RIS to a given \ac{UE}. This work proposes a novel modulation scheme to enhance the reliability of RIS-ID by reducing the miss-detection ($P_\text{m}$) and false-alarm ($P_\text{f}$) probabilities. Specifically, we leverage the RIS's passive beamforming gain to enable over-the-air modulation of the RIS ID, combined with passive beam sweeping to extend detection coverage in angular space. Prototype experiments demonstrate the effectiveness of the proposed scheme in reducing $P_\text{m}$ and $P_\text{f}$ to $10^{-3}$ across different operating points.

eess.SP↗

arXiv2026

LIVE-RIS: Real-Time In-Flight Actuation of UAV-Mounted RIS

Reconfigurable intelligent surfaces (RIS) are emerging as a key technology for sixth-generation (6G) wireless networks due to their ability to dynamically control the propagation environment. To ensure favorable Line-of-Sight (LoS) conditions in real-world applications, the RIS is mounted on an unmanned aerial vehicle (UAV). While the potential of UAV-mounted RIS has been extensively studied in theoretical works, experimental validation with real-world data remains limited. Such validation is particularly important, as UAV motion and disturbances may degrade the performance of the RIS-enabled link. In this paper, we present the first fully functional, real-time capable UAV-mounted RIS prototype and validate its performance through experimental measurements under realistic disturbances and hardware constraints. We show that the RIS pose can be predicted based on the UAV's extended Kalman filter (EKF) and onboard sensors. By utilizing this estimation, we demonstrate that the RIS can be reconfigured in real time, effectively mitigating disturbance effects and preserving the performance gains of the RIS-enabled link. Furthermore, we systematically evaluate different deployment locations to provide insights into RIS performance in real-world scenarios.

eess.SP↗

arXiv2026

From Metric to Mechanism: Designing Wireless Resilience through Finite Blocklength Dynamics

Next-generation wireless networks must maintain reliable operation under abrupt and severe disruptions, particularly in ultra-reliable low-latency communication (URLLC) scenarios where strict time constraints dominate system design. This work addresses network resilience from a time-centric perspective by explicitly integrating finite blocklength (FBL) communication, thereby exposing transmission duration as a controllable resource for system recovery. To this end, we propose a unified cross-layer framework that jointly couples queue dynamics, rate adaptation, and blocklength optimization, enabling the system to actively absorb, adapt to, and recover from diverse resilience events. To systematically evaluate these mechanisms, we introduce an interpretable resilience metric that decomposes disruption impact into absorption loss, adaptation efficiency, and recovery behavior, enabling a direct and intuitive assessment of system resilience. Building on this framework, we develop a three-stage alternating optimization approach that jointly optimizes PHY-layer parameters, including beamforming, reconfigurable intelligent surface (RIS) phase shifts, and blocklength, revealing the importance of time-aware resource allocation in the FBL regime. Numerical results demonstrate strong resilience performance under repeated channel disruptions and AI-driven traffic surges, highlighting the effectiveness of cross-layer resource adaptation. Finally, the proposed resilience metric enables an intuitive and consistent comparison of resilience performance across different approaches and disruption types, while revealing their respective strengths and limitations.

eess.SP↗

arXiv2026

Design and Deployment Guidelines for UAV-Mounted RIS Under Position Uncertainty

UAV-mounted reconfigurable intelligent surfaces (RIS) are a promising enabler for 6G networks, offering dynamic control of wireless propagation for coverage enhancement, integrated sensing and communication (ISAC), and localization. By exploiting UAV mobility, RIS can maintain favorable line-of-sight links, improving channel quality in dynamic environments. However, UAV positioning uncertainties introduce channel distortions that degrade RIS phase alignment and coherent combining. This work develops a GUM-based uncertainty propagation framework for UAV-mounted RIS channels, mapping UAV position uncertainty through the geometric Tx-RIS-Rx model into the complex cascaded channel. We derive a closed-form stochastic propagation model capturing nonlinear phase uncertainty effects and quantify their impact on channel coherence. The results show that phase uncertainty induces exponential coherence loss, dominating performance degradation. To characterize this transition, we introduce a performance-driven coherence threshold (PCT) that defines the boundary where incoherent combining results in a predetermined performance loss. Results based on analytical scaling laws and Monte Carlo simulations confirm the tightness of the PCT in accurately capturing the coherence transition. This validated threshold is then leveraged to derive optimal UAV-mounted RIS placement, revealing that realistic positioning conditions significantly deviate from the conventional RIS intuition, which typically favors placement close to either the transmitter or receiver.

eess.SP↗

arXiv2026

Minimizing Quantized Semantic Age of Information (QSAoI) in Foundation Model-Based Semantic Communications

The emerging techniques of semantic communications and edge computing in 6G networks necessitate a paradigm shift toward co-designed semantic-aware and adaptive resource allocation for short-packet transmissions. However, there is a fundamental gap between the semantic layer and the physical layer under low-latency finite blocklength (FBL) effects. To bridge this gap, we introduce the Quantized Semantic Age of Information (QSAoI), a novel metric that rigorously captures the trade-offs among freshness and semantic efficiency of high-level features in real-time communication in the FBL regime. Guided by this metric, we propose a novel foundation model-based efficient co-designed framework to minimize the expected QSAoI over wireless fading channels in latency-constrained semantic communication. Specifically, we formulate a non-linear joint optimization problem to dynamically optimize the block-wise mixed-precision quantization (MPQ) strategy and the physical blocklength. To efficiently resolve this complex problem, we develop a high-efficiency low-complexity algorithm based on fixpoint inspection and bisection search. Extensive simulations validate that our proposed algorithm dynamically adapts the semantic quantization precision to varying channel conditions, effectively minimizing the expected QSAoI compared to baselines.

eess.SP↗

arXiv2026

RIS-Assisted Rank Enhancement With Commodity WiFi Transceivers: Real-World Experiments

Reconfigurable intelligent surfaces (RISs) are a promising enabling technology for the sixth-generation ($6$G) of wireless communications. RISs, thanks to their intelligent design, can reshape the wireless channel to provide favorable propagation conditions for information transfer. In this work, we experimentally investigate the potential of RISs to enhance the effective rank of multiple-input multiple-output (MIMO) channels, thereby improving spatial multiplexing capabilities. In our experiment, commodity WiFi transceivers are used, representing a practical MIMO system. In this context, we propose a passive beam-focusing technique to manipulate the propagation channel between each transmit-receive antenna pair and achieve a favorable propagation condition for rank improvement. The proposed algorithm is tested in two different channel scenarios: low and medium ranks. Experimental results show that, when the channel is rank-deficient, the RIS can significantly increase the rank by $112\%$ from its default value without the RIS, providing a rank increment of $1.5$. When the rank has a medium value, a maximum of $61\%$ enhancement can be achieved, corresponding to a rank increment of $1$. These results provide the first experimental evidence of RIS-driven rank manipulation with off-the-shelf WiFi hardware, offering practical insights into RIS deployment for spatial multiplexing gains.

eess.SP↗

arXiv2026

Quantifying System Level KPI Deviations of Sionna RT: Material and Near-Field Error Analysis Using a 5G OAI Testbed

Ray tracing (RT) has recently gained renewed interest in wireless communications, driven by its integration into digital twin (DT) frameworks for site specific channel modeling. Several previous studies have validated RT at the channel level, yet how these errors propagate into real 5G system level key performance indicators (KPIs) on actual hardware remains unquantified. This paper addresses this gap by comparing Sionna RT simulated channels against vector network analyzer (VNA) measured channels using an OpenAirInterface (OAI) 5G NR testbed. Channel measurements are conducted at 20 receiver positions in an indoor laboratory, with both channel types injected into a hardware in the loop channel emulator interfacing an OAIBOX MAX base station and a Quectel UE. RSRP, PUCCH SNR, and SINR are evaluated under both conditions. The results identify antenna near-field transition effects as a critical position-dependent error source, alongside material property mismatch, providing a quantitative benchmark for digital twin-based 5G and beyond network planning.

eess.SP↗

arXiv2026

Accelerated Recovery with RIS: Designing Wireless Resilience in Mission-Critical Environments

As 6G and beyond redefine connectivity, wireless networks become the foundation of critical operations, making resilience more essential than ever. With this shift, wireless systems cannot only take on vital services previously handled by wired infrastructures but also enable novel innovative applications that would not be possible with wired systems. As a result, there is a pressing demand for strategies that can adapt to dynamic channel conditions, interference, and unforeseen disruptions, ensuring seamless and reliable performance in an increasingly complex environment. Despite considerable research, existing resilience assessments lack comprehensive key performance indicators (KPIs), especially those quantifying its adaptability, which are vital for identifying a system's capacity to rapidly adapt and reallocate resources. In this work, we bridge this gap by proposing a novel framework that explicitly quantifies the adaption performance by augmenting the gradient of the system's rate function. To further enhance the network resilience, we integrate Reconfigurable Intelligent Surfaces (RISs) into our framework due to their capability to dynamically reshape the propagation environment while providing alternative channel paths. Numerical results show that gradient augmentation enhances resilience by improving adaptability under adverse conditions while proactively preparing for future disruptions.

eess.SP↗

arXiv2026

Service Placement in Small Cell Networks Using Distributed Best Arm Identification in Linear Bandits

As users in small cell networks increasingly rely on computation-intensive services, cloud-based access often results in high latency. Multi-access edge computing (MEC) mitigates this by bringing computational resources closer to end users, with small base stations (SBSs) serving as edge servers to enable low-latency service delivery. However, limited edge capacity makes it challenging to decide which services to deploy locally versus in the cloud, especially under unknown service demand and dynamic network conditions. To tackle this problem, we model service demand as a linear function of service attributes and formulate the service placement task as a linear bandit problem, where SBSs act as agents and services as arms. The goal is to identify the service that, when placed at the edge, offers the greatest reduction in total user delay compared to cloud deployment. We propose a distributed and adaptive multi-agent best-arm identification (BAI) algorithm under a fixed-confidence setting, where SBSs collaborate to accelerate learning. Simulations show that our algorithm identifies the optimal service with the desired confidence and achieves near-optimal speedup, as the number of learning rounds decreases proportionally with the number of SBSs. We also provide theoretical analysis of the algorithm's sample complexity and communication overhead.

cs.NI↗

arXiv2026

Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models

Future sixth-generation (6G) mobile networks are envisioned to be equipped with a diverse set of powerful, yet highly specialized, optimization experts. Such a promising vision is concurrently expected to give rise to the need for scalable mechanisms that can select, combine, and orchestrate such experts based on high-level intent and uncertainty descriptions. In this paper, we propose an agentic artificial intelligence (AI)-based network optimization framework that integrates mixture of experts (MoE) architectures with large language models (LLMs). Under the proposed framework, the employed LLM acts as a semantic gate to reason over operator objectives and dynamically compose suitable optimization agents. The proposed framework is formulated in a model-agnostic manner and bridges human-readable network intents with low-level resource allocation decisions, enabling flexible optimization across heterogeneous objectives and operating conditions. As a representative instantiation, we apply the framework to a joint communication and computing network and design a library of specialized optimization experts covering throughput, fairness, and delay-driven objectives under both regular and robust conditions. Numerical simulations demonstrate that the proposed agentic MoE framework consistently achieves near-optimal performance compared to exhaustive expert combinations while outperforming individual experts across diverse objectives, including delay minimization and throughput maximization.

cs.LG↗

arXiv2026

Exploiting Near-Field Dynamics with Movable Antennas to Enhance Discrete Transmissive RIS

The design of low-complexity transceivers is crucial for the deployment of next-generation wireless systems. In this work, we combine two emerging concepts, movable antennas (MA) and transmissive reconfigurable intelligent surfaces (TRIS), which have recently attracted significant attention for enhancing wireless communication performance. In particular, we propose a compact base station (BS) architecture that integrates a single MA with a TRIS operating in their near-field region. We address the joint optimization of the MA location and the quantized TRIS phase configuration. Due to the non-convex coupling between spatial positioning and discrete phase constraints, an alternating optimization (AO) framework is developed, where the MA position is updated via gradient ascent (GA) and the TRIS phases are optimized through quantized phase alignment. Simulation results demonstrate that the proposed architecture significantly outperforms conventional BS designs equipped with fixed fully-active antenna arrays under the same channel model and transmit power constraint. Moreover, MA repositioning effectively mitigates the performance degradation caused by discrete TRIS phase quantization in near-field propagation environments. This reveals a favorable trade-off between hardware complexity and spatial signal processing, where the spatial adaptability of the MA can compensate for low-resolution TRIS phase control.

eess.SP↗

arXiv2026

A Scalable Reconfigurable Intelligent Surface with 3 Bit Phase Resolution and High Bandwidth for 3.6 GHz 5G/6G Applications

Reconfigurable Intelligent Surfaces enable active control of wireless propagation channels, which is crucial for future 5G and 6G networks. This work presents a scalable RIS design operating at 3.6 GHz with both 1 bit and 3 bit phase resolution, supporting wideband applications. The unit cells employ low-cost printed circuit board technology with an innovative spring-contact feeding structure, enabling efficient assembly and reduced manufacturing complexity for large-area arrays. The design achieves broadband phase control, low power consumption, and high scalability, with experimental results demonstrating phase tunability across the n78 frequency band and competitive reflection performance compared to existing solutions. This RIS architecture provides a practical platform for experimental studies of smart radio environments, beam steering, and sensing applications in next-generation wireless networks.

eess.SY↗

arXiv2026

Measurement-Based Validation of Geometry-Driven RIS Beam Steering in Industrial Environments

Reconfigurable intelligent surfaces (RISs) offer programmable control of radio propagation for future wireless systems. For configuration, geometry-driven analytical approaches are appealing for their simplicity and real-time operation, but their performance in challenging environments such as industrial halls with dense multipath and metallic scattering is not well established. To this end, we present a measurement-based evaluation of geometry-driven RIS beam steering in a large industrial hall using a 5 GHz RIS prototype. A novel RIS configuration is proposed in which four patch antennas are mounted in close proximity in front of the RIS to steer the incident field and enable controlled reflection. For this setup, analytically computed, quantized configurations are implemented. Two-dimensional received power maps from two measurement areas reveal consistent, spatially selective focusing. Configurations optimized near the receiver produce clear power maxima, while steering to offset locations triggers a rapid 20-30 dB reduction. With increasing RIS-receiver distance, elevation selectivity broadens due to finite-aperture and geometric constraints, while azimuth steering remains robust. These results confirm the practical viability of geometry-driven RIS beam steering in industrial environments and support its use for spatial field control and localization under non-ideal propagation.

eess.SP↗

arXiv2026

Deep Unfolded Fractional Optimization for Maximizing Robust Throughput in 6G Networks

The sixth-generation (6G) of wireless communication networks aims to leverage artificial intelligence tools for efficient and robust network optimization. This is especially the case since traditional optimization methods often face high computational complexity, motivating the use of deep learning (DL)-based optimization frameworks. In this context, this paper considers a multi-antenna base station (BS) serving multiple users simultaneously through transmit beamforming in downlink mode. To account for robustness, this work proposes an uncertainty-injected deep unfolded fractional programming (UI-DUFP) framework for weighted sum rate (WSR) maximization under imperfect channel conditions. The proposed method unfolds fractional programming (FP) iterations into trainable neural network layers refined by projected gradient descent (PGD) steps, while robustness is introduced by injecting sampled channel uncertainties during training and optimizing a quantile-based objective. Simulation results show that the proposed UI-DUFP achieves higher WSR and improved robustness compared to classical weighted minimum mean square error, FP, and DL baselines, while maintaining low inference time and good scalability. These findings highlight the potential of deep unfolding combined with uncertainty-aware training as a powerful approach for robust optimization in 6G networks.

cs.IT↗

arXiv2025

Short Blocks, Fast Sensing: Finite Blocklength Tradeoffs in RIS-Assisted ISAC

Integrated sensing and communication (ISAC) is a cornerstone for future sixth-generation (6G) networks, enabling simultaneous connectivity and environmental awareness. However, practical realization faces significant challenges, including residual self-interference (SI) in full-duplex systems and performance degradation of short-packet transmissions under finite blocklength (FBL) constraints. This work studies a reconfigurable intelligent surface (RIS)-assisted full-duplex ISAC system serving multiple downlink users while tracking a moving target, explicitly accounting for SI and FBL effects in both communication and sensing. We formulate an optimization framework to minimize service adaptation gaps while ensuring sensing reliability, solved via alternating optimization and successive convex approximation. Numerical results show that short blocklengths enable fast adaptation but raise radar outage from fewer pulses and motion sensitivity. Longer blocklengths improve signal-to-interference-plus-noise ratio (SINR) and reduce outages but allow motion to degrade sensing. A "sweet spot" arises where blocklength and beamformer allocation optimize throughput and sensing, seen as a local minimum in radar SINR variance. RIS-assisted optimization identifies this balance, achieving reliable communication and radar sensing jointly.

eess.SP↗

arXiv2025

A Lightweight Framework for Integrated Sensing and Communications with RIS

Reconfigurable Intelligent Surfaces (RIS) have been recognized as a promising technology to enhance both communication and sensing performance in integrated sensing and communication (ISAC) systems for future 6G networks. However, existing RIS optimization methods for improving ISAC performance are mainly based on semidefinite relaxation (SDR) or iterative algorithms. The former suffers from high computational complexity and limited scalability, especially when the number of RIS elements becomes large, while the latter yields suboptimal solutions whose performance depends on initialization. In this work, we introduce a lightweight RIS phase design framework that provides a closed-form solution and explicitly accounts for the trade-off between communication and sensing, as well as proportional beam gain distribution toward multiple sensing targets. The key idea is to partition the RIS configuration into two parts: the first part is designed to maximize the communication performance, while the second introduces small perturbations to generate multiple beams for multi-target sensing. Simulation results validate the effectiveness of the proposed approach and demonstrate that it achieves performance comparable to SDR but with significantly lower computational complexity.

eess.SP↗