Core Network Research Articles

Research on high-speed digital optical signal jitter measurement technology based on clock recovery algorithm using eye diagram opening area

With the rapid development of information technology, high-speed digital optical signal transmission technology has become the core of modern communication networks. However, the increase in transmission rates brings challenges such as noise, distortion, and interference, which affect the accuracy of clock recovery. To address these issues, this study proposes a clock recovery algorithm based on the eye diagram opening area to improve the accuracy and efficiency of high-speed digital optical signal jitter measurement. The proposed method extracts clock information from the signal using the opening area and curvature characteristics of the eye diagram for jitter measurement. Experimental results demonstrate that the clock recovery algorithm based on the eye diagram opening area can stably reconstruct the signal eye diagram and obtain jitter parameters under different optical power conditions. At optical powers of −7.2 dBm, −12.2 dBm, and −17.2 dBm, the Q-factors were 8.8, 7.6, and 4.3, respectively, and the RMS jitter values were 12.2 ps, 13.4 ps, and 21.2 ps, respectively. At optical powers of −2.3 dBm, 0.1 dBm, 2.4 dBm, 4.6 dBm, and 6.0 dBm, the Q-factors were 9.1, 9.3, 9.5, 9.7, and 10.0, respectively, and the average jitter values were 8.9 ps, 8.5 ps, 8.0 ps, 7.5 ps, and 7.0 ps. These results indicate that the proposed algorithm performs excellently under low optical power conditions and maintains high recovery accuracy even when jitter increases at higher optical powers. The clock recovery algorithm based on the eye diagram opening area significantly improves the accuracy and stability of high-speed digital optical signal jitter measurement, enriches the theoretical research of clock recovery algorithms, and shows significant advantages in improving signal transmission quality, reducing bit error rate, and enhancing communication link reliability. The research outcomes provide key technical support for the optimization of modern high-speed optical communication systems.

Enhancing 5G network performance through effective resource management with network slicing

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications.

Early Warning of Core Network Capacity in Space-Terrestrial Integrated Networks

Early Warning of Core Network Capacity in Space-Terrestrial Integrated Networks

Designing for the materialization of networked learning spaces

The potential of a Networked Learning (NL) space comes into being when participants establish communication, build connections among each other, and create a dialogic space. Moving from this premise, this article poses a complex question: How to design for the materialization of a NL space? It bases its theoretical framework on Bakhtin’s idea of centrifugal and centripetal forces, the concepts of network core and periphery, as well as the idea of the strength of weak ties. Through these lenses, the article presents a cross-case analysis of two projects aiming at technology education in Denmark. Forces, connections, and movements are exposed and analyzed, showing the importance of unstructured communication processes that nurture a networked community. These results lead to new speculations for future educational design of NL spaces.

DenseNet-Transformer: A deep learning method for spatial–temporal traffic prediction in optical fronthaul network

The rapid evolution of 6G Radio Access Networks (RAN) towards virtualization and intelligence is driven by the widespread adoption of high-bandwidth services and the proliferation of mobile communications. Active Antenna Units (AAU) interface with Distributed Units (DU) over Passive Optical Networks (PON), serving as crucial bearers that transmit extensive data through the optical fronthaul network to the core network. However, the escalating demand for high-bandwidth services, coupled with the uneven spatial–temporal traffic patterns influenced by factors such as geographical location and urban functionalities across various base stations, poses significant challenges to the optical fronthaul network in terms of operational costs and resource allocation efficiency. To tackle these challenges, we introduce the DenseNet-Transformer spatial–temporal traffic prediction algorithm tailored specifically for the traffic characteristics of optical fronthaul networks. In DenseNet-Transformer, DenseNet captures spatial feature correlations among nearby traffic in adjacent regions, facilitating enhanced learning of traffic characteristics across distant areas through dense connections. The Transformer component learns both long and short-term temporal dependencies, enhancing the algorithm’s temporal prediction capabilities using multi-head attention mechanisms and positional encoding. We validate the effectiveness of DenseNet-Transformer through a series of ablation experiments and comparative tests against other algorithms under identical conditions. Experimental results on real datasets demonstrate that, in most scenarios, DenseNet-Transformer outperforms existing algorithms for time traffic prediction in both wireless and optical communication domains, as well as spatial–temporal prediction algorithms.

Seamless group handover authentication protocol for vehicle networks: Services continuity

The advantages of low latency, high reliability and high bandwidth of dense cellular network support real-time sensing, decision making and regulation of vehicular network. Network densification demands smaller coverage areas for base stations that are strategically deployed near consumers to support multiple services. This results in more frequently triggered handover processes. To reduce the latency induced by the handover process and ensure service continuity, this paper proposes a seamless vehicle group handover authentication protocol named SVGHP. The proposed protocol utilizes lightweight algorithms such as quadratic residual and symmetric encryption, and generates and distributes keys for group members based on the Chinese Residual Theorem to achieve handover authentication. Secondly, SVGHP includes a re-authentication phase, specifically, the vehicle group and the subsequent base stations receive the updated keys distributed by the previous RSUs for simplified authentication. And the involvement of the cloud server is avoided, eliminating the network communication delay to the core network. Detailed performance evaluation and comparison with existing protocols demonstrates that the proposed protocol is efficient in terms of computational cost and communication overhead. And in general, our scheme reduces the handover delay by at least 67.91% over the previous authentication schemes. We also provide formal and informal security proofs to prove that our protocol meets common security requirements.

Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System.

This paper presents an innovative approach towards space-ground integrated communication systems by combining terrestrial cellular networks, UAV networks, and satellite networks, leveraging advanced slicing technology. The proposed architecture addresses the challenges posed by future user surges and aims to reduce network overhead effectively. Central to our approach is the introduction of a marginal mobile station (MS)-assisted network resource allocation decision architecture. Building upon this foundation, we introduce the DP-DQN model, an enhanced decision-making algorithm tailored for MSs in dynamic network environments. Furthermore, this study introduces a feedback mechanism to ensure the accuracy and adaptability of the marginalization model over time. Through extensive simulations and experimental validations, our DP-DQN-based edge decision method demonstrates substantial potential in alleviating core network overhead while improving success access ratios compared to conventional methods.

HybridPy: The Simulation Suite for Mesoscopic and Microscopic Traffic Simulations

Mesoscopic, agent-based simulations efficiently model and assess entire regions’ daily activities and travel patterns, exemplified by smaller countries like Switzerland. The queue-based simulation represents a compromise between computational speed on the one hand and the necessity of detailed modeling infrastructure on the other hand. Thus, mesoscopic simulations enable an efficient and reasonably detailed analysis of the complex interplay between supply and demand in mobility research. Conversely, microsimulations excel at reproducing individual speed profiles and behavior by modeling the interactions between traffic participants, including pedestrians, bicycles, and scooters. Although allowing for more detailed system analysis, the downside is the high computational burden, which often prevents large-scale microscopic simulations from running in optimization or calibration loops. hybridPY, an extension of SUMOPy, aims to close the gap and benefit from both environments. The simulation suite allows the running of mesoscopic as well as microscopic traffic simulations based on the core idea: running a microscopic simulation in a smaller dedicated area, using the routes or mobility plans generated from a larger mesoscopic model. The main features of this software are: (i) import, editing and visualization of MATSim and BEAM CORE networks; (ii) conversion of MATSim plans to SUMO routes or plans within the SUMO area; (iii) configuring and running of MATSim simulations. The capability of hybridPY is demonstrated by two applications: the simulation of Schwabing, Germany, based on the MITO MATSim model, and the San Francisco municipality, USA, based on the mesoscopic BEAM CORE model of the entire San Francisco Bay area. Both scenarios demonstrate that the hybrid approach results in significant computational gains with respect to a pure microscopic approach.

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans.

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer.

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods' intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods.

A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users

Due to the emergence of various new applications, such as short videos and online games, higher requirements of their computing and storage capacity are demanded of mobile networks. The traditional cloud computing paradigm has the shortcomings of large latency and high bandwidth demand of the core network. Therefore, how to mine the hotspot distribution of these applications and reasonably configure 5G edge nodes to reduce latency and core network bandwidth are facing great challenges. To address these issues, we designed a placement method for the 5G edge nodes based on mobile hotspots. In this method, we first cluster all locations from the user trajectories to obtain the cluster areas. Further, we extract the features, such as the number of users and duration time in all cluster areas, and extract the hotspots from all cluster areas based on the features of each cluster. Then, we introduce the base station’s high load utilization rate and the core network’s bandwidth reduction rate as the optimization parameters to construct the mathematical model of multi-objective optimization. Finally, we formalize the model into a 0–1 integer programming problem and design a greedy algorithm to solve this model. We also complete a series of experiments to evaluate our proposed methods using the GeoLife dataset. The experimental results show that the high load utilization rate can be increased up to 7.69%, and the bandwidth reduction rate of the core network can be improved up to 6.34%.

Edge2LoRa: Enabling edge computing on long-range wide-area Internet of Things

Long-Power Wide Area Networks (LPWAN) is a low-cost solution to deploy very-large scale Internet of Things (IoT) infrastructures with minimal requirements following a classic producer/consumer model. Inevitably such deployments will require a shift towards low-latency, distributed and collaborative data aggregation models. The cloud edge computing continuum (CECC) has been proposed as an evolution of the traditional central ultra-high-end processing cloud into a continuum of collaborative processing elements distributed from the cloud to the network edge. Until today, incorporating existing centralized and monolithic LPWAN architectures in the CECC faces multiple security-related implications. We propose Edge2LoRa, a complete secure solution to incorporate LPWAN architectures in CECC enabling faster data processing while reducing the transmission of sensitive data. It improves network performance through data pre-processing, traffic flow optimization, and real-time local analysis. Edge2LoRa gradually transform existing LPWAN deployments into agile and versatile infrastructures that enable the seamless and efficient processing of data throughout the CECC while guaranteeing service continuity and full-backwards compatibility. We implement Edge2LoRa in hardware compliant with the Things Stack and the LoRaWAN v1.0.4 and v1.1. We evaluate the performance in terms of networking and computing resource utilization, quality of service and security. The results provide a clear indication of the improvements to public and private LoRaWAN infrastructures without any disruption or service degradation for existing legacy services. In public LoRaWAN deployments where large-scale IoT data streams drive big data analytics, we demonstrate core network bandwidth usage reductions of up to 90% and data processing latency improvements by a ×10 factor.

Broadening the scope: Multiple functional connectivity networks underlying threat conditioning and extinction

Abstract Threat learning processes are thought to be foundational to anxiety and fear-related disorders. However, the study of these processes in the human brain has largely focused on specific brain regions, owing partly to the ease of translating between these regions in human and nonhuman animals. Moving beyond analyzing focal regions of interest to whole-brain dynamics and connectivity during threat learning is essential for understanding the neuropathology of fear-related disorders in humans. In this study, 223 participants completed a 2-day Pavlovian threat conditioning paradigm while undergoing fMRI. Participants completed threat acquisition and extinction. Extinction recall was assessed 48 hours later. Using a data-driven group independent component analysis (ICA), we examined large-scale functional connectivity networks during each phase of threat learning. Connectivity networks were tested to see how they responded to conditioned stimuli during early and late phases of threat acquisition and extinction as well as during early trials of extinction recall. A network overlapping with the default mode network involving hippocampus, ventromedial prefrontal cortex (vmPFC), and posterior cingulate was implicated in threat acquisition and extinction. Another network overlapping with the salience network involving dorsal anterior cingulate cortex (dACC), mPFC, and inferior frontal gyrus was implicated both in threat acquisition and in extinction recall. Other networks overlapping with parts of the salience, somatomotor, visual, and frontoparietal networks were involved in the acquisition or in the extinction of learned threat responses. These findings help support the functional cooperation of specific brain regions during threat learning in a model-free fashion while introducing new findings of spatially independent functional connectivity networks during threat and safety learning. Rather than being a single process in a core network of regions, threat learning involves multiple brain networks operating in parallel performing different functions at different timescales. Understanding the nature and interplay of these dynamics will be critical for comprehensive understanding of the multiple processes that may be at play in the neuropathology of anxiety and fear-related disorders.

Comparing ventriculoatrial and ventriculopleural shunts in pediatric hydrocephalus: a Hydrocephalus Clinical Research Network study.

When the peritoneal cavity cannot serve as the distal shunt terminus, nonperitoneal shunts, typically terminating in the atrium or pleural space, are used. The comparative effectiveness of these two terminus options has not been evaluated. The authors directly compared shunt survival and complication rates for ventriculoatrial (VA) and ventriculopleural (VPl) shunts in a pediatric cohort. The Hydrocephalus Clinical Research Network Core Data Project was used to identify children ≤ 18 years of age who underwent either VA or VPl shunt insertion. The primary outcome was time to shunt failure. Secondary outcomes included distal site complications and frequency of shunt failure at 6, 12, and 24 months. The search criteria yielded 416 children from 14 centers with either a VA (n = 318) or VPl (n = 98) shunt, including those converted from ventriculoperitoneal shunts. Children with VA shunts had a lower median age at insertion (6.1 years vs 12.4 years, p < 0.001). Among those children with VA shunts, a hydrocephalus etiology of intraventricular hemorrhage (IVH) secondary to prematurity comprised a higher proportion (47.0% vs 31.2%) and myelomeningocele comprised a lower proportion (17.8% vs 27.3%) (p = 0.024) compared with those with VPl shunts. At 24 months, there was a higher cumulative number of revisions for VA shunts (48.6% vs 38.9%, p = 0.038). When stratified by patient age at shunt insertion, VA shunts in children < 6 years had the lowest shunt survival rate (p < 0.001, log-rank test). After controlling for age and etiology, multivariable analysis did not find that shunt type (VA vs VPl) was predictive of time to shunt failure. No differences were found in the cumulative frequency of complications (VA 6.0% vs VPl 9.2%, p = 0.257), but there was a higher rate of pneumothorax in the VPl cohort (3.1% vs 0%, p = 0.013). Shunt survival was similar between VA and VPl shunts, although VA shunts are used more often, particularly in younger patients. Children < 6 years with VA shunts appeared to have the shortest shunt survival, which may be a result of the VA group having more cases of IVH secondary to prematurity; however, when age and etiology were included in a multivariable model, shunt location (atrium vs pleural space) was not associated with time to failure. The baseline differences between children treated with a VA versus a VPl shunt likely explain current practice patterns.

Interaction between the 4G - EPC core and the 5G RAN

The Radio Access Network - 5G RAN is designed to work seamlessly with the existing 4G EPC network infrastructure. This enables a smooth transition from fourth 4G to fifth generation 5G mobile systems, both for operators and end users. The radio access network - 5G RAN uses a new core network architecture - 5GC, which is designed to be more flexible and scalable than the previous EPC architecture. The purpose of writing the article is to analyze the methods of interaction between the EPC core of 4G networks and the 5G system core. To explore interfaces and interconnection protocols with an emphasis on roaming and non-roaming architectural models. To discuss the interaction between the 5GC core network and the E-UTRAN radio network associated with the EPC in terms of achieving this goal through non-3GPP access. Analysis of the methods of interaction between the EPC core of 4G networks and the 5GС, the study of interfaces and interaction protocols in different operating environments can be noted as contributions to the development of the article.

AUTHENTICATION OF WIRELESS SYSTEMS BASED ON A DRONE SWARM AS A COMPONENT OF THE 5G RADIO ACCESS NETWORK ARCHITECTURE

Background. When establishing security measures for telecommunication networks involving unmanned aerial vehicles (UAVs), characteristics such as high scalability, device diversity, and high mobility should be considered. Authentication is a fundamental property that allows a UAV network to establish secure communication between its core components. Authentication also protects the UAV network from attackers impersonating legitimate UAVs. UAV authentication can further secure the communication channel by preventing impersonation and replay attacks. The design of UAV access control schemes, such as authorization and authentication mechanisms, remains a challenging research problem in UAV networks. The network becomes even more complicated when it interacts with a multitude of UAVs, called a swarm. A swarm by its very nature has a dynamic structure, and this raises the issue of unreliable constraints on UAVs in its composition. A standardized solution for the authentication of a single drone using the new 5G radio network (NR) is known, but for a swarm of drones, this is an open field of research. Per-UAV authentication key sharing as described in 5G NR does not scale across groups of UAVs. Objective. The purpose of this work is to conduct an analytical review and consider approaches to creating procedures for the authentication of a swarm of UAVs/drones with wireless equipment on board for the 5G NR network, taking into account the features of swarm formation and the very requirements for 5G authentication. Methods. Analysis of factors affecting the quality of provision of telecommunication services using UAVs in fifth generation networks. Analysis of well-known publications dedicated to the implementation of 5G networks and the use of drones in them. Comparing the implementation of UAV authentication procedures with on-board wireless equipment in the 5G network. Results. The widespread use of small UAVs, as well as the large expansion of wireless 5G networks, requires new security measures to prevent unauthorized access to sensitive data. Identification and Authentication for a mobile operator's network using drones allows for secure communication between its main components. This makes it possible to recognize the very drones that participate in the formation of such a network. Drone authentication often protects the communication channel by preventing replay attacks. The development of drone radio access control mechanisms, such as authorization and authentication mechanisms, remain relevant researches for the construction of promising radio access networks involving UAVs. It has been confirmed that the introduction of special group procedures for the authentication of a swarm of drones in the 5G network can significantly improve the quality of the provision of telecommunication services. Conclusions. When working with a swarm of drones, in addition to the usual problems with encryption and authentication (within the swarm and for communication between the swarm and the ground control station), there are additional problems related to the constant change in the composition of the swarm and its hovering position: drones can join or leave a swarm. Depending on the swarm management structure, a different method of authentication will be needed, which makes it difficult to unify such procedures for a swarm of drones. Authentication procedures for a swarm of drones in 5G can be implemented through the following approaches: individual authentication, when each drone as a member of the swarm undergoes authentication with one NR 5G ground station; you can consider such an approach as authentication of a group of IoT devices, if the traffic of the swarm is very limited; group authentication through a leader drone that communicates with swarm members and the 5G operator's network; group distributed authentication through edge drones. Group authentication via a drone leader is presented, where authentication is performed through a mechanism based on distributed delegation to reduce the service traffic directed to the 5G operator's core network. Here, legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the underlying network. Group distributed authentication through boundary drones is considered, which offers more solutions than the case of authentication through a leader drone. Here, a solution is possible for several cases at once, for example, authentication of new drones (entering the swarm or leaving the swarm) and merging two separate drone swarms.

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets.

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Cores in multiway networks

Cores in multiway networks

Enabling IP-optical integration in core and metro networks [Invited

IP-optical (Internet protocol and optical) integration, or “IP over DWDM (dense wavelength division multiplexing),” is a concept spanning over two decades since the advent of coherent DSP (digital signal processing). In recent years, coherent DSP technology has progressed to a point where it can be integrated on small pluggable form factors that can be equipped in optical line systems and disaggregated transponders or directly in IP routers to support a range of metro and core applications. The option of equipping pluggable digital coherent optics (DCOs) directly in IP routers has the biggest potential for operational cost savings but hinges on the availability of a unified management environment with a single pane of glass to coordinate and automate IP routing and optical transport functions. This work presents alternative software-defined networking (SDN) architectures and evaluates the challenges associated with the evolution to IP over DWDM network architectures. It demonstrates the first, to our knowledge, implementation of the Transport API supporting colored pluggable interfaces in routers in a real network testbed. This work contributes to the realization of end-to-end network management for IP-optical networks, offering operators comprehensive visibility into multi-layer and multi-domain services and empowering revenue generation.

Broadcast/multicast delivery integration in B5G/6G environments

This paper describes the design of a Broadcast Core Network (BCN). The BCN is intended to enable the integration of existing terrestrial broadcast systems into a 5G/6G ecosystem. The existing multimedia content distribution architecture in current broadcast networks and the capabilities of 5G access and core networks (5GC) are analyzed to dissect their limitations. We show, inter alia, how the lack of an equivalent data plane makes it impossible to import a 5GC as a solution. We propose a dynamic model that addresses the necessary structure for integrating a BCN in today’s broadcast networks. The paper then considers the evolution of the BCN up to its integration into a 5G/6G cellular network ecosystem. The required architectural components are described. Detailed definitions of the microservices are included, along with the relationships and message calls to enable relevant use cases. Finally, a simulation and lab prototype were developed to validate the proposal. The lab platform is based on multimedia content broadcasting nodes using ATSC 3.0 and a BCN prototype for its control. The results demonstrate the ability of the proposed solution to maximize spectrum utilization and improve the efficiency of current systems.