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The evolution towards 6G will require changes in the way how networks have to be operated to face the stringent service demands while at the same time using efficiently the available resources. In this context, this paper presents the Optimisation-as-a-Service (OaaS) platform being designed by the OPTIMAIX project that makes use of the Network Digital Twin (NDT) concept to test network configurations in a controlled environment. After describing the OaaS platform architecture, the paper presents the optimisation algorithms considered by the project and summarizes the use cases developed in the prototyping activities., This work is funded by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union - NextGenerationEU under projects OPTIMAIX_OaaS (Ref. TSI-063000-2021-34) and OPTIMAIX_NDT (Ref. TSI063000-2021-35)., Peer Reviewed, Postprint (published version)

User mobility prediction can be exploited in cellular networks for different purposes, such as enhancing the handover process, proactive resource allocation, proactive load balancing, etc., in order to improve the network performance. While many works aimed for the prediction of the next cell visited by the User Equipment (UE), the prediction of future UE locations has received less attention. In fact, only a few works deal with the prediction of the next UE location while other few works aim to predict the future direction of UEs arriving at a crossroad. This paper presents a methodology for the prediction of the trajectory followed by the UEs inside the cell. First, UE trajectory patterns are learnt by means of an off-line clustering of historical UE trajectories. Then, the obtained trajectory patterns are used for on-line prediction of the UE trajectory inside the cell, the prediction of the next cell that the UE will visit and an estimation of the time to reach this new cell. A dataset with UE trajectories moving around a large real-life cellular network has been considered., This work is part of VERGE project, which has received funding from the Smart Networks and Services Joint Undertaking under the European Union’s Horizon Europe research and innovation programme under Grant Agreement 101096034. This paper has also been partly funded by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020- 115104RB-I00, Peer Reviewed, Postprint (published version)

The continuous evolution of the communications networks in the race to support 6G services will introduce important operational challenges for network operators, especially in terms of efficient usage of resources, near real-time performance, etc. Such advanced 6G services will necessarily leverage on existing brownfield networks complemented with novel technologies and supportive tools. In this context, the concept of network should be understood in a broad sense, including both networking and computing capabilities enabling the connectivity of the customers to service functions or applications in a very dynamic way. Until recently, the main driver for networks design and operation has been the guarantee of throughput since bandwidth-based services have been dominant so far. With the advent of 5G, delay-based services have started to become relevant as well, as a consequence of the offerings of low latency and interactive services, such as tactile internet or augmented and virtual reality. Now, the advent of forthcoming beyond 5G (B5G) and 6G services is expected to introduce more stringent requirements motivating a new radical change on operations, adding new dimensions to service provision, such as delay-variation or precision-based services. All this complexity triggers the need of evolving the mode of operation by introducing smarter, faster, and educated decisions on the operational processes, pursuing an overall optimization on the usage of both networking and compute resources. In this context, the concept of Network Digital Twins (NDTs) is gaining traction as a perfect complement to the foreseen complex operation., This work is funded by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union - NextGenerationEU under projects OPTIMAIX_OaaS (Ref. TSI-063000-2021-34) and OPTIMAIX_NDT (Ref. TSI063000-2021-35)., Peer Reviewed, Postprint (author's final draft)

Radio Resource Management (RRM) strategies are essential components in mobile wireless networks, such as Long Term Evolution (LTE) or 5G New Radio (NR)[1]. The design of RRM algorithmic solutions is an area of research that has received a lot of attention for decades (see e.g. surveys [2–4] and references therein). Given that RRM strategies need to handle traffic dynamicity in the Radio Access Network (RAN), the availability of realistic user distributions in space and time can be very useful to support a more realistic performance assessment of RRM solutions. For this purpose, this article introduces a dataset containing real measurements of the number of users connected to the different Wifi Access Points (APs) at the Campus Nord facilities of the Universitat Politècnica de Catalunya (UPC) in Barcelona. The Wifi network is composed of 247 APs. To characterize the temporal variations, the data was collected for each AP every 1000 s (approximately) during 62 days. Besides the number of users connected to each AP (including users connected to both 2.4 GHz and 5 GHz bands), the dataset also contains information about the theoretical coverage area of each AP, so that the number of users connected to each AP can be associated to a specific geographical area. In this way, the dataset captures the spatio-temporal variations of users in the Campus at different times of the day, different days of the week and different periods of the academic year., This work is partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/ 10.13039/501100011033 under ARTIST project (ref. PID2020–115104RB-I00). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. The work of Irene Vila has been funded by the European Union-NextGenerationEU, the Spanish Ministry of Universities and the Plan for Recovery, Transformation and Resilience, through the call for Margarita Salas Grants of the Universitat Politecnica de Catalunya (ref. 2022UPC-MSC- 94079)., Peer Reviewed, Postprint (published version)

Relevant services envisaged for beyond 5G (B5G) systems, such as extended reality and holographic communications, have extremely demanding user experience requirements with significant computational and communication demands. While edge computing aims to address the computation requirements by offloading the computational tasks to edge servers near the user, the communication will take advantage of the technologies developed for 5G New Radio jointly with an never-before-seen degree of network densification. This paper proposes the use of relays with edge computing capabilities. The approach's potential for B5G are identified, and a system model is defined to characterize both computational and communications viewpoints. Based on this, results are provided to highlight the gains and limitations of the proposed approach from a system-level perspective. Finally, the main challenges for enabling relays with computing capabilities in B5G deployments are discussed., This work is part of VERGE project, which has received funding from the Smart Networks and Services Joint Undertaking under the European Union’s Horizon Europe research and innovation programme under Grant Agreement 101096034. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This paper has also been partly funded by the Spanish Ministry of Science and Innovation MCIN/AEI/ 10.13039/501100011033 under ARTIST project (ref. PID2020- 115104RB-I00). The work of Irene Vila ` has been funded by the European Union-NextGenerationEU, the Spanish Ministry of Universities and the Plan for Recovery, Transformation and Resilience, through the call for Margarita Salas Grants of the Universitat Politècnica de Catalunya (ref. 2022UPC-MSC- 94079)., Peer Reviewed, Postprint (published version)

High-speed trains (HST) are nowadays more present in our lives currently, some of them can reach speeds up to 500 km/h and futuristic concepts such as hyperloop tunnels could make trains travel at speeds up to 1000 km/h. Dealing with such high speeds arises many communication problems, for example, in mobility management, with many handovers or high Doppler frequency shifts. You might be thinking how it is possible to provide a good QoS to the users inside the train, when traveling at such elevated velocities. In the thesis, we rely on the development of 5G New Radio and the benefits associated, such as a new handover protocol introduced by 3GPP called conditional handover (CHO). By simulating with Simu5G a HST scenario we have proved that CHO can provide a better service to the users by improving the SINR levels and being more efficient than common handover., Los trenes de alta velocidad están cada vez más presentes en nuestro día a día, algunos ya alcanzan velocidades de 500 km/h, mientras que otros conceptos futuristas como los túneles hyperloop podrían hacer que alcanzaran velocidades de hasta 1000 km/h. En el ámbito de las telecomunicaciones, trabajar a tan altas velocidades conlleva algunos problemas, como por ejemplo un elevado número de handovers. Seguramente, os estéis preguntando cómo es posible establecer un servicio que cumpla unos mínimos de calidad para el usuario, cuando este viaja a tan altas velocidades. Para ello, nos hemos apoyado en la tecnología 5G i un nuevo concepto de handover llamado conditional handover (CHO), introducido por el 3GPP. A través del simulador Simu5G, hemos conseguido demostrar que el CHO no solo es un protocolo más eficiente, sino que además conlleva una mejora en los niveles de SINR, en condiciones parecidas a las de un tren de alta velocidad., Els trens d'alta velocitat estan cada vegada més presents en el nostre dia a dia, alguns ja son capaços d'arribar a velocitats pròximes als 500 km/h, mentre que altres conceptes futuristes com els túnels hyperloop podrien fer que els trens arribessin a velocitats de 1000 km/h. En l'àmbit de les comunicacions, treballar amb velocitats tan elevades comporta alguns problemes, com per exemple un ampli número de handovers. Segurament, estareu pensant com es possible establir un servei que compleixi uns mínims de qualitat de cara a l'usuari, al estar treballant amb velocitats tant elevades. Per fer-ho ens hem recolzat en la tecnologia 5G i un nou concepte de handover presentat pel 3GPP, el conditional handover (CHO). Simulant a través de Simu5G un escenari similar al d'un tren d'alta velocitat, hem pogut demostrar que el CHO no es només un protocol més eficient que el handover normal, sinó que a més a més millora els nivells de SINR.

A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in the real network, as well as for predicting the performance of such algorithms under different conditions. While an NDT can be defined for the different subsystems of the network, this paper proposes an NDT architecture focusing on the Radio Access Network (RAN), describing the components to represent and model the operation of the different RAN elements, and to perform emulations. Different application use cases are identified, and among them, the paper puts the focus on the training of Reinforcement Learning (RL) solutions for the RAN. For this use case, the paper introduces a framework aligned with O-RAN specifications and discusses the functionalities needed to integrate the NDT. This use case is illustrated with the description of a RAN NDT implementation used for training an RL-based capacity-sharing solution for network slicing. Presented results demonstrate that the implemented RAN NDT is a suitable platform to successfully train the RL solution, achieving service-level agreement satisfaction values above 85%., Peer Reviewed, Postprint (published version)

Capacity sharing in RAN slicing, The 5G network slicing feature allows the creation of a multiple of logical networks, known as "network slices", on top of a shared physical infrastructure. Each network slice is customized to specific service requirements and operates in an separate manner from other slices. This technology also is difficult to apply in Radio Access Networks (RANs), since it demands efficient radio resource management while meeting specific needs for each created RAN slices. The first part of this thesis provides an overview of the DRL technology and its application to RAN Management challenges. This will help to develop advanced solutions for capacity sharing in 5G networks. Then, a DRL-based solution is evaluated for multi-tenant scenarios within 5G RAN. The solution involves a Multi-Agent Reinforcement Learning (MARL) approach with Deep Q-Network (DQN) agents. DQN's MARL-based solution is designed to address capacity-sharing challenges within multi-cell networks, allowing users to adjust to changing traffic conditions while maintaining Service Level Agreements (SLA) for each network segment. To this end, the solution is evaluated in four different scenarios and its strengths, weaknesses and opportunities for improvement are examined. Collab platform is used as a tool to test the potential of this tool as an education tool.

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., This work is funded by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union - NextGenerationEU under projects OPTIMAIX_OaaS (Ref. TSI-063000-2021-34) and OPTIMAIX_NDT (Ref. TSI063000-2021-35)., Peer Reviewed, Postprint (published version)

This paper addresses the problem of optimally using the relay capabilities of user equipment (UE) to augment the radio access network (RAN) in 5G deployments and beyond. This can be particularly useful in coverage constrained scenarios, such as those using millimeter waves, due to the difficulty radio signals penetrate some structures. This can lead to signal blockages and high penetration losses when providing outdoor-to-indoor coverage. To overcome these limitations, the use of relay UEs (RUEs) is seen as a possible solution to effectively extend the coverage of a cellular network. In this context, this paper proposes a deep learning-based algorithm to optimize the decision regarding when RUEs should be activated and deactivated in accordance with the benefits they can provide for increasing the spectral efficiency and decreasing outage probability for the network users. The obtained results reveal a promising capability of the proposed solution to activate the most beneficial RUEs given the network conditions being experienced, leading to improvements of average spectral efficiency of 12.3% and reductions of outage probability of 89% with respect to the case without relays., FUNDING: This work was supported in part by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and in part by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). The work of J.J. Hernández-Carlón was supported by the Spanish Ministry of Science and Innovation under grant ref. PRE2018-084691. The work of I. Vilà was supported by the European Union-NextGenerationEU, Spanish Ministry of Universities and the Plan for Recovery, Peer Reviewed, Postprint (author's final draft)

This paper proposes a model to detect and solve coverage holes in 5G Radio Access Network (RAN) deployments operating with millimeter waves. The proposed model utilizes a DBSCAN-based detector to identify coverage constrained areas and then proposes the use of Relay User Equipment (RUE) capabilities to extend the RAN coverage. To optimize the activation and deactivation of RUEs, a Deep-Q-Network-based algorithm is proposed, aiming to improve spectral efficiency and decrease the outage probability experienced by network users. The obtained results demonstrate the effectiveness of the model in accurately detecting coverage constrained areas and efficiently solving these issues by means of an effective RUE activation, leading to significant improvements in network performance while minimizing the time that RUEs remain in active mode, which implies potential benefits for MNOs and UE holders and significant energy savings., This paper is part of ARTIST project (ref. PID2020- 115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033. The work is also funded by the Spanish Ministry of Science and Innovation under grant ref. PRE2018-084691. The work of Irene Vilà has also been funded by European Union-NextGenerationEU, Spanish Ministry of Universities and the Plan for Recovery, Transformation and Resilience, through the call for Margarita Salas Grants of the Universitat Politècnica de Catalunya (ref. 2022UPC-MSC- 94079), Peer Reviewed, Postprint (published version)

One of the applications where fifth generation (5G) networks are expected to have a greatest impact is vehicular-to-everything (V2X) communications. The exchange of data among different vehicles on the network will make roads a safer environment. However, the massive usage of V2X communications leads to an increase of data traffic and resources consumption. Moreover, the mobility associated to vehicles may drain the available resources in particular cells, compromising the required quality of service (QoS) of connected users. To avoid these situations, the introduction of machine learning algorithms to perform mobility load balancing between neighboring cells arises as a promising tool to ensure to all connected vehicles their demanded resources. In this paper, we address the cell overload problem by proposing an O-RAN compliant Q-learning algorithm that dynamically adapts the handover offset between two neighboring cells to mitigate a network overload situation. The algorithm performance is assessed using realistic vehicular traces. Results show that the network overload appearing during rush hour can be successfully mitigated and the load is fairly distributed between cells., This research was partially supported by the Spanish Centre for the Development of Industrial Technology and the Ministry of Economy, Industry and Competitiveness under grant/project CER-20191015 / Open, Virtualized Technology Demonstrators for Smart Networks (Open-VERSO) and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union – Next Generation EU, in the framework of the Recovery Plan, Transformation and Resilience (Call UNICO I+D 5G 2021, ref. number TSI063000-2021-29)., Peer Reviewed, Postprint (author's final draft)

With the fast development of Quantum Computers and a better understanding of the quantum realm, Quantum Communications (QC) promises to be one of the main research areas of the upcoming decade. Over the last several years, tremendous effort and progress have already been made in this sense, but until now, QC have primarily been considered at optical frequencies, namely the Near-Infrared (NIR) and visible regions (60-800 THz). This thesis proposes, for the first time, the realization of QC in the terahertz (THz) band (0.1-10 THz), which is expected to become one of the most used bands in the near- immediate future. During the analysis, different possible implementations are considered and discussed. Also, the shape a THz Wireless Quantum channel would take and the main challenges this channel would impose are thoroughly analyzed, considering the wireless channel usually used in quantum optical systems. This thesis is aimed at setting a bench- mark to address the unexplored topic of THz Quantum Communications, enabling future researchers to enter this field and making their work easier., Con el rápido desarrollo de la Computación Cuántica y un mejor conocimiento del mundo cuántico, el campo de las Comunicaciones Cuánticas (QC) promete ser una de las principales áreas de investigación de la próxima década. Durante los últimos años se han realizado considerables esfuerzos y progresos en este campo, pero hasta ahora las QC se han planteado solo para frecuencias ópticas, es decir, las regiones cercanas al infrarojo (NIR) y al visible (60-800 THz). Esta tesis propone, por primera vez, la realización de QC en la banda de terahertz (THz), comprendida entre 0.1 y 10 THz, la cual se espera que sea una de las bandas más utilizadas en el futuro cercano. Durante el análisis se tienen en cuenta y se discuten diferentes posibles implementaciones. También la forma que tendrá el Canal Cuántico Radio a THz y los principales obstáculos que se deberán tener en cuenta, son debidamente analizados partiendo del canal radio considerado en sistemas ópticos. Esta tesis está enfocada en establecer una referencia en el poco explorado campo de las QC a THz, permitiendo a futuros investigadores adentrarse en este campo y facilitarles su trabajo., Amb el ràpid desenvolupament de la Computació Quàntica i un millor coneixement del món quàntic, el camp de les Comunicacions Quàntiques (QC) promet ser una de les principals àrees de recerca de la propera dècada. Durant els darrers anys s'han realitzat considerables esforços i progressos en aquest camp, però fins ara les QC han estat plantejades només per a freqüències òptiques, és a dir, les regions properes a l'infraroig (NIR) i el visible (60-800 THz). Aquesta tesis proposa, per primer cop, la realització de QC a la banda de terahertz (THz), compresa entre 0.1 i 10 THz, la qual s'espera que esdevingui una de les bandes més utilitzades en el futur proper. Durant l'anàlisi es tenen en compte i es discuteixen diferents possibles implementacions. També la forma que obtindria el Canal Quàntic Ràdio a THz, i els principals obstacles que s'haurien de tenir en compte són degudament analitzats, partint del canal ràdio considerat per a sistemes òptics. Aquesta tesis està enfocada a establir una referència en el camp poc explorat de les QC a THz, permetent a futurs investigadors endinsar-se en aquest camp i facilitzar la seva feina.

Relevant services envisaged for beyond 5G (B5G) systems, such as extended reality and holographic communications, present stringent user experience requirements with high computational and communication demands. While edge computing aims to address the computation requirements by offloading the computational tasks to edge servers close to the user, the communication will leverage the technologies developed for 5G New Radio together with an unprecedented level of network densification. This paper advocates for deploying relays equipped with edge computing capabilities. The potentials of this approach for B5G are identified and a system model is presented to characterize both computational and communications perspectives. Based on this, results are provided to show the benefits and limitations of the proposed approach from a system-level perspective., This work is part of VERGE project, which has received funding from the Smart Networks and Services Joint Undertaking under the European Union's Horizon Europe research and innovation programme under Grant Agreement 101096034. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This paper has also been partly funded by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). The work of Irene Vilà has been funded by European UnionNextGenerationEU, Spanish Ministry of Universities and the Plan for Recovery, Transformation and Resilience, through the call for Margarita Salas Grants of the Universitat Politècnica de Catalunya (ref. 2022UPC-MSC- 94079)., Peer Reviewed, Postprint (author's final draft)

Beyond 5G (B5G) communication networks face the challenge of meeting the demanding requirements of various service types, including uRLLC, mIoT, eMBB, and emerging technologies like Extended Reality (XR). Edge computing can address these demands effectively because of the ability to bring computational power and resources closer to the source of data. Nevertheless, the realization of this potential necessitates an open, flexible, and automated architectural framework capable of supporting disaggregated applications and network designs. In this context, this paper introduces a novel architecture designed to advance the evolution of edge computing in B5G, developed within the EU-funded project VERGE. The proposed architecture is modular and scalable, guided by artificial intelligence (AI), and founded on three essential pillars: "edge for AI," "AI for edge," and "security, privacy, and trustworthiness for AI." After presenting this architecture, the paper showcases its applicability through the examination of two vertical use cases within the industrial and transportation domains., Project VERGE has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101096034. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. UK participants in VERGE are supported by UKRI grant numbers 10071211 (Samsung Electronics (UK) Limited) and 10061781 (King’s College London), Peer Reviewed, Postprint (author's final draft)

The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is use, This work has been partially funded by Huawei Technologies. Work by X. Gelabert and B. Klaiqi is partially funded by the European Union's Horizon Europe research and innovation programme (HORIZON-MSCA-2021-DN-0) under the Marie Skłodowska-Curie grant agreement No 101073265. Work by J. Perez-Romero and O. Sallent is also partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). This last project has also funded the work by D. Campoy., Peer Reviewed, Postprint (author's final draft)

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., VERGE has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101096034., Peer Reviewed, Postprint (author's final draft)

This paper presents a technical overview of BeGREEN project, a Horizon Europe, Smart Networks and Services Joint-Undertaking (SNS-JU) Phase 1 project kicked off on January 1, 2023 [1]. This paper is intended to describe BeGREEN's technical scope and objectives. These objectives aim at improving energy efficiency of the beyond 5G (B5G) networks. BeGREEN technical agenda includes analysis of the combined energy and spectrum efficiency of the B5G networks, based on massive multiple-input-multiple-output (mMIMO) scenarios. The project proposes a novel architecture that includes several innovative solutions. An offloading engine is used for hardware acceleration that is a solution for compute-heavy physical layer processing in 5G new radio (5G NR) mMIMO and beyond to improve the processing performance and energy efficiency. The architecture also includes joint communication and sensing (JCAS) for improving energy efficiency of the physical layer functions by, e.g., efficient beam-search and beam tracking, and uses reconfigurable intelligent surfaces (RIS) as an enabler for JCAS. BeGreenproposes an artificial intelligence (AI)-assisted energy-aware “Intelligent Plane” as an additional plane along with user plane and data plane, that allows the data, model, and inference to be seamlessly exchanged between network functions. The project also proposes an AI Engine that is consist of an execution environment that can host AI models and will manage their lifecycle and access to data., This work is supported by the European Commission’s Horizon Europe, Smart Networks and Services Joint Undertaking, research and innovation program under grant agreement #101097083, BeGREEN project., Peer Reviewed, Postprint (author's final draft)