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Softwarization, programmable network control and the use of all-encompassing controllers acting at different timescales are heralded as the key drivers for the evolution to next-generation cellular networks. These technologies have fostered…
We study the important and challenging problem of virtualized radio access network (vRAN) design in its most general form. We develop an optimization framework that decides the number and deployment locations of central/cloud units (CUs);…
The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to…
Multi-label image classification demands adaptive training strategies to navigate complex, evolving visual-semantic landscapes, yet conventional methods rely on static configurations that falter in dynamic settings. We propose MAT-Agent, a…
The transition toward softwarized Radio Access Networks (RANs), driven by the Open RAN (O-RAN) paradigm, enables flexible, vendor-neutral deployments through disaggregation and virtualization of base station functions. However, this shift…
The Open Radio Access Network (O-RAN) architecture aims to support a plethora of network services, such as beam management and network slicing, through the use of third-party applications called xApps. To efficiently provide network…
This paper provides solutions for virtualizing C-RANs wireless resources and sharing them between multiple mobile network operators (MNOs). The proposed solutions dynamically allocate wireless resources to users who subscribe to MNOs across…
Open Radio Access Network (O-RAN) is considered as a major step in the evolution of next-generation cellular networks given its support for open interfaces and utilization of artificial intelligence (AI) into the deployment, operation, and…
We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL). We equip each transmitter in the network with a deep RL agent that receives…
Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging. Although Artificial Intelligence (AI) has been applied to many telecom tasks, existing models…
In recent years, the radio access network (RAN) domain has witnessed a sea change with increasing levels of virtualization and softwarization driven by emerging paradigms such as the Open RAN (O-RAN) movement. However, the fundamental…
The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation.…
Emerging AI/ML techniques have been showing great potential in automating network control in open radio access networks (Open RAN). However, existing approaches heavily rely on blackbox policies parameterized by deep neural networks, which…
[...] This paper presents AutoRAN, an automated, intent-driven framework for zero-touch provisioning of open, programmable cellular networks. Leveraging cloud-native principles, AutoRAN employs virtualization, declarative…
Distributed scheduling is essential for open radio access network (O-RAN) employing advanced physical-layer techniques such as multi-user MIMO (MU-MIMO), carrier aggregation (CA), and joint transmission (JT). This work investigates the…
Open RAN (O-RAN) defines an emerging cellular radio access network (RAN) architecture for future 6G wireless networks, emphasizing openness and intelligence which are considered the foundations of future 6G wireless networks. While the…
Future wireless networks are moving toward autonomous service operation, where network control and resource management need to respond to time-varying radio conditions and evolving service objectives. To address this shift, this article…
To keep supporting next-generation requirements, the radio access infrastructure will increasingly densify. Cell-free (CF) network architectures are emerging, combining dense deployments with extreme flexibility in allocating resources to…
Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to…
Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL…