Ponències/Comunicacions de congressos
http://hdl.handle.net/2117/1659
2024-03-19T06:43:05ZThe role of network digital twinning in a 6G-oriented optimization-as-a-service platform
http://hdl.handle.net/2117/403910
The role of network digital twinning in a 6G-oriented optimization-as-a-service platform
Martínez Caro, José Manuel; Pérez Romero, Jordi; Agüero Calvo, Ramón; Muñiz, Alejandro; Atxutegi, Eneko; Díez, Luis; Contreras, Luis Miguel; González, Ivan; Moreno-Muro, Francisco Javier; Pavón-Marino, Pablo; Sallent Roig, Oriol
The advent of 5G has meant providing users with higher data rates, more connected devices and lower latency, implying the need to redefine traditional network management methods. In addition, 6G is expected to provide much more challenging and restrictive performance, enhancing the idea of redefining network management methodologies, aimed at maximising the use of network resources. Network Digital Twinning (NDT) and Optimization-as-a-Service (OaaS) are two key mechanisms, along with the use of artificial intelligence (AI) and machine learning (ML), to assist in network management procedures. On the one hand, the NDT is a digital replica of the physical network, which permits evaluating the impact of concrete configuration settings prior to its real implementation in the system, helping to optimize the overall network performance. On the other hand, OaaS offers ad-hoc optimized recommendations to third-party applications based on the execution of several optimization algorithms. In this work, we propose an open, flexible and scalable OaaS platform, defined in the OPTIMAIX project, to exploit the NDT potential targeted to support automated planning and optimization of networks. Also, an evaluation of the NDT role within this architecture is provided.
2024-03-07T09:06:04ZMartínez Caro, José ManuelPérez Romero, JordiAgüero Calvo, RamónMuñiz, AlejandroAtxutegi, EnekoDíez, LuisContreras, Luis MiguelGonzález, IvanMoreno-Muro, Francisco JavierPavón-Marino, PabloSallent Roig, OriolThe advent of 5G has meant providing users with higher data rates, more connected devices and lower latency, implying the need to redefine traditional network management methods. In addition, 6G is expected to provide much more challenging and restrictive performance, enhancing the idea of redefining network management methodologies, aimed at maximising the use of network resources. Network Digital Twinning (NDT) and Optimization-as-a-Service (OaaS) are two key mechanisms, along with the use of artificial intelligence (AI) and machine learning (ML), to assist in network management procedures. On the one hand, the NDT is a digital replica of the physical network, which permits evaluating the impact of concrete configuration settings prior to its real implementation in the system, helping to optimize the overall network performance. On the other hand, OaaS offers ad-hoc optimized recommendations to third-party applications based on the execution of several optimization algorithms. In this work, we propose an open, flexible and scalable OaaS platform, defined in the OPTIMAIX project, to exploit the NDT potential targeted to support automated planning and optimization of networks. Also, an evaluation of the NDT role within this architecture is provided.AI-powered edge computing evolution for beyond 5G communication networks
http://hdl.handle.net/2117/394179
AI-powered edge computing evolution for beyond 5G communication networks
Kartsakli, Elli; Pérez Romero, Jordi; Sallent Roig, Oriol; Bartzoudis, Nikolaos; Frascolla, Valerio; Mohalik, Swarup Kumar; Metsch, Thijs; Antonopoulos, Angelos; Tuna, Ömer Faruk; Deng, Yansha; Tao, Xin; Serrano, Maria A.; Quiñones Moreno, Eduardo
Edge computing is a key enabling technology that is expected to play a crucial role in beyond 5G (B5G) and 6G communication networks. By bringing computation closer to where the data is generated, and leveraging Artificial Intelligence (AI) capabilities for advanced automation and orchestration, edge computing can enable a wide range of emerging applications with extreme requirements in terms of latency and computation, across multiple vertical domains. In this context, this paper first discusses the key technological challenges for the seamless integration of edge computing within B5G/6G and then presents a roadmap for the edge computing evolution, proposing a novel design approach for an open, intelligent, trustworthy, and distributed edge architecture.
2023-09-28T07:59:51ZKartsakli, ElliPérez Romero, JordiSallent Roig, OriolBartzoudis, NikolaosFrascolla, ValerioMohalik, Swarup KumarMetsch, ThijsAntonopoulos, AngelosTuna, Ömer FarukDeng, YanshaTao, XinSerrano, Maria A.Quiñones Moreno, EduardoEdge computing is a key enabling technology that is expected to play a crucial role in beyond 5G (B5G) and 6G communication networks. By bringing computation closer to where the data is generated, and leveraging Artificial Intelligence (AI) capabilities for advanced automation and orchestration, edge computing can enable a wide range of emerging applications with extreme requirements in terms of latency and computation, across multiple vertical domains. In this context, this paper first discusses the key technological challenges for the seamless integration of edge computing within B5G/6G and then presents a roadmap for the edge computing evolution, proposing a novel design approach for an open, intelligent, trustworthy, and distributed edge architecture.A deep q network-based multi-connectivity algorithm for heterogeneous 4G/5G cellular systems
http://hdl.handle.net/2117/387322
A deep q network-based multi-connectivity algorithm for heterogeneous 4G/5G cellular systems
Hernández Carlón, Juan Jesús; Pérez Romero, Jordi; Sallent Roig, Oriol; Vilà Muñoz, Irene; Casadevall Palacio, Fernando José
Multi-connectivity, which allows a user equipment to be simultaneously connected to multiple cells from different radio access network nodes that can be from a single or multiple radio access technologies, has emerged as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill high data rate and reliability requirements. This paper proposes the use of deep reinforcement learning to optimally split the traffic among cells when multi-connectivity is considered in a heterogeneous 4G/5G networks scenario. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits depending on the current traffic and radio conditions in the considered scenario. Moreover, the paper analyses the robustness of the obtained policy in front of variations with respect to the conditions used during the training.
2023-05-11T15:06:38ZHernández Carlón, Juan JesúsPérez Romero, JordiSallent Roig, OriolVilà Muñoz, IreneCasadevall Palacio, Fernando JoséMulti-connectivity, which allows a user equipment to be simultaneously connected to multiple cells from different radio access network nodes that can be from a single or multiple radio access technologies, has emerged as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill high data rate and reliability requirements. This paper proposes the use of deep reinforcement learning to optimally split the traffic among cells when multi-connectivity is considered in a heterogeneous 4G/5G networks scenario. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits depending on the current traffic and radio conditions in the considered scenario. Moreover, the paper analyses the robustness of the obtained policy in front of variations with respect to the conditions used during the training.Deep learning-based multi-connectivity optimization in cellular networks
http://hdl.handle.net/2117/387277
Deep learning-based multi-connectivity optimization in cellular networks
Hernández Carlón, Juan Jesús; Pérez Romero, Jordi; Sallent Roig, Oriol; Vilà Muñoz, Irene; Casadevall Palacio, Fernando José
Multi-connectivity emerges as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill the demanding requirements in terms of data rate and reliability. It allows a device to be simultaneously connected to multiple cells belonging to different radio access network nodes from a single or multiple radio access technologies. This paper addresses the problem of optimally splitting the traffic among cells when multi-connectivity is used. For this purpose, it proposes the use of deep learning to determine the optimum amount of traffic of a device that needs to be sent through one or another cell depending on the current traffic and radio conditions. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits in the considered scenario.
2023-05-10T16:29:33ZHernández Carlón, Juan JesúsPérez Romero, JordiSallent Roig, OriolVilà Muñoz, IreneCasadevall Palacio, Fernando JoséMulti-connectivity emerges as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill the demanding requirements in terms of data rate and reliability. It allows a device to be simultaneously connected to multiple cells belonging to different radio access network nodes from a single or multiple radio access technologies. This paper addresses the problem of optimally splitting the traffic among cells when multi-connectivity is used. For this purpose, it proposes the use of deep learning to determine the optimum amount of traffic of a device that needs to be sent through one or another cell depending on the current traffic and radio conditions. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits in the considered scenario.On the implementation of a reinforcement learning-based capacity sharing algorithm in O-RAN
http://hdl.handle.net/2117/387183
On the implementation of a reinforcement learning-based capacity sharing algorithm in O-RAN
Vilà Muñoz, Irene; Sallent Roig, Oriol; Pérez Romero, Jordi
The capacity sharing problem in Radio Access Network (RAN) slicing deals with the distribution of the capacity available in each RAN node among various RAN slices to satisfy their traffic demands and efficiently use the radio resources. While several capacity sharing algorithmic solutions have been proposed in the literature, their practical implementation still remains as a gap. In this paper, the implementation of a Reinforcement Learning-based capacity sharing algorithm over the O-RAN architecture is discussed, providing insights into the operation of the involved interfaces and the containerization of the solution. Moreover, the description of the testbed implemented to validate the solution is included and some performance and validation results are presented.
2023-05-08T16:32:33ZVilà Muñoz, IreneSallent Roig, OriolPérez Romero, JordiThe capacity sharing problem in Radio Access Network (RAN) slicing deals with the distribution of the capacity available in each RAN node among various RAN slices to satisfy their traffic demands and efficiently use the radio resources. While several capacity sharing algorithmic solutions have been proposed in the literature, their practical implementation still remains as a gap. In this paper, the implementation of a Reinforcement Learning-based capacity sharing algorithm over the O-RAN architecture is discussed, providing insights into the operation of the involved interfaces and the containerization of the solution. Moreover, the description of the testbed implemented to validate the solution is included and some performance and validation results are presented.Marco de desarrollo software e implementación de algoritmos de inteligencia artificial para la gestión de redes radio 5G
http://hdl.handle.net/2117/387057
Marco de desarrollo software e implementación de algoritmos de inteligencia artificial para la gestión de redes radio 5G
Vilà Muñoz, Irene; Sallent Roig, Oriol; Pérez Romero, Jordi
The increase in complexity of 5G and beyond mobile communications networks to accommodate multiple services with stringent requirements has led to the introduction of Artificial Intelligence (AI) capabilities for automating their management and operation, particularly in the Radio Access Network (RAN). Although there exist a large number of proposals of AI algorithms for different problems in the RAN, little attention has been paid to their practical implementation. This paper intends to fill this gap by discussing the practical aspects on the software development and implementation of AI algorithms for the RAN. This is done based on a specific example that uses deep reinforcement learning for the capacity sharing problem in RAN slicing. The paper presents an implementation of this solution in the context of the O-RAN architecture, detailing the operation of the involved interfaces and the containerization of the solution.
2023-05-04T15:21:15ZVilà Muñoz, IreneSallent Roig, OriolPérez Romero, JordiThe increase in complexity of 5G and beyond mobile communications networks to accommodate multiple services with stringent requirements has led to the introduction of Artificial Intelligence (AI) capabilities for automating their management and operation, particularly in the Radio Access Network (RAN). Although there exist a large number of proposals of AI algorithms for different problems in the RAN, little attention has been paid to their practical implementation. This paper intends to fill this gap by discussing the practical aspects on the software development and implementation of AI algorithms for the RAN. This is done based on a specific example that uses deep reinforcement learning for the capacity sharing problem in RAN slicing. The paper presents an implementation of this solution in the context of the O-RAN architecture, detailing the operation of the involved interfaces and the containerization of the solution.RAN slicing for multi-tenancy support in a WLAN scenario
http://hdl.handle.net/2117/386011
RAN slicing for multi-tenancy support in a WLAN scenario
Koutlia, Katerina; Umbert Juliana, Anna; García Escriche, Sergio; Casadevall Palacio, Fernando José
Radio Access Network (RAN) slicing is a key technology, based on Software Defined Networks (SDN) and Network Function Virtualization (NFV), which aims at providing a more efficient utilization of the available network resources and the reduction of the operational costs. On that respect, in this demo a Wireless LAN hypervisor is presented that is based on a time variant scheduling mechanism and that is able to follow the dynamicity of the traffic variations seen by the different tenants in the network Access Points (APs). The work builds upon the 5G-EmPOWER tool kit, which is provided with SDN and NFV capabilities. During this demo it will be shown that the proposed hypervisor is able to dynamically assign, in every AP of the network, the appropriate resources per tenant according to their traffic requirements.
2023-04-06T08:09:21ZKoutlia, KaterinaUmbert Juliana, AnnaGarcía Escriche, SergioCasadevall Palacio, Fernando JoséRadio Access Network (RAN) slicing is a key technology, based on Software Defined Networks (SDN) and Network Function Virtualization (NFV), which aims at providing a more efficient utilization of the available network resources and the reduction of the operational costs. On that respect, in this demo a Wireless LAN hypervisor is presented that is based on a time variant scheduling mechanism and that is able to follow the dynamicity of the traffic variations seen by the different tenants in the network Access Points (APs). The work builds upon the 5G-EmPOWER tool kit, which is provided with SDN and NFV capabilities. During this demo it will be shown that the proposed hypervisor is able to dynamically assign, in every AP of the network, the appropriate resources per tenant according to their traffic requirements.On the value of context awareness for relay activation in beyond 5G radio access networks
http://hdl.handle.net/2117/384007
On the value of context awareness for relay activation in beyond 5G radio access networks
Pérez Romero, Jordi; Sallent Roig, Oriol
This paper envisions to augment the Radio Access Network (RAN) infrastructure in Beyond 5G(B5G) systems by exploiting relaying capabilities of user equipment (UE) as a way to improve the coverage, capacity and robustness. Despite the concept and enabling technologies have been in place for some time, their efficient realization requires the conception and development of new features in B5G systems. Among them, this paper focuses on the Relay UE (RUE) activation decision making, in charge of deciding where and when a UE is suitable to be activated to relay traffic from other UEs. Specifically, the paper analyses seven RUE activation strategies that differ on the criteria and the type of context information considered for this decision-making problem. The considered strategies are evaluated through system level simulations in a realistic urban scenario with the objective of assessing the value of each type of context information. Results reveal that the most efficient strategies from the perspective of outage probability reduction are those that account for the number of UEs that would be served by a RUE based on the experienced spectral efficiency.
2023-02-23T09:34:41ZPérez Romero, JordiSallent Roig, OriolThis paper envisions to augment the Radio Access Network (RAN) infrastructure in Beyond 5G(B5G) systems by exploiting relaying capabilities of user equipment (UE) as a way to improve the coverage, capacity and robustness. Despite the concept and enabling technologies have been in place for some time, their efficient realization requires the conception and development of new features in B5G systems. Among them, this paper focuses on the Relay UE (RUE) activation decision making, in charge of deciding where and when a UE is suitable to be activated to relay traffic from other UEs. Specifically, the paper analyses seven RUE activation strategies that differ on the criteria and the type of context information considered for this decision-making problem. The considered strategies are evaluated through system level simulations in a realistic urban scenario with the objective of assessing the value of each type of context information. Results reveal that the most efficient strategies from the perspective of outage probability reduction are those that account for the number of UEs that would be served by a RUE based on the experienced spectral efficiency.On relay user equipment activation in beyond 5G radio access networks
http://hdl.handle.net/2117/382712
On relay user equipment activation in beyond 5G radio access networks
Pérez Romero, Jordi; Sallent Roig, Oriol; Ruiz García, Olga
This paper envisages a Beyond 5G (B5G) Radio Access Network (RAN) in which the relaying capabilities offered by user equipment (UE) are used as a way to improve the coverage and robustness of the network. The paper proposes and develops the functional framework for supporting the activation of the suitable relay UEs (RUEs) in coverage constrained scenarios. It is based on characterizing each potential RUE through a utility metric that measures the coverage enhancements brought to the network when the RUE is activated. To derive this metric for all the candidate RUEs, the framework considers the use of a Network Digital Twin that allows the offline analysis of different configurations in a fast and safe way. Using the proposed framework, a RUE activation algorithm is proposed and evaluated. The obtained results reflect that significant outage probability reductions can be obtained in the scenario under different traffic distributions thanks to the activation of the RUEs with the highest utility.
2023-02-09T08:02:40ZPérez Romero, JordiSallent Roig, OriolRuiz García, OlgaThis paper envisages a Beyond 5G (B5G) Radio Access Network (RAN) in which the relaying capabilities offered by user equipment (UE) are used as a way to improve the coverage and robustness of the network. The paper proposes and develops the functional framework for supporting the activation of the suitable relay UEs (RUEs) in coverage constrained scenarios. It is based on characterizing each potential RUE through a utility metric that measures the coverage enhancements brought to the network when the RUE is activated. To derive this metric for all the candidate RUEs, the framework considers the use of a Network Digital Twin that allows the offline analysis of different configurations in a fast and safe way. Using the proposed framework, a RUE activation algorithm is proposed and evaluated. The obtained results reflect that significant outage probability reductions can be obtained in the scenario under different traffic distributions thanks to the activation of the RUEs with the highest utility.On alleviating cell overload in vehicular scenarios
http://hdl.handle.net/2117/381215
On alleviating cell overload in vehicular scenarios
Trullenque Ortiz, Martín; Sallent Roig, Oriol; Camps Mur, Daniel; Escrig, Josep; Herranz Claveras, Carlos; Nasreddine, Jad; Pérez Romero, Jordi
Fifth Generation (5G) networks will support countless new applications and new business models. One of the 5G paradigms is network slicing, which enables the integration of multiple logical networks each one tailored to the requirements of the different services that can be provided by both network operators and vertical industries. One of the services where 5G is expected to have a greatest impact is vehicular-to-everything (V2X) communications, which will have their stringent latency requirements now met. However, the mobility associated to vehicles can lead to cell overload compromising the required quality of service (QoS). To address this problem, in this paper we propose and evaluate the performance of three network overload alleviation techniques to control network congestion provoked by traffic jams using realistic vehicular traces in a network slicing environment. Firstly, we describe the architecture supporting V2X communications. Secondly, the network congestion control approaches are explained. Finally, after providing a complete description of the considered scenario, results will be detailed, showing that the network overload appearing during rush hour can be significantly reduced.
2023-01-26T12:15:49ZTrullenque Ortiz, MartínSallent Roig, OriolCamps Mur, DanielEscrig, JosepHerranz Claveras, CarlosNasreddine, JadPérez Romero, JordiFifth Generation (5G) networks will support countless new applications and new business models. One of the 5G paradigms is network slicing, which enables the integration of multiple logical networks each one tailored to the requirements of the different services that can be provided by both network operators and vertical industries. One of the services where 5G is expected to have a greatest impact is vehicular-to-everything (V2X) communications, which will have their stringent latency requirements now met. However, the mobility associated to vehicles can lead to cell overload compromising the required quality of service (QoS). To address this problem, in this paper we propose and evaluate the performance of three network overload alleviation techniques to control network congestion provoked by traffic jams using realistic vehicular traces in a network slicing environment. Firstly, we describe the architecture supporting V2X communications. Secondly, the network congestion control approaches are explained. Finally, after providing a complete description of the considered scenario, results will be detailed, showing that the network overload appearing during rush hour can be significantly reduced.