Pablo Soldati

From Intents to Actions: Agentic AI in Autonomous Networks

Burak Demirel, Pablo Soldati, Yu Fei Wang

Telecommunication networks are increasingly expected to operate autonomously while supporting heterogeneous services with diverse and often conflicting intents -- that is, performance objectives, constraints, and requirements specific to each service. However, transforming high-level intents -- such as ultra-low latency, high throughput, or …

From Intents to Actions: Agentic AI in Autonomous Networks

Burak Demirel, Pablo Soldati, Yu Fei Wang

Telecommunication networks are increasingly expected to operate autonomously while supporting heterogeneous services with diverse and often conflicting intents -- that is, performance objectives, constraints, and requirements specific to each service. However, transforming high-level intents -- such as ultra-low latency, high throughput, or …

Generalization in Reinforcement Learning for Radio Access Networks

Burak Demirel, Yu Wang, Cristian Tatino, Pablo Soldati

Modern radio access networks (RANs) operate in highly dynamic and heterogeneous environments, where hand-tuned, rule-based radio resource management (RRM) algorithms frequently underperform. While reinforcement learning (RL) can surpass these heuristics in constrained scenarios, the unpredictable nature of radio channels and the diversity …

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Practical Policy Distillation for Reinforcement Learning in Radio Access Networks

Sara Khosravi, Burak Demirel, Javier Rasines, Pablo Soldati

Adopting artificial intelligence (AI) in radio access networks (RANs) presents several challenges, limited availability of link-level measurements (e.g., CQI reports), stringent real-time processing constraints (e.g., sub-1ms per TTI), and network heterogeneity (different spectrum bands, cell types, and vendor equipment). A critical yet …

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Generalization in Reinforcement Learning for Radio Access Networks

Burak Demirel, Yu Wang, Cristian Tatino, Pablo Soldati

Modern RAN operate in highly dynamic and heterogeneous environments, where hand-tuned, rule-based RRM algorithms often underperform. While RL can surpass such heuristics in constrained settings, the diversity of deployments and unpredictable radio conditions introduce major generalization challenges. Data-driven policies frequently overfit to …

Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation

Samuele Peri, Alessio Russo, Gábor Fodor, Pablo Soldati

Link adaptation (LA) is an essential function in modern wireless communication systems that dynamically adjusts the transmission rate of a communication link to match time- and frequency-varying radio link conditions. However, factors such as user mobility, fast fading, imperfect channel quality information, …

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Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation

Samuele Peri, Alessio Russo, Gábor Fodor, Pablo Soldati

Link adaptation (LA) is an essential function in modern wireless communication systems that dynamically adjusts the transmission rate of a communication link to match time- and frequency-varying radio link conditions. However, factors such as user mobility, fast fading, imperfect channel quality information, …

Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks

Pablo Soldati, Euhanna Ghadimi, Burak Demirel, Yu Wang et autres

This article emphasizes the pivotal role of model generalization in enhancing performance and enabling scalable AI integration within radio communications. We outline design principles for model generalization in three key domains: environment for robustness, intents for adaptability to system objectives, and control …

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Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks

Pablo Soldati, Euhanna Ghadimi, Burak Demirel, Yu Wang et autres

Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in radio communication systems. Research in this area, however, focused on AI solutions for specific, limited conditions, hindering models from learning and adapting to generic situations, such …

Approaching AI-native RANs through generalization and scalability of learning

Pablo Soldati, Euhanna Ghadimi, Burak Demirel, Yu Wang et autres

A holistic vision of an AI-native radio-access network (RAN) would be a system designed for artificial intelligence (AI) algorithms, in which a single AI algorithm could learn and govern most networking operations, ranging from the physical layer to Radio Resource Management (RRM).

Decentralized Joint Beamforming, User Scheduling, and QoS Management in 5G and Beyond Systems

Roberto Pinto Antonioli, Gábor Fodor, Pablo Soldati, Tarcísio F. Maciel

Fifth generation cellular systems support a broad range of services, including mobile broadband, critical and massive Internet of Things and are used in a variety of scenarios. In many of these scenarios, the main challenge is maintaining high throughput and ensuring proper …

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Decentralized Joint Beamforming, User Scheduling and QoS Management in\n 5G and Beyond Systems

Roberto P. Antonioli, Gábor Fodor, Pablo Soldati, Tarcísio F. Maciel

Fifth generation cellular systems support a broad range of services,\nincluding mobile broadband, critical and massive Internet of Things and are\nused in a variety of scenarios. In many of these scenarios, the main challenge\nis maintaining high throughput and ensuring proper quality of service …