WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils
http://hdl.handle.net/2117/7265
2024-03-29T07:32:22ZFlexible radio access network optimization with cell coordination
http://hdl.handle.net/2117/387212
Flexible radio access network optimization with cell coordination
Ruiz Boqué, Sílvia; García Lozano, Mario; Guerra Gómez, Rolando; Saeed, Umar
This paper focuses on Beyond fifth generation
(B5G) non-linear data modeling and decision-making tools
to optimize cost reduction versus coverage-QoS tradeoff.
Especially, the distribution of active Remote Radio
Heads or Units (RRHs) needed, according to traffic demands,
is improved. The proposed optimization platform
is based on a multi-objective optimization model, which
is designed to reduce the network cost while maintaining
the coverage-QoS. Capacity constraints, User Equipments
(UEs), and different slices are considered to test the
results under realistic conditions. Results at 3.6 and 28
GHz are presented by analyzing and comparing several
Cloud Radio Access Network (C-RAN) split options in
a heterogeneous deployment with Macro-RRHs (MRRHs)
and Small-RRHs (SRRHs). Results show cost reductions
from 30% to 70% depending on the scenario. Moreover, the
proposed algorithm aggregates the possibility to consider
the coordination between cells in order to improve the cost
reduction. The results considering cooperation has been
presented at both frequency bands with a fully centralized
C-RAN (split option 8).
2023-05-09T17:32:23ZRuiz Boqué, SílviaGarcía Lozano, MarioGuerra Gómez, RolandoSaeed, UmarThis paper focuses on Beyond fifth generation
(B5G) non-linear data modeling and decision-making tools
to optimize cost reduction versus coverage-QoS tradeoff.
Especially, the distribution of active Remote Radio
Heads or Units (RRHs) needed, according to traffic demands,
is improved. The proposed optimization platform
is based on a multi-objective optimization model, which
is designed to reduce the network cost while maintaining
the coverage-QoS. Capacity constraints, User Equipments
(UEs), and different slices are considered to test the
results under realistic conditions. Results at 3.6 and 28
GHz are presented by analyzing and comparing several
Cloud Radio Access Network (C-RAN) split options in
a heterogeneous deployment with Macro-RRHs (MRRHs)
and Small-RRHs (SRRHs). Results show cost reductions
from 30% to 70% depending on the scenario. Moreover, the
proposed algorithm aggregates the possibility to consider
the coordination between cells in order to improve the cost
reduction. The results considering cooperation has been
presented at both frequency bands with a fully centralized
C-RAN (split option 8).A comprehensive survey of V2X cybersecurity mechanisms and future research paths
http://hdl.handle.net/2117/382734
A comprehensive survey of V2X cybersecurity mechanisms and future research paths
Sedar, Mohottige Roshan Madhusanka; Kalalas, Charalampos; Vazquez Gallego, Francisco; Alonso Zárate, Luis Gonzaga; Alonso Zarate, Jesús
Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.
2023-02-09T12:08:05ZSedar, Mohottige Roshan MadhusankaKalalas, CharalamposVazquez Gallego, FranciscoAlonso Zárate, Luis GonzagaAlonso Zarate, JesúsRecent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.PSM-DMO: power save mode and discontinuous BLE mesh operation
http://hdl.handle.net/2117/376933
PSM-DMO: power save mode and discontinuous BLE mesh operation
Hernandez Solana, Angela; Pérez Díaz de Cerio, David; García Lozano, Mario; Valdovinos, Antonio; Valenzuela González, José Luis
The Bluetooth Low Energy (BLE) mesh profile, standardized by the Bluetooth Special Interest Group (SIG), has an increasing interest in IoT solutions. However, the standard assumes that relay and friend nodes should be continuously scanning the channel awaiting any incoming transmissions. This could be very inefficient in terms of energy consumption, particularly in application scenarios where the backbone of the mesh network cannot be powered and traffic is infrequent. Hence, we present a novel strategy, named PSM-DMO, that minimizes the scan periods and thus, significantly reduces the overall energy consumption of the mesh network. PSM-DMO is defined as a new and optional feature for the currently published BLE mesh specifications, coexists with the standard operation, and is implemented without modifying the core of the specification. The proposal, that ensures the reliability of the mesh operation, can be used in BLE sensor networks that can tolerate a certain transmission delay. PSM-DMO replaces the continuous scan by a periodic but asynchronous polling process whereby the relay and sink nodes interrogate their neighbors about the existence of data to receive or to retransmit through the network. Nodes only go into scan mode during the period of time the mesh network will be involved in the transmission and dissemination. This period is estimated by the node which is the source of data, it is announced to its neighbors and it is propagated consecutively by all the relay nodes until the destination. PSM-DMO allows a theoretical reduction in the energy consumption of relay nodes up to 99.24 %.
2022-11-22T17:04:06ZHernandez Solana, AngelaPérez Díaz de Cerio, DavidGarcía Lozano, MarioValdovinos, AntonioValenzuela González, José LuisThe Bluetooth Low Energy (BLE) mesh profile, standardized by the Bluetooth Special Interest Group (SIG), has an increasing interest in IoT solutions. However, the standard assumes that relay and friend nodes should be continuously scanning the channel awaiting any incoming transmissions. This could be very inefficient in terms of energy consumption, particularly in application scenarios where the backbone of the mesh network cannot be powered and traffic is infrequent. Hence, we present a novel strategy, named PSM-DMO, that minimizes the scan periods and thus, significantly reduces the overall energy consumption of the mesh network. PSM-DMO is defined as a new and optional feature for the currently published BLE mesh specifications, coexists with the standard operation, and is implemented without modifying the core of the specification. The proposal, that ensures the reliability of the mesh operation, can be used in BLE sensor networks that can tolerate a certain transmission delay. PSM-DMO replaces the continuous scan by a periodic but asynchronous polling process whereby the relay and sink nodes interrogate their neighbors about the existence of data to receive or to retransmit through the network. Nodes only go into scan mode during the period of time the mesh network will be involved in the transmission and dissemination. This period is estimated by the node which is the source of data, it is announced to its neighbors and it is propagated consecutively by all the relay nodes until the destination. PSM-DMO allows a theoretical reduction in the energy consumption of relay nodes up to 99.24 %.On the use of sniffers for spectrum occupancy measurements of Bluetooth low energy primary channels
http://hdl.handle.net/2117/374986
On the use of sniffers for spectrum occupancy measurements of Bluetooth low energy primary channels
Valenzuela Pérez, Ana; García Lozano, Mario; Valenzuela González, José Luis; Pérez Díaz de Cerio, David; Hernandez Solana, Angela; Valdovinos, Antonio
The methods usually employed to measure channel occupancy show limitations in the context of Bluetooth Low Energy (BLE) advertisements. We propose and analyze the use of BLE sniffers as light and portable low-cost spectrum occupancy meters to be used in scenarios where real time signal analyzers are not adequate. For the measurement technique to be successful, several low-level effects must be considered. The paper argues about on-air time, receiving blind times due to processing and intra system interference, buffer saturation and frequency anchoring. Hence, a compensation procedure based on collision rate estimation is proposed. Results with the refined method show that occupancies of 40% can be measured with an overestimation error whose percentile 95% is 5 percentage points. This is reduced to 1.9 points when the occupancy is 15%. The sniffers perform in real time and are shown to correctly track short term load variations. The strategy has been successfully used to characterize occupancy in highly variable and loaded scenarios such as subway platforms and a shopping mall. Values up to 25% have been observed, which implies a relevant packet error rate. Hence, the tool can be used to make agile audits and configure the parameters that control communication redundancy in new or existing networks.
2022-10-25T16:05:55ZValenzuela Pérez, AnaGarcía Lozano, MarioValenzuela González, José LuisPérez Díaz de Cerio, DavidHernandez Solana, AngelaValdovinos, AntonioThe methods usually employed to measure channel occupancy show limitations in the context of Bluetooth Low Energy (BLE) advertisements. We propose and analyze the use of BLE sniffers as light and portable low-cost spectrum occupancy meters to be used in scenarios where real time signal analyzers are not adequate. For the measurement technique to be successful, several low-level effects must be considered. The paper argues about on-air time, receiving blind times due to processing and intra system interference, buffer saturation and frequency anchoring. Hence, a compensation procedure based on collision rate estimation is proposed. Results with the refined method show that occupancies of 40% can be measured with an overestimation error whose percentile 95% is 5 percentage points. This is reduced to 1.9 points when the occupancy is 15%. The sniffers perform in real time and are shown to correctly track short term load variations. The strategy has been successfully used to characterize occupancy in highly variable and loaded scenarios such as subway platforms and a shopping mall. Values up to 25% have been observed, which implies a relevant packet error rate. Hence, the tool can be used to make agile audits and configure the parameters that control communication redundancy in new or existing networks.Demand-aware cooperative content caching in 5G/6G networks with MEC-enabled edges
http://hdl.handle.net/2117/374057
Demand-aware cooperative content caching in 5G/6G networks with MEC-enabled edges
Ayenew, Tadege Mihretu; Xenakis, Dionysis; Alonso Zárate, Luis Gonzaga; Passas, Nikos; Merakos, Lazaros
Today, billions of smart devices are interconnected via wireless networks, leading to large volumes of video contents circulating through the bandwidth-limited backhaul. This causes network performance to deteriorate. As a mitigation mechanism, caching of highly popular contents to network edges is deployed. We propose a cooperative and demand-aware caching strategy, which is modelled using the Separable Assignment Problem, to maximize the cache hit ratio. This problem is solved with a recursive enumeration method, where dynamic programming is used to fill each edge. The extensive application-level evaluations show that the proposed strategy outperforms existing caching policies.
2022-10-06T06:59:49ZAyenew, Tadege MihretuXenakis, DionysisAlonso Zárate, Luis GonzagaPassas, NikosMerakos, LazarosToday, billions of smart devices are interconnected via wireless networks, leading to large volumes of video contents circulating through the bandwidth-limited backhaul. This causes network performance to deteriorate. As a mitigation mechanism, caching of highly popular contents to network edges is deployed. We propose a cooperative and demand-aware caching strategy, which is modelled using the Separable Assignment Problem, to maximize the cache hit ratio. This problem is solved with a recursive enumeration method, where dynamic programming is used to fill each edge. The extensive application-level evaluations show that the proposed strategy outperforms existing caching policies.SCHEMA: Service Chain Elastic Management with distributed reinforcement learning
http://hdl.handle.net/2117/367516
SCHEMA: Service Chain Elastic Management with distributed reinforcement learning
Dalgkitsis, Anestis; Garrido Platero, Luis Ángel; Mekikis, Prodromos-Vasileios; Ramantas, Kostas; Alonso Zárate, Luis Gonzaga; Verikoukis, Christos
As the demand for Network Function Virtualization accelerates, service providers are expected to advance the way they manage and orchestrate their network services to offer lower latency services to their future users. Modern services require complex data flows between Virtual Network Functions, placed in separate network domains, risking an increase in latency that compromises the offered latency constraints. This shift requires high levels of automation to deal with the scale and load of future networks. In this paper, we formulate the Service Function Chaining (SFC) placement problem and then we tackle it by introducing SCHEMA, a Distributed Reinforcement Learning (RL) algorithm that performs complex SFC orchestration for low latency services. We combine multiple RL agents with a Bidding Mechanism to enable scalability on multi-domain networks. Finally, we use a simulation model to evaluate SCHEMA, and we demonstrate its ability to obtain a 60.54% reduction of average service latency when compared to a centralised RL solution.
2022-05-19T06:47:47ZDalgkitsis, AnestisGarrido Platero, Luis ÁngelMekikis, Prodromos-VasileiosRamantas, KostasAlonso Zárate, Luis GonzagaVerikoukis, ChristosAs the demand for Network Function Virtualization accelerates, service providers are expected to advance the way they manage and orchestrate their network services to offer lower latency services to their future users. Modern services require complex data flows between Virtual Network Functions, placed in separate network domains, risking an increase in latency that compromises the offered latency constraints. This shift requires high levels of automation to deal with the scale and load of future networks. In this paper, we formulate the Service Function Chaining (SFC) placement problem and then we tackle it by introducing SCHEMA, a Distributed Reinforcement Learning (RL) algorithm that performs complex SFC orchestration for low latency services. We combine multiple RL agents with a Bidding Mechanism to enable scalability on multi-domain networks. Finally, we use a simulation model to evaluate SCHEMA, and we demonstrate its ability to obtain a 60.54% reduction of average service latency when compared to a centralised RL solution.A collaborative statistical actor-critic learning approach for 6G network slicing control
http://hdl.handle.net/2117/367515
A collaborative statistical actor-critic learning approach for 6G network slicing control
Rezazadeh, Farhad; Chergui, Hatim; Blanco Botana, Luis; Alonso Zárate, Luis Gonzaga; Verikoukis, Christos
Artificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital use-cases. In this paper, we propose a novel model-free deep reinforcement learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC) that enables a scalable and farsighted slice performance management in a 6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC targets the optimization of the latency cost under a long-term statistical service-level agreement (SLA). In particular, we consider the Q-th delay percentile SLA metric and enforce some slice-specific preset constraints on it. Moreover, to implement distributed learners, we propose a developed variant of soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we present numerical results to showcase the gain of the adopted approach on our built OpenAI-based network slicing environment and verify the performance in terms of latency, SLA Q-th percentile, and time efficiency. To the best of our knowledge, this is the first work that studies the feasibility of an AI-driven approach for massive network slicing under statistical SLA.
2022-05-19T06:20:09ZRezazadeh, FarhadChergui, HatimBlanco Botana, LuisAlonso Zárate, Luis GonzagaVerikoukis, ChristosArtificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital use-cases. In this paper, we propose a novel model-free deep reinforcement learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC) that enables a scalable and farsighted slice performance management in a 6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC targets the optimization of the latency cost under a long-term statistical service-level agreement (SLA). In particular, we consider the Q-th delay percentile SLA metric and enforce some slice-specific preset constraints on it. Moreover, to implement distributed learners, we propose a developed variant of soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we present numerical results to showcase the gain of the adopted approach on our built OpenAI-based network slicing environment and verify the performance in terms of latency, SLA Q-th percentile, and time efficiency. To the best of our knowledge, this is the first work that studies the feasibility of an AI-driven approach for massive network slicing under statistical SLA.Energy and cost footprint reduction for 5G and beyond with flexible radio access network
http://hdl.handle.net/2117/365819
Energy and cost footprint reduction for 5G and beyond with flexible radio access network
Guerra Gómez, Rolando; Ruiz Boqué, Sílvia; García Lozano, Mario; Saeed, Umar
This paper focuses on Beyond fifth generation (B5G) non-linear data modeling and decision-making tools to optimize cost reduction versus coverage-QoS trade-off, in other words, the number of active Remote Radio Heads or Units (RRHs) needed according to traffic demands. The cost and energy optimization are analytically expressed by modeling the complex relationships between input and output system parameters using realistic scenarios and traffic profiles for low, medium, and high traffic environments. The optimization tool is based on a multi-objective integer linear programming model, designed to reduce the network cost while maintaining a good coverage-QoS and accounting for capacity constraints, User Equipments (UEs), and different slices. Results at 3.6 and 28 GHz are presented by analyzing and comparing several Cloud Radio Access Network (C-RAN) split options in a heterogeneous deployment with Macro-RRHs (MRRHs) and Small-RRHs (SRRHs). Cost reductions ranging from 30 % to 70 % have been obtained depending on the scenario. This proposal allows mobile network opera
2022-04-13T08:44:58ZGuerra Gómez, RolandoRuiz Boqué, SílviaGarcía Lozano, MarioSaeed, UmarThis paper focuses on Beyond fifth generation (B5G) non-linear data modeling and decision-making tools to optimize cost reduction versus coverage-QoS trade-off, in other words, the number of active Remote Radio Heads or Units (RRHs) needed according to traffic demands. The cost and energy optimization are analytically expressed by modeling the complex relationships between input and output system parameters using realistic scenarios and traffic profiles for low, medium, and high traffic environments. The optimization tool is based on a multi-objective integer linear programming model, designed to reduce the network cost while maintaining a good coverage-QoS and accounting for capacity constraints, User Equipments (UEs), and different slices. Results at 3.6 and 28 GHz are presented by analyzing and comparing several Cloud Radio Access Network (C-RAN) split options in a heterogeneous deployment with Macro-RRHs (MRRHs) and Small-RRHs (SRRHs). Cost reductions ranging from 30 % to 70 % have been obtained depending on the scenario. This proposal allows mobile network operaSpeeding up bluetooth mesh
http://hdl.handle.net/2117/354290
Speeding up bluetooth mesh
Pérez Díaz de Cerio, David; Hernandez Solana, Angela; García Lozano, Mario; Valdovinos Bardají, Antonio; Valenzuela González, José Luis
Bluetooth has constantly evolved from its cradle in 1997 to the last 5.2 version in 2020. With each update and amendment, it has gained in speed, range, and versatility. One of the latest introductions was the Bluetooth Mesh Profile (BMP) making it a technology suitable for a wide variety of applications. Nevertheless, BMP was designed to maintain the compatibility with Bluetooth version 4 devices already deployed in the market. This imposes some restrictions that place Bluetooth Mesh under other competing technologies like Zigbee or Thread in terms of throughput performance. In this paper we propose two mechanisms to overcome these limitations and take advantage of the new extended advertising capabilities introduced with Bluetooth 5. These mechanisms are presented as modifications to the current protocol stack to allow the transmission of larger data structures. Thus, it is possible to boost the throughput of Bluetooth Mesh making it suitable to more demanding applications like, for example, image transmission. The first proposal is designed as an adaptation layer to avoid modifying the standard in its current form. The second makes minimal changes to the frame structure at the different layers enabling the user to accommodate possible encapsulations (i.e., tunneling) without incurring IPv6-layer fragmentation. We have analyzed both solutions and compared them with the current BMP in terms of throughput, delay, and energy consumption for different channel conditions and network size. The results show that except for very small messages or poor channel conditions the proposals improve the throughput and delay of the current BMP.
2021-10-21T15:43:12ZPérez Díaz de Cerio, DavidHernandez Solana, AngelaGarcía Lozano, MarioValdovinos Bardají, AntonioValenzuela González, José LuisBluetooth has constantly evolved from its cradle in 1997 to the last 5.2 version in 2020. With each update and amendment, it has gained in speed, range, and versatility. One of the latest introductions was the Bluetooth Mesh Profile (BMP) making it a technology suitable for a wide variety of applications. Nevertheless, BMP was designed to maintain the compatibility with Bluetooth version 4 devices already deployed in the market. This imposes some restrictions that place Bluetooth Mesh under other competing technologies like Zigbee or Thread in terms of throughput performance. In this paper we propose two mechanisms to overcome these limitations and take advantage of the new extended advertising capabilities introduced with Bluetooth 5. These mechanisms are presented as modifications to the current protocol stack to allow the transmission of larger data structures. Thus, it is possible to boost the throughput of Bluetooth Mesh making it suitable to more demanding applications like, for example, image transmission. The first proposal is designed as an adaptation layer to avoid modifying the standard in its current form. The second makes minimal changes to the frame structure at the different layers enabling the user to accommodate possible encapsulations (i.e., tunneling) without incurring IPv6-layer fragmentation. We have analyzed both solutions and compared them with the current BMP in terms of throughput, delay, and energy consumption for different channel conditions and network size. The results show that except for very small messages or poor channel conditions the proposals improve the throughput and delay of the current BMP.Evaluación de la implantación del aprendizaje basado en proyectos en la EPSC (2001-2003)
http://hdl.handle.net/2117/350331
Evaluación de la implantación del aprendizaje basado en proyectos en la EPSC (2001-2003)
Alcober Segura, Jesús Ángel; Ruiz Boqué, Sílvia; Valero García, Miguel
Aprendizaje basado en problemas o proyectos (a partir de ahora PBL) es el
aprendizaje que se produce como resultado del esfuerzo que realiza el alumno
para resolver un problema o llevar a cabo un proyecto.
Cuando se usa PBL, el punto de partida del proceso de aprendizaje es el
enunciado de un proyecto que los alumnos deben llevar a cabo, normalmente
organizados en grupos (por ejemplo, de 5 alumnos). Cada grupo debe:
1. Identificar qué cosas ya sabe y qué cosas debería aprender el grupo
para abordar el proyecto
2. Establecer y llevar a cabo un plan de aprendizaje
3. Revisar el proyecto a la luz del aprendizaje adquirido y volver a
identificar nuevos aprendizajes necesarios
2021-07-30T13:27:05ZAlcober Segura, Jesús ÁngelRuiz Boqué, SílviaValero García, MiguelAprendizaje basado en problemas o proyectos (a partir de ahora PBL) es el
aprendizaje que se produce como resultado del esfuerzo que realiza el alumno
para resolver un problema o llevar a cabo un proyecto.
Cuando se usa PBL, el punto de partida del proceso de aprendizaje es el
enunciado de un proyecto que los alumnos deben llevar a cabo, normalmente
organizados en grupos (por ejemplo, de 5 alumnos). Cada grupo debe:
1. Identificar qué cosas ya sabe y qué cosas debería aprender el grupo
para abordar el proyecto
2. Establecer y llevar a cabo un plan de aprendizaje
3. Revisar el proyecto a la luz del aprendizaje adquirido y volver a
identificar nuevos aprendizajes necesarios