Abstract Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learnin...
Network function virtualization (NFV) has drawn significant attention from both industry and academia as an important shift in telecommunication service provisioning. By decoupling network functions (NFs) from the physical devices on which they run, NFV has the potential to lead to significant reductions in operating expenses (OPEX) and capital expenses (CAPEX) and facilitate the deployment of new services with increased agility and faster time-to-value. The NFV paradigm is still in its infancy and there is a large spectrum of opportunities for the research community to develop new architectures, systems and applications, and to evaluate alternatives and trade-offs in developing technologies for its successful deployment. In this paper, after discussing NFV and its relationship with complementary fields of software defined networking (SDN) and cloud computing, we survey the state-of-the-art in NFV, and identify promising research directions in this area. We also overview key NFV projects, standardization efforts, early implementations, use cases, and commercial products.
This paper describes a database designed to evaluate the performance of speech recognition algorithms in noisy conditions. The database may either be used for the evaluation of front-end feature extraction algorithms using a defined HMM recognition back-end or complete recognition systems. The source speech for this database is the TIdigits, consisting of connected digits task spoken by American English talkers (downsampled to 8kHz) . A selection of 8 different real-world noises have been added to the speech over a range of signal to noise ratios and special care has been taken to control the filtering of both the speech and noise. The framework was prepared as a contribution to the ETSI STQ-AURORA DSR Working Group [1]. Aurora is developing standards for Distributed Speech Recognition (DSR) where the speech analysis is done in the telecommunication terminal and the recognition at a central location in the telecom network. The framework is currently being used to evaluate alternative proposals for front-end feature extraction. The database has been made publicly available through ELRA so that other speech researchers can evaluate and compare the performance of noise robust algorithms. Recognition results are presented for the first standard DSR feature extraction scheme that is based on a cepstral analysis.
This paper proposes a Kalman-gain-driven neural Kalman filtering (KF) defense framework, termed KFDBP, for secure state estimation in cyber–physical systems (CPSs) under denial-of-service (DoS), spoofing, and replay attacks. Unlike end-to-end neural filtering approaches such as KalmanNet that directly learn state estimators or implicitly approximate the Kalman gain using deep recurrent architectures, the proposed method employs a lightweight back-propagation (BP) neural network to adaptively regulate the Kalman gain online, while strictly preserving the classical Kalman filter prediction–correction recursion. By formulating an innovation-oriented Kalman gain learning objective, KFDBP explicitly addresses attack-induced observation uncertainty and non-Gaussian measurement corruption without requiring prior knowledge of attack timing, attack type, or attack probability during online estimation. The bounded gain regulation mechanism enhances estimation stability and interpretability, which are critical for safety-sensitive CPS applications, while significantly reducing computational complexity compared with deep neural network–based filters. Extensive Monte Carlo simulations under single and hybrid attack scenarios demonstrate that KFDBP consistently achieves lower estimation error and improved robustness than the conventional Kalman filter and KalmanNet under different attack probabilities, making it suitable for real-time and resource-constrained CPS applications.
As humans make ambitious efforts toward long-duration activities beyond Earth, new challenges will continue to emerge that highlight the need for governance frameworks capable of managing shared resources and technical standards in order to sustain human life in these hostile environments. Earth-based governance models of cooperative sovereignty can inform governance mechanisms for future Mars settlements, particularly regarding inter-settlement relations and the technical coordination required for multiple independent settlements to coexist. This study analyzes the International Telecommunication Union (ITU) and the Universal Postal Union (UPU), two of the oldest international organizations, which have successfully established evolving standards across sovereign nations. This analysis of the development and governance structures of these two organizations, and how they resolved key sovereignty issues, reveals principles that could be applicable to future settlements beyond Earth, particularly on Mars. Key insights include the strategic necessity of institutional neutrality, the management of asymmetric power relations, and the governance of shared resources under conditions of mutual vulnerability. The study distinguishes between a "Survival Layer" of technical standards essential for immediate safety and an "Operational Layer" governing economic and political activities, suggesting different governance approaches for each. Although some of these examples of cooperative sovereignty on Earth might not be sufficient for Mars due to its unique environment, lessons from the ITU and UPU case studies offer valuable strategies for designing flexible and sustainable governance models that can function from inception through explicit Earth-based coordination.
Clement Ruah, Houssem Sifaou, Osvaldo Simeone
et al.
Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datasets. However, there is a need to develop solutions to bridge the inherent simulation-to-reality (sim-to-real) gap between synthetic and real-world data. This paper reviews recent advances on two complementary strategies: 1) the calibration of digital twins (DTs) through real-world measurements, and 2) the use of sim-to-real gap-aware training strategies to robustly handle residual discrepancies between digital twin-generated and real data. For the latter, we evaluate two conceptually distinct methods that model the sim-to-real gap either at the level of the environment via Bayesian learning or at the level of the training loss via prediction-powered inference.
The integration of artificial intelligence (AI) into telecommunications infrastructure introduces novel risks, such as algorithmic bias and unpredictable system behavior, that fall outside the scope of traditional cybersecurity and data protection frameworks. This paper introduces a precise definition and a detailed typology of telecommunications AI incidents, establishing them as a distinct category of risk that extends beyond conventional cybersecurity and data protection breaches. It argues for their recognition as a distinct regulatory concern. Using India as a case study for jurisdictions that lack a horizontal AI law, the paper analyzes the country's key digital regulations. The analysis reveals that India's existing legal instruments, including the Telecommunications Act, 2023, the CERT-In Rules, and the Digital Personal Data Protection Act, 2023, focus on cybersecurity and data breaches, creating a significant regulatory gap for AI-specific operational incidents, such as performance degradation and algorithmic bias. The paper also examines structural barriers to disclosure and the limitations of existing AI incident repositories. Based on these findings, the paper proposes targeted policy recommendations centered on integrating AI incident reporting into India's existing telecom governance. Key proposals include mandating reporting for high-risk AI failures, designating an existing government body as a nodal agency to manage incident data, and developing standardized reporting frameworks. These recommendations aim to enhance regulatory clarity and strengthen long-term resilience, offering a pragmatic and replicable blueprint for other nations seeking to govern AI risks within their existing sectoral frameworks.
Leandro Márcio Bertholdo, Renan Barreto Paredes, Gabriela de Lima Marin
et al.
In May 2024, weeks of severe rainfall in Rio Grande do Sul, Brazil caused widespread damage to infrastructure, impacting over 400 cities and 2.3 million people. This study presents the construction of comprehensive telecommunications datasets during this climatic event, encompassing Internet measurements, fiber cut reports, and Internet Exchange routing data. By correlating network disruptions with hydrological and operational factors, the dataset offers insights into the resilience of fiber networks, data centers, and Internet traffic during critical events. For each scenario, we investigate failures related to the Information and Communication Technology infrastructure and highlight the challenges faced when its resilience is critically tested. Preliminary findings reveal trends in connectivity restoration, infrastructure vulnerabilities, and user behavior changes. These datasets and pre-analysis aim to support future research on disaster recovery strategies and the development of robust telecommunications systems.
The exponential growth of internet generated data has fueled advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) for extracting actionable insights in marketing,telecom, and health sectors. This chapter explores ML applications across three domains namely healthcare, marketing, and telecommunications, with a primary focus on developing a framework for optimal ML algorithm selection. In healthcare, the framework addresses critical challenges such as cardiovascular disease prediction accounting for 28.1% of global deaths and fetal health classification into healthy or unhealthy states, utilizing three datasets. ML algorithms are categorized into eager, lazy, and hybrid learners, selected based on dataset attributes, performance metrics (accuracy, precision, recall), and Akaike Information Criterion (AIC) scores. For validation, eight datasets from the three sectors are employed in the experiments. The key contribution is a recommendation framework that identifies the best ML model according to input attributes, balancing performance evaluation and model complexity to enhance efficiency and accuracy in diverse real-world applications. This approach bridges gaps in automated model selection, offering practical implications for interdisciplinary ML deployment.
Driven by both business and technology, the communication industry is focused on smart network operations. It is an industry consensus to promote network intelligent operation with autonomous networks as the driving force. It was analyzed that operational support system (OSS) was the core component of the three-layer architecture of autonomous network, and the key to improve the level of autonomous networks was to enhance the intelligence capability of OSS. Specific implementation schemes such as the definition method of OSS product business scope, the analysis of the shortcoming of OSS product ability,and systematic improvement of OSS product capabilities driven by autonomous networks were elaborated. The solution was described in detail using the digital operation value scenario of broadband services as an example. Finally, the intelligent ability map of OSS driven by autonomous network was discussed. Improving the intelligence capability of OSS for autonomous network can effectively promote the research and development direction of OSS, and guide the planning and research and development of OSS products for operators.
Tasnim Ahmed, Nicola Piovesan, Antonio De Domenico
et al.
Large Language Models (LLMs) have emerged as a significant advancement in the field of Natural Language Processing (NLP), demonstrating remarkable capabilities in language generation and other language-centric tasks. Despite their evaluation across a multitude of analytical and reasoning tasks in various scientific domains, a comprehensive exploration of their knowledge and understanding within the realm of natural language tasks in the telecommunications domain is still needed. This study, therefore, seeks to evaluate the knowledge and understanding capabilities of LLMs within this domain. To achieve this, we conduct an exhaustive zero-shot evaluation of four prominent LLMs-Llama-2, Falcon, Mistral, and Zephyr. These models require fewer resources than ChatGPT, making them suitable for resource-constrained environments. Their performance is compared with state-of-the-art, fine-tuned models. To the best of our knowledge, this is the first work to extensively evaluate and compare the understanding of LLMs across multiple language-centric tasks in this domain. Our evaluation reveals that zero-shot LLMs can achieve performance levels comparable to the current state-of-the-art fine-tuned models. This indicates that pretraining on extensive text corpora equips LLMs with a degree of specialization, even within the telecommunications domain. We also observe that no single LLM consistently outperforms others, and the performance of different LLMs can fluctuate. Although their performance lags behind fine-tuned models, our findings underscore the potential of LLMs as a valuable resource for understanding various aspects of this field that lack large annotated data.
This paper proposes a slot-based energy storage approach for decision-making in the context of an Off-Grid telecommunication operator. We consider network systems powered by solar panels, where harvest energy is stored in a battery that can also be sold when fully charged. To reflect real-world conditions, we account for non-stationary energy arrivals and service demands that depend on the time of day, as well as the failure states of PV panel. The network operator we model faces two conflicting objectives: maintaining the operation of its infrastructure and selling (or supplying to other networks) surplus energy from fully charged batteries. To address these challenges, we developed a slot-based Markov Decision Process (MDP) model that incorporates positive rewards for energy sales, as well as penalties for energy loss and battery depletion. This slot-based MDP follows a specific structure we have previously proven to be efficient in terms of computational performance and accuracy. From this model, we derive the optimal policy that balances these conflicting objectives and maximizes the average reward function. Additionally, we present results comparing different cities and months, which the operator can consider when deploying its infrastructure to maximize rewards based on location-specific energy availability and seasonal variations.
Customer churn, the discontinuation of services by existing customers, poses a significant challenge to the telecommunications industry. This paper proposes a novel adaptive ensemble learning framework for highly accurate customer churn prediction. The framework integrates multiple base models, including XGBoost, LightGBM, LSTM, a Multi-Layer Perceptron (MLP) neural network, and Support Vector Machine (SVM). These models are strategically combined using a stacking ensemble method, further enhanced by meta-feature generation from base model predictions. A rigorous data preprocessing pipeline, coupled with a multi-faceted feature engineering approach, optimizes model performance. The framework is evaluated on three publicly available telecom churn datasets, demonstrating substantial accuracy improvements over state-of-the-art techniques. The research achieves a remarkable 99.28% accuracy, signifying a major advancement in churn prediction.The implications of this research for developing proactive customer retention strategies withinthe telecommunications industry are discussed.
The World Radiocommunications Conference 2023 (WRC-23), held in Dubai, intensively discussed several Agenda Items related to possible new International Mobile Telecommunications (IMT) identifications of several radio frequency bands. One example of a frequency band is 10-10.5 GHz, which has a primary allocation to Radiolocation, Earth-Exploration Satellite services. In order to ensure continued protection of such incumbent services, sharing and compatibility studies were conducted, and debated at length, at the ITU-R Study Groups from the year 2020 to 2023. This document presents a coexistence study. During the 41st Meeting of Permanent Consultative Committee II: Radiocommunications of the Inter American Telecommunication Commission (CITEL), one administration modified its previous Draft Inter-American Proposal, to include additional protection to the incumbent services (e.g., EESS (Active)). The same administration introduced three conditions meant to ensure protection of the incumbent services, in particular for the EESS (Active) and Radiolocation Service. Such conditions were: Administrations shall take practical measures to ensure the transmitting antennas of outdoor IMT base stations (BSs) are normally pointing below the true horizon when deployed within 10-10.5 GHz; additionally, the mechanical pointing needs to be at or below the horizon; Administrations shall use side lobe suppression techniques providing 29.5 dB of attenuation for elevation angles above 30 degrees where 0 degrees relates to the horizon and 90 degrees to the zenith, referenced to the maximum antenna gain at the boresight; The maximum equivalent isotropically radiated power (e.i.r.p.) emitted by an IMT base station shall not exceed 32 dB W/100 MHz.