arXiv subjects

Marios Kountouris

Publications and source records attributed to Marios Kountouris.

At least 55 records · Page 3Linked to original sources

arXiv2025

DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks

Recent developments and emerging use cases, such as smart Internet of Things (IoT) and Edge AI, have sparked considerable interest in the training of neural networks over fully decentralized (serverless) networks. One of the major challenges of decentralized learning is to ensure stable convergence without resorting to strong assumptions applied for each agent regarding data distributions or updating policies. To address these issues, we propose DRACO, a novel method for decentralized asynchronous Stochastic Gradient Descent (SGD) over row-stochastic gossip wireless networks by leveraging continuous communication. Our approach enables edge devices within decentralized networks to perform local training and model exchanging along a continuous timeline, thereby eliminating the necessity for synchronized timing. The algorithm also features a specific technique of decoupling communication and computation schedules, which empowers complete autonomy for all users and manageable instructions for stragglers. Through a comprehensive convergence analysis, we highlight the advantages of asynchronous and autonomous participation in decentralized optimization. Our numerical experiments corroborate the efficacy of the proposed technique.

cs.LG↗

arXiv2025

CSI-Free Low-Complexity Remote State Estimation over Wireless MIMO Fading Channels using Semantic Analog Aggregation

In this work, we investigate low-complexity remote system state estimation over wireless multiple-input-multiple-output (MIMO) channels without requiring prior knowledge of channel state information (CSI). We start by reviewing the conventional Kalman filtering-based state estimation algorithm, which typically relies on perfect CSI and incurs considerable computational complexity. To overcome the need for CSI, we introduce a novel semantic aggregation method, in which sensors transmit semantic measurement discrepancies to the remote state estimator through analog aggregation. To further reduce computational complexity, we introduce a constant-gain-based filtering algorithm that can be optimized offline using the constrained stochastic successive convex approximation (CSSCA) method. We derive a closed-form sufficient condition for the estimation stability of our proposed scheme via Lyapunov drift analysis. Numerical results showcase significant performance gains using the proposed scheme compared to several widely used methods.

eess.SY↗

arXiv2025

Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication

This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain a push-and-pull model, which allows the transmission controller at the SA to decide to push an update to the AA and the query controller at the AA to pull updates by raising queries at specific time instances. To gauge effectiveness, we utilize a grade of effectiveness (GoE) metric incorporating updates' freshness, usefulness, and timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of updates' effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic effect-agnostic policies at either or both agents.

cs.IT↗

arXiv2025

On the Rate-Distortion-Perception Function for Gaussian Processes

In this paper, we investigate the rate-distortion-perception function (RDPF) of a source modeled by a Gaussian Process (GP) on a measure space $Ω$ under mean squared error (MSE) distortion and squared Wasserstein-2 perception metrics. First, we show that the optimal reconstruction process is itself a GP, characterized by a covariance operator sharing the same set of eigenvectors of the source covariance operator. Similarly to the classical rate-distortion function, this allows us to formulate the RDPF problem in terms of the Karhunen-Loève transform coefficients of the involved GPs. Leveraging the similarities with the finite-dimensional Gaussian RDPF, we formulate an analytical tight upper bound for the RDPF for GPs, which recovers the optimal solution in the "perfect realism" regime. Lastly, in the case where the source is a stationary GP and $Ω$ is the interval $[0, T]$ equipped with the Lebesgue measure, we derive an upper bound on the rate and the distortion for a fixed perceptual level and $T \to \infty$ as a function of the spectral density of the source process.

cs.IT↗

arXiv2024

So Timely, Yet So Stale: The Impact of Clock Drift in Real-Time Systems

In this paper, we address the problem of timely delivery of status update packets in a real-time communication system, where a transmitter sends status updates generated by a source to a receiver over an unreliable channel. The timestamps of transmitted and received packets are measured using separate clocks located at the transmitter and receiver, respectively. To account for possible clock drift between these two clocks, we consider both deterministic and probabilistic drift scenarios. We analyze the system's performance regarding the Age of Information (AoI) and derive closed-form expressions for the distribution and the average AoI under both clock drift models. Additionally, we explore the impact of key system parameters on the average AoI through analytical and numerical results.

cs.IT↗

arXiv2024

From OTFS to AFDM: A Comparative Study of Next-Generation Waveforms for ISAC in Doubly-Dispersive Channels

Next-generation wireless systems will offer integrated sensing and communications (ISAC) functionalities not only in order to enable new applications, but also as a means to mitigate challenges such as doubly-dispersive channels, which arise in high mobility scenarios and/or at millimeter-wave (mmWave) and Terahertz (THz) bands. An emerging approach to accomplish these goals is the design of new waveforms, which draw from the inherent relationship between the doubly-dispersive nature of time-variant (TV) channels and the environmental features of scatterers manifested in the form of multi-path delays and Doppler shifts. Examples of such waveforms are the delay-Doppler domain orthogonal time frequency space (OTFS) and the recently proposed chirp domain affine frequency division multiplexing (AFDM), both of which seek to simultaneously combat the detrimental effects of double selectivity and exploit them for the estimation (or sensing) of environmental information. This article aims to provide a consolidated and comprehensive overview of the signal processing techniques required to support reliable ISAC over doubly-dispersive channels in beyond fifth generation (B5G)/sixth generation (6G) systems, with an emphasis on OTFS and AFDM waveforms, as those, together with the traditional orthogonal frequency division multiplexing (OFDM) waveform, suffice to elaborate on the most relevant properties of the trend. The analysis shows that OTFS and AFDM indeed enable significantly improved robustness against inter-carrier interference (ICI) arising from Doppler shifts compared to OFDM. In addition, the inherent delay-Doppler domain orthogonality of the OTFS and AFDM effective channels is found to provide significant advantages for the design and the performance of integrated sensing functionalities.

eess.SP↗

arXiv2024

How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning

Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed learning framework. In this work, we focus on cross-silo FL, where clients become the model owners after training and are only concerned about the model's generalization performance on their local data. Due to the data heterogeneity issue, asking all the clients to join a single FL training process may result in model performance degradation. To investigate the effectiveness of collaboration, we first derive a generalization bound for each client when collaborating with others or when training independently. We show that the generalization performance of a client can be improved only by collaborating with other clients that have more training data and similar data distribution. Our analysis allows us to formulate a client utility maximization problem by partitioning clients into multiple collaborating groups. A hierarchical clustering-based collaborative training (HCCT) scheme is then proposed, which does not need to fix in advance the number of groups. We further analyze the convergence of HCCT for general non-convex loss functions which unveils the effect of data similarity among clients. Extensive simulations show that HCCT achieves better generalization performance than baseline schemes, whereas it degenerates to independent training and conventional FL in specific scenarios.

cs.LG↗

arXiv2024

Optimizing Version Innovation Age for Monitoring Markovian Source in Energy-Harvesting Systems

We study the real-time remote tracking of a two-state Markov process by an energy harvesting source. The source decides whether to transmit over an unreliable channel based on the state. We formulate this scenario as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically verify the analytical structure of the optimal policy and compare the performance of our proposed policy against two baseline policies across various system parameters, establishing the superior performance of our approach.

cs.IT↗

arXiv2024

Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space

This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security.

cs.CR↗

arXiv2024

AFDM vs OTFS: A Comparative Study of Promising Waveforms for ISAC in Doubly-Dispersive Channels

**PLEASE FIND THE FULL EXTENDED ARTICLE "From OTFS to AFDM: A Comparative Study of Next-Generation Waveforms for ISAC in Doubly-Dispersive Channels" (Accepted for publication at the IEEE Signal Processing Magazine - Special Issue on Signal Processing for the Integrated Sensing and Communications Revolution)** This white paper aims to briefly describe a proposed article that will provide a thorough comparative study of waveforms designed to exploit the features of doubly-dispersive channels arising in heterogeneous high-mobility scenarios as expected in the beyond fifth generation (B5G) and sixth generation (6G), in relation to their suitability to integrated sensing and communications (ISAC) systems. In particular, the full article will compare the well-established delay-Doppler domain-based orthognal time frequency space (OTFS) and the recently proposed chirp domain-based affine frequency division multiplexing (AFDM) waveforms. Both these waveforms are designed based on a full delay- Doppler representation of the time variant (TV) multipath channel, yielding not only robustness and orthogonality of information symbols in high-mobility scenarios, but also a beneficial implication for environment target detection through the inherent capability of estimating the path delay and Doppler shifts, which are standard radar parameters. These modulation schemes are distinct candidates for ISAC in B5G/6G systems, such that a thorough study of their advantages, shortcomings, implications to signal processing, and performance of communication and sensing functions are well in order. In light of the above, a sample of the intended contribution (Special Issue paper) is provided below.

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arXiv2024

Age of Information Versions: a Semantic View of Markov Source Monitoring

We consider the problem of real-time remote monitoring of a two-state Markov process, where a sensor observes the state of the source and makes a decision on whether to transmit the status updates over an unreliable channel or not. We introduce a modified randomized stationary sampling and transmission policy where the decision to perform sampling occurs probabilistically depending on the current state of the source and whether the system was in a sync state during the previous time slot or not. We then propose two new performance metrics, coined the Version Innovation Age (VIA) and the Age of Incorrect Version (AoIV) and analyze their performance under the modified randomized stationary and other state-of-the-art sampling and transmission policies. Specifically, we derive closed-form expressions for the distribution and the average of VIA, AoIV, and Age of Incorrect Information (AoII) under these policies. Furthermore, we formulate and solve three constrained optimization problems. The first optimization problem aims to minimize the average VIA subject to constraints on the time-averaged sampling cost and time-averaged reconstruction error. In the second and third problems, the objective is to minimize the average AoIV and AoII, respectively, while considering a constraint on the time-averaged sampling cost. Finally, we compare the performance of various sampling and transmission policies and identify the conditions under which each policy outperforms the others in optimizing the proposed metrics.

cs.IT↗

arXiv2024

Goal-Oriented Multiple Access Connectivity for Networked Intelligent Systems

We design a self-decision goal-oriented multiple access scheme, where sensing agents observe a common event and individually decide to communicate the event's attributes as updates to the monitoring agents, to satisfy a certain goal. Decisions are based on the usefulness of updates, generated under uniform, change- and semantics-aware acquisition, as well as statistics and updates of other agents. We obtain optimal activation probabilities and threshold criteria for decision-making under all schemes, maximizing a grade of effectiveness metric. Alongside studying the effect of different parameters on effectiveness, our simulation results show that the self-decision scheme may attain at least 92% of optimal performance.

cs.IT↗

arXiv2024

A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data

This study presents a new approach for estimating confidence in machine learning model predictions, specifically in regression tasks utilizing Earth Observation (EO) data, with a particular focus on mosquito abundance (MA) estimation. We take advantage of a Variational AutoEncoder architecture, to derive a confidence metric by the latent space representations of EO datasets. This methodology is pivotal in establishing a correlation between the Euclidean distance in latent representations and the Absolute Error (AE) in individual MA predictions. Our research focuses on EO datasets from the Veneto region in Italy and the Upper Rhine Valley in Germany, targeting areas significantly affected by mosquito populations. A key finding is a notable correlation of 0.46 between the AE of MA predictions and the proposed confidence metric. This correlation signifies a robust, new metric for quantifying the reliability and enhancing the trustworthiness of the AI model's predictions in the context of both EO data analysis and mosquito abundance studies.

cs.LG↗

arXiv2024

An Information-Theoretic Analog of the Twin Paradox

We revisit the familiar scenario involving two parties in relative motion, in which Alice stays at rest while Bob goes on a journey at speed $βc$ along an arbitrary trajectory and reunites with Alice after a certain period of time. It is a well-known consequence of special relativity that the time that passes until they meet again is different for the two parties and is shorter in Bob's frame by a factor of $\sqrt{1-β^2}$. We investigate how this asymmetry manifests from an information-theoretic viewpoint. Assuming that Alice and Bob transmit signals of equal average power to each other during the whole journey, and that additive white Gaussian noise is present at both sides, we show that the maximum number of bits per second that Alice can transmit reliably to Bob is always higher than the one Bob can transmit to Alice. Equivalently, the energy per bit invested by Alice is lower than that invested by Bob, meaning that the traveler is less efficient from the communication perspective, as conjectured by Jarett and Cover.

cs.IT↗

arXiv2024

Integrated Sensing and Communications with Affine Frequency Division Multiplexing

Integrated sensing and communications (ISAC) is regarded as a key technology in next-generation (6G) mobile communication systems. Affine frequency division multiplexing (AFDM) is a recently proposed waveform that achieves optimal diversity gain in high mobility scenarios and has appealing properties in high-frequency communication. In this letter, we present an AFDM-based ISAC system. We first show that in order to identify all delay and Doppler components associated with the propagation medium, either the full AFDM signal or only its pilot part consisting of one discrete affine Fourier transform (DAFT) domain symbol and its guard interval can be used. Our results show that using one pilot symbol achieves almost the same sensing performance as using the entire AFDM frame. Furthermore, due to the chirp nature of AFDM, sensing with one pilot provides a unique feature allowing for simple self-interference cancellation, thus avoiding the need for expensive full duplex methods.

cs.IT↗

arXiv2024

Semantic Filtering and Source Coding in Distributed Wireless Monitoring Systems

The problem of goal-oriented semantic filtering and timely source coding in multiuser communication systems is considered here. We study a distributed monitoring system in which multiple information sources, each observing a physical process, provide status update packets to multiple monitors having heterogeneous goals. Two semantic filtering schemes are first proposed as a means to admit or drop arrival packets based on their goal-dependent importance, which is a function of the intrinsic and extrinsic attributes of information and the probability of occurrence of each realization. Admitted packets at each sensor are then encoded and transmitted over block-fading wireless channels so that served monitors can timely fulfill their goals. A truncated error control scheme is derived, which allows transmitters to drop or retransmit undelivered packets based on their significance. Then, we formulate the timely source encoding optimization problem and analytically derive the optimal codeword lengths assigned to the admitted packets which maximize a weighted sum of semantic utility functions for all pairs of communicating sensors and monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using the proposed semantic filtering, source coding, and error control schemes.

cs.IT↗

arXiv2024

Effective Communication: When to Pull Updates?

We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals so as to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the long-term average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates.

cs.IT↗

arXiv2024

Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundaries and integrate communication, computation, control, and intelligence. This paper presents the 6G-GOALS approach to goal-oriented and semantic communications for AI-Native 6G Networks. The proposed approach incorporates semantic, pragmatic, and goal-oriented communication into AI-native technologies, aiming to facilitate information exchange between intelligent agents in a more relevant, effective, and timely manner, thereby optimizing bandwidth, latency, energy, and electromagnetic field (EMF) radiation. The focus is on distilling data to its most relevant form and terse representation, aligning with the source's intent or the destination's objectives and context, or serving a specific goal. 6G-GOALS builds on three fundamental pillars: i) AI-enhanced semantic data representation, sensing, compression, and communication, ii) foundational AI reasoning and causal semantic data representation, contextual relevance, and value for goal-oriented effectiveness, and iii) sustainability enabled by more efficient wireless services. Finally, we illustrate two proof-of-concepts implementing semantic, goal-oriented, and pragmatic communication principles in near-future use cases. Our study covers the project's vision, methodologies, and potential impact.

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