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Aydin Sezgin

Publications and source records attributed to Aydin Sezgin.

At least 55 records · Page 3Linked to original sources

Covertness in the Near Field: Maximizing the Covert Region with FDA

Covert communication in wireless networks ensures that transmissions remain undetectable to adversaries, making it a potential enabler for privacy and security in sensitive applications. However, to meet the high performance and connectivity demands of sixth-generation (6G) networks, future wireless systems will require larger antenna arrays, higher operating frequencies, and advanced antenna architectures. This shift changes the propagation model from far-field planar-wave to near-field spherical-wave which necessitates a redesign of existing covert communication systems. Unlike far-field beamforming, which relies only on direction, near-field beamforming depends on both distance and direction, providing additional degrees of freedom for system design. In this paper, we aim to utilize those freedoms by proposing near-field Frequency Diverse Array (FDA)-based transmission strategies that manipulate the beampattern in both distance and angle, thereby establishing a non-covert region around the legitimate user. Our approach takes advantage of near-field properties and FDA technology to significantly reduce the area vulnerable to detection by adversaries while maintaining covert communication with the legitimate receiver. Numerical simulations show that our methods outperform conventional phased arrays by shrinking the non-covert region and allowing the covert region to expand as the number of antennas increases.

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Uncertainty Propagation and Minimization for Channel Estimation in UAV-mounted RIS Systems

Reconfigurable Intelligent Surfaces (RIS) are emerging as a key technology for sixth-generation (6G) wireless networks, leveraging adjustable reflecting elements to dynamically control electromagnetic wave propagation and optimize wireless connectivity. By positioning the RIS on an unmanned aerial vehicle (UAV), it can maintain line-of-sight and proximity to both the transmitter and receiver, critical factors that mitigate path loss and enhance signal strength. The lightweight, power-efficient nature of RIS makes UAV integration feasible, yet the setup faces significant disturbances from UAV motion, which can degrade RIS alignment and link performance. In this study, we address these challenges using both experimental measurements and analytical methods. Using an extended Kalman filter (EKF), we estimate the UAV's orientation in real time during experimental flights to capture real disturbance effects. The resulting orientation uncertainty is then propagated to the RIS's channel estimates by applying the Guide to the Expression of Uncertainty in Measurement (GUM) framework as well as complex-valued propagation techniques to accurately assess and minimize the impact of UAV orientation uncertainties on RIS performance. This method enables us to systematically trace and quantify how orientation uncertainties affect channel gain and phase stability in real-time. Through numerical simulations, we find that the uncertainty of the RIS channel link is influenced by the RIS's configuration. Furthermore, our results demonstrate that the uncertainty area is most accurately represented by an annular section, enabling a 58% reduction in the uncertainty area while maintaining a 95% coverage probability.

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Robust Communication Design in RIS-Assisted THz Channels

Terahertz (THz) communication offers the necessary bandwidth to meet the high data rate demands of next-generation wireless systems. However, it faces significant challenges, including severe path loss, dynamic blockages, and beam misalignment, which jeopardize communication reliability. Given that many 6G use cases require both high data rates and strong reliability, robust transmission schemes that achieve high throughput under these challenging conditions are essential for the effective use of high-frequency bands. In this context, we propose a novel mixed-criticality superposition coding scheme for reconfigurable intelligent surface (RIS)-assisted THz systems. This scheme leverages both the strong but intermittent direct line-of-sight link and the more reliable, yet weaker, RIS path to ensure robust delivery of high-criticality data while maintaining high overall throughput. We model a mixed-criticality queuing system and optimize transmit power to meet reliability and queue stability constraints. Simulation results show that our approach significantly reduces queuing delays for critical data while sustaining high overall throughput, outperforming conventional time-sharing methods. Additionally, we examine the impact of blockage, beam misalignment, and beamwidth adaptation on system performance. These results demonstrate that our scheme effectively balances reliability and throughput under challenging conditions, while also underscoring the need for robust beamforming techniques to mitigate the impact of misalignment in RIS-assisted channels.

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Realization of Reconfigurable Intelligent Surfaces with Space-Time Coded Metasurfaces

This paper presents experimental realization of a reconfigurable intelligent surface (RIS) using space-time coding metasurfaces to enable concurrent beam steering and data modulation. The proposed approach harnesses the capabilities of metasurfaces, allowing precise temporal control over individual unit cells of the RIS. We show that by employing proper binary codes manipulating the state of unit cells, the RIS can act as a digital data modulator with beam steering capability. We describe the experimental setup and computational tools, followed by validation through harmonic generation and investigation of beam steering and data modulation. Additionally, four digital modulation schemes are evaluated. By implementing customized binary codes, constellations under varying conditions are compared, showcasing the potential for real-world applications. This study offers new insights into the practical implementation of RIS for advanced wireless communication systems.

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Wireless Localization with Space-Time Coded Reconfigurable Intelligent Surfaces

In this paper, a novel approach for wireless localization is proposed and experimentally validated that leverages space-time coded reconfigurable intelligent surfaces (RIS). It is demonstrated that applying proper single-bit codes to each RIS element, enables accurate determination of the direction of arrival (AOA) at the receiver. Moreover, we introduce different scenarios that such technique can be used for localization. By incorporating RIS, a passive component, the method significantly reduces the complexity found in previous localization techniques. Additionally, the use of 1-bit codes minimizes hardware requirements, offering a reliable, low-cost solution for localization in advanced telecommunications networks.

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Optimizing Energy Efficiency with RSMA: Balancing Low and High QoS Requirements

Future wireless systems are expected to deliver significantly higher quality-of-service (QoS) albeit with fewer energy resources for diverse, already existing and also novel wireless applications. The optimal resource allocation for a system in this regard could be investigated by reducing the overall power available at the expense of reduced QoS for the inefficient users. In other words, we maximize the system energy efficiency by achieving power saving through a minimal back-off in terms of QoS. In this paper, we investigate the energy efficiency vs. delivered QoS trade-off for the rate-splitting multiple access (RSMA) assisted downlink system. We first determine the user grouping with a normalised channel similarity metric so as to allow a large number of users with non-zero achievable private message rates. Through the private message removal (PMR) of these users, we aim to investigate the QoS vs. energy efficiency trade-off. Numerical results indicate a peak of ~$10\%$ increase in the network energy efficiency for the proposed normalised channel similarity metric based user grouping with scheduled PMR.

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A Structural Analysis of the User Behavior Dynamics for Environmentally Sustainable ICT

The sector of information and communication technology (ICT) can contribute to the fulfillment of the Paris agreement and the sustainable development goals (SDGs) through the introduction of sustainability strategies. For environmental sustainability, such strategies should contain efficiency, sufficiency, and consistency measures. To propose such, a structural analysis of ICT is undertaken in this manuscript. Thereby, key mechanisms and dynamics behind the usage of ICT and the corresponding energy and resource use are analyzed by describing ICT as a complex system. The system contains data centers, communication networks, smartphone hardware, apps, and the behavior of the users as sub-systems, between which various Morinian interactions are present. Energy and non-energy resources can be seen as inputs of the system, while e-waste is an output. Based on the system description, we propose multiple measures for efficiency, sufficiency and consistency to reduce greenhouse gas emissions and other environmental impacts.

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On Neural-Network Representation of Wireless Self-Interference for Inband Full-Duplex Communications

Neural network modeling is a key technology of science and research and a platform for deployment of algorithms to systems. In wireless communications, system modeling plays a pivotal role for interference cancellation with specifically high requirements of accuracy regarding the elimination of self-interference in full-duplex relays. This paper hence investigates the potential of identification and representation of the self-interference channel by neural network architectures. The approach is promising for its ability to cope with nonlinear representations, but the variability of channel characteristics is a first obstacle in straightforward application of data-driven neural networks. We therefore propose architectures with a touch of "adaptivity" to accomplish a successful training. For reproducibility of results and further investigations with possibly stronger models and enhanced performance, we document and share our data.

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RIS-Aided Bistatic Radar for Rapid NLOS Sensing in the Teraharetz Band

In this paper, we investigate a non-lineof-sight (NLOS) sensing problem at terahertz frequencies. To be able to observe the targets shadowed by a blockage, we propose a method using reconfigurable intelligent surfaces (RIS). We employ a bistatic radar system and scan the obstructed area with RIS using hierarchical codebooks (HCB). Moreover, we propose an iterative maximum likelihood estimation (MLE) scheme to yield the optimum sensing accuracy, converging to Cramer-Rao lower bound (CRLB). We take band-specific effects such as diffraction and beam squint into account and show that these effects are relevant factors affecting localization performance in RIS-employed radar setups. The results show that under NLOS conditions, the system can still localize all the targets with very good accuracy using the RIS. The initial estimates obtained by the HCBs can provide centimeter-level accuracy, and when the optimal performance is needed, at the cost of a few extra transmissions, the proposed iterative MLE method improves the accuracy to sub-millimeter accuracy, yielding the position error bound.

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Robust Communication and Computation using Deep Learning via Joint Uncertainty Injection

The convergence of communication and computation, along with the integration of machine learning and artificial intelligence, stand as key empowering pillars for the sixth-generation of communication systems (6G). This paper considers a network of one base station serving a number of devices simultaneously using spatial multiplexing. The paper then presents an innovative deep learning-based approach to simultaneously manage the transmit and computing powers, alongside computation allocation, amidst uncertainties in both channel and computing states information. More specifically, the paper aims at proposing a robust solution that minimizes the worst-case delay across the served devices subject to computation and power constraints. The paper uses a deep neural network (DNN)-based solution that maps estimated channels and computation requirements to optimized resource allocations. During training, uncertainty samples are injected after the DNN output to jointly account for both communication and computation estimation errors. The DNN is then trained via backpropagation using the robust utility, thus implicitly learning the uncertainty distributions. Our results validate the enhanced robust delay performance of the joint uncertainty injection versus the classical DNN approach, especially in high channel and computational uncertainty regimes.

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Show Me the Way: Real-Time Tracking of Wireless Mobile Users with UWB-Enabled RIS

The integration of Reconfigurable Intelligent Surfaces (RIS) in 6G wireless networks offers unprecedented control over communication environments. However, identifying optimal configurations within practical constraints remains a significant challenge. This becomes especially pronounced, when the user is mobile and the configurations need to be deployed in real time. Leveraging Ultra-Wideband (UWB) as localization technique, we capture and analyze real-time movements of a user within the RIS-enabled indoor environment. Given this information about the system's geometry, a model-based optimization is utilized, which enables real-time beam steering of the RIS towards the user. However, practical limitations of UWB modules lead to fluctuating UWB estimates, causing the RIS beam to occasionally miss the tracked user. The methodologies proposed in this work aim to increase the compatibility between these two systems. To this end, we provide two key solutions: beam splitting for obtaining more robust RIS configurations and UWB estimation correction for reducing the variations in the UWB data. Through comprehensive theoretical and experimental evaluations in both stationary and mobile scenarios, the effectiveness of the proposed techniques is demonstrated. When combined, the proposed methods improve worst-case tracking performance by a significant 17.5dB compared to the conventional approach.

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Dynamic Rate Splitting Grouping for Antifragile Responses to Wireless Network Disruptions

The reliance on wireless network architectures for applications demanding high reliability and fault tolerance is growing. These architectures heavily depend on wireless channels, making them susceptible to impairments and blockages. Ensuring functionality, particularly for safety-critical applications, demands robust countermeasures at the physical layer. In response, this work proposes the utilization of a dynamic Rate Splitting (RS) grouping approach as a resilience mechanism during blockages. RS effectively manages interference within networks but faces challenges during outages and blockages, where system performance can deteriorate due to the lowest decoding rate dictating the common rate and increased interference from fewer available channel links. As a strategic countermeasure, RS is leveraged to mitigate the impact of blockages, maintaining system efficiency and performance amidst disruptions. In fact, the introduction of new RS groups enables the exploration of novel solutions to the resource allocation problem, potentially outperforming those adopted before the occurrence of a blockage. As it turns out, by employing the dynamic RS grouping, the network exhibits an antifragile recovery response, showcasing the network's ability to not only recover from disruptions but also surpass its initial performance.

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Enhancing the Secrecy Rate with Direction-range Focusing with FDA and RIS

One of the great potentials to improve the confidentiality in mmWave/THz at the physical layer of technical communication, measured by the secrecy rate, lies in the use of reconfigurable intelligent surfaces (RISs). However, an important open problem arises when the eavesdropper is aligned with the legitimate user or in proximity to the RIS or legitimate user. The limitation comes, on one hand, from the high directional gain caused by the dominant line-of-sight (LOS) path in high-frequency transmission, and, on the other hand, from the high energy leakage in the proximity of the RIS and the legitimate user. To address these issues, we employ the concept of frequency diverse arrays (FDA) at the base station (BS) associated with random inverted transmit beamforming and reflective element subset selection (RIBES). More specifically, we consider a passive eavesdropper with unknown location, and design the transmit beamforming and RIS configuration based on the channel information of the legitimate user only. In this context, the secrecy rate with the proposed transmission technique is evaluated in the case of deterministic eavesdropper channel, demonstrating that we can ensure a secure transmission regarding both direction and range. Furthermore, assuming no prior information about the eavesdropper, we describe the wiretap region and derive the worst-case secrecy rate in closed form. The latter is further optimized by determining the optimal subset sizes of the transmit antennas and reflective elements. Simulations verify the correctness of the closed-form expressions and demonstrate that we can effectively improve the secrecy rate, especially when the eavesdropper is close to the RIS or the legitimate user.

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Intermittency versus Path Loss in RIS-aided THz Communication: A Data Significance Approach

The transition to Terahertz (THz) frequencies, providing an ultra-wide bandwidth, is a key driver for future wireless communication networks. However, the specific properties of the THz channel, such as severe path loss and vulnerability to blockage, pose a significant challenge in balancing data rate and reliability. This work considers reconfigurable intelligent surface (RIS)-aided THz communication, where the effective exploitation of a strong, but intermittent line-of-sight (LOS) path versus a reliable, yet weaker RIS-path is studied. We introduce a mixed-criticality superposition coding scheme that addresses this tradeoff from a data significance perspective. The results show that the proposed scheme enables reliable transmission for a portion of high-criticality data without significantly impacting the overall achievable sum rate and queuing delay. Additionally, we gain insights into how the LOS blockage probability and the channel gain of the RIS-link influence the rate performance of our scheme.

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Maximizing Spectral and Energy Efficiency in Multi-user MIMO OFDM Systems with RIS and Hardware Impairment

An emerging technology to enhance the spectral efficiency (SE) and energy efficiency (EE) of wireless communication systems is reconfigurable intelligent surface (RIS), which is shown to be very powerful in single-carrier systems. However, in multi-user orthogonal frequency division multiplexing (OFDM) systems, RIS may not be as promising as in single-carrier systems since an independent optimization of RIS elements at each sub-carrier is impossible in multi-carrier systems. Thus, this paper investigates the performance of various RIS technologies like regular (reflective and passive), simultaneously transmit and reflect (STAR), and multi-sector beyond diagonal (BD) RIS in multi-user multiple-input multiple-output (MIMO) OFDM broadcast channels (BC). This requires to formulate and solve a joint MIMO precoding and RIS optimization problem. The obtained solution reveals that RIS can significantly improve the system performance even when the number of RIS elements is relatively low. Moreover, we develop resource allocation schemes for STAR-RIS and multi-sector BD-RIS in MIMO OFDM BCs, and show that these RIS technologies can outperform a regular RIS, especially when the regular RIS cannot assist the communications for all the users.

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EMF-Constrained Artificial Noise for Secrecy Rates with Stochastic Eavesdropper Channels

An information-theoretic confidential communication is achievable if the eavesdropper has a degraded channel compared to the legitimate receiver. In wireless channels, beamforming and artificial noise can enable such confidentiality. However, only distribution knowledge of the eavesdropper channels can be assumed. Moreover, the transmission of artificial noise can lead to an increased electromagnetic field (EMF) exposure, which depends on the considered location and can thus also be seen as a random variable. Hence, we optimize the $\varepsilon$-outage secrecy rate under a $δ$-outage exposure constraint in a setup, where the base station (BS) is communicating to a user equipment (UE), while a single-antenna eavesdropper with Rayleigh distributed channels is present. Therefore, we calculate the secrecy outage probability (SOP) in closed-form. Based on this, we convexify the optimization problem and optimize the $\varepsilon$-outage secrecy rate iteratively. Numerical results show that for a moderate exposure constraint, artificial noise from the BS has a relatively large impact due to beamforming, while for a strict exposure constraint artificial noise from the UE is more important.

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Extended Reality via Cooperative NOMA in Hybrid Cloud/Mobile-Edge Computing Networks

Extended reality (XR) applications often perform resource-intensive tasks, which are computed remotely, a process that prioritizes the latency criticality aspect. To this end, this paper shows that through leveraging the power of the central cloud (CC), the close proximity of edge computers (ECs), and the flexibility of uncrewed aerial vehicles (UAVs), a UAV-aided hybrid cloud/mobile-edge computing architecture promises to handle the intricate requirements of future XR applications. In this context, this paper distinguishes between two types of XR devices, namely, strong and weak devices. The paper then introduces a cooperative non-orthogonal multiple access (Co-NOMA) scheme, pairing strong and weak devices, so as to aid the XR devices quality-of-user experience by intelligently selecting either the direct or the relay links toward the weak XR devices. A sum logarithmic-rate maximization problem is, thus, formulated so as to jointly determine the computation and communication resources, and link-selection strategy as a means to strike a trade-off between the system throughput and fairness. Subject to realistic network constraints, e.g., power consumption and delay, the optimization problem is then solved iteratively via discrete relaxations, successive-convex approximation, and fractional programming, an approach which can be implemented in a distributed fashion across the network. Simulation results validate the proposed algorithms performance in terms of log-rate maximization, delay-sensitivity, scalability, and runtime performance. The practical distributed Co-NOMA implementation is particularly shown to offer appreciable benefits over traditional multiple access and NOMA methods, highlighting its applicability in decentralized XR systems.

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Validating Properties of RIS Channel Models with Prototypical Measurements

The integration of Reconfigurable Intelligent Surfaces (RIS) holds substantial promise for revolutionizing 6G wireless networks, offering unprecedented capabilities for real-time control over communication environments. However, determining optimal RIS configurations remains a pivotal challenge, necessitating the development of accurate analytical models. While theoretically derived models provide valuable insights, their potentially idealistic assumptions do not always translate well to practical measurements. This becomes especially problematic in mobile environments, where signals arrive from various directions. This study deploys an RIS prototype on a turntable, capturing the RIS channels' dependency on the angle of incoming signals. The difference between theory and practice is bridged by refining a model with angle-dependent reflection coefficients. The improved model exhibits a significantly closer alignment with real-world measurements. Analysis of the reflect coefficients reveals that non-perpendicular receiver angles can induce an additional attenuation of up to -14.5dB. Additionally, we note significant phase shift deviations, varying for each reflect element.

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