Network Operators Research Articles

GSNR-aware resource re-provisioning for C to C+L-bands upgrade in optical backbone networks

Efficient network management in optical backbone networks is essential to manage continuous traffic growth. To accommodate this growth, network operators need to upgrade their infrastructure at appropriate times. Given the cost constraint of upgrading the entire network at once, upgrading the network periodically in multiple batches is a more pragmatic approach to meet the growing demands. While multi-period, batch-upgrade strategies to increase network capacity from the conventional C band to C+L bands have been proposed, they did not consider so far the possibility to re-provision existing traffic. In this work, we investigate how to selectively re-provision connections from C band to L band during a batch upgrade. This is to ensure greater availability of C-band resources which can help to delay network upgrade and hence reduce upgrade cost, while limiting the number of disrupted connections in the network. This study proposes two re-provisioning strategies, namely, Budget-Based (BB) and Margin-Aware (MA) re-provisioning, which rely on the Quality of Transmission (QoT) of lightpaths. These strategies leverage the knowledge of Generalized Signal-to-Noise Ratio (GSNR) to choose which lightpaths to re-provision. We compare these strategies with a baseline distance-based strategy that uses path length to select and re-provision lightpaths. We also incorporate Machine Learning techniques for QoT estimation of lightpaths to reduce the computational time required for optical-path feasibility check. Numerical results show that, compared to distance-based strategy, BB and MA strategies reduce disruption by about 22% and 27%, respectively, in representative network topologies.

Robust economic-resilient operational planning of distribution networks considering multi-microgrids black-start service

The increasing growth of natural disasters and cyber-physical attacks has made the development of resilience enhancement strategies for distribution networks a significant challenge for operators. Hence, in this paper, a black-start strategy based on multi-microgrids capability is developed as a two-layer preventive-corrective measure within a cost-effective energy management framework. The preventive layer includes regulating bilateral contracts between the active distribution network operator and the multi-microgrids owner pre-event. The corrective layer includes islanding microgrids during the event, calling the responsive loads, and economic-resilient operational planning of the active distribution network post-event. In this regard, a hybrid robust multi-objective optimization approach is applied to model the proposed black-start strategy as a risk-averse portfolio. The effectiveness of the developed model is tested on the modified IEEE 33-bus distribution network. Results such as an 83.38 % load recovery of the active distribution network, a 65.78 % reduction in distribution system operator costs, and a 131.28 % increase in multi-microgrid owner profit clearly illustrate the enhanced resilience of the active distribution network within an economically robust framework, which indicates the effectiveness of the proposed strategy.

Data-driven modeling of unsteady flow based on deep operator network

Time-dependent flow fields are typically generated by a computational fluid dynamics method, which is an extremely time-consuming process. However, the latent relationship between the flow fields is governed by the Navier–Stokes equations and can be described by an operator. We therefore train a deep operator network (DeepONet) to learn the temporal evolution between flow snapshots. Once properly trained, given a few consecutive snapshots as input, the network has a great potential to generate the next snapshot accurately and quickly. Using the output as a new input, the network iterates the process, generating a series of successive snapshots with little wall time. Specifically, we consider two-dimensional flow around a circular cylinder at Reynolds number 1000 and prepare a set of high-fidelity data using a high-order spectral/hp element method as ground truth. Although the flow fields are periodic, there are many small-scale features in the wake flow that are difficult to generate accurately. Furthermore, any discrepancy between the prediction and the ground truth for the first snapshots can easily accumulate during the iterative process, which eventually amplifies the overall deviations. Therefore, we propose two alternative techniques to improve the training of DeepONet. The first one enhances the feature extraction of the network by harnessing the “multi-head non-local block.” The second one refines the network parameters by leveraging the local smooth optimization technique. Both techniques prove to be highly effective in reducing the cumulative errors, and our results outperform those of the dynamic mode decomposition method.

Machine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis

The number of satellites in orbit around Earth is increasing rapidly, with the risk of collision rising accordingly. Trends of the global population of satellites need to be analyzed to test the viability and impact of proposed rules and laws affecting the satellite population and collision avoidance strategies. This requires large scale simulations of satellites that are propagated on long timescales to compute the large amounts of actionable close encounters (called conjunctions), which could lead to collisions. Rigorously checking for conjunctions by computing future states of orbits is computationally expensive due to the large amount of objects involved and conjunction filters are thus used to remove non-conjuncting orbit pairs from the list of possible conjunctions. In this work, we explore the possibility of machine learning (ML) based conjunction filters using several algorithms such as eXtreme Gradient Boosting, TabNet and (physics-informed) neural networks and deep operator networks. To show the viability and the potential of ML based filters, these algorithms are trained to predict the future state of orbits. For the physics-informed approaches, multiple partial differential equations are set up using the Kepler equation as a basis. The empirical results demonstrate that physics-informed deep operator networks are capable of predicting the future state of orbits using these equations (RMSE: 0.136) and outperform eXtreme Gradient Boosting (RMSE: 0.568) and TabNet (RMSE: 0.459). We also propose a filter based on the trained deep operator network which is shown to outperforms the filter capability of the commonly used perigee-apogee test and the orbit path filter on a synthetic dataset, while being on average 3.2 times faster to compute than a rigorous conjunction check.

AlarmGPT: an intelligent alarm analyzer for optical networks using a generative pre-trained transformer

The proliferating development of optical networks has broadened the network scope and caused a corresponding rise in equipment deployment. This growth potentially results in a significant number of alarms in the case of equipment malfunctions or broken fiber. Managing these alarms efficiently and accurately has always been a critical concern within the research and industry community. The alarm processing workflow typically includes filtration, analysis, and diagnostic stages. In current optical networks, these procedures are often performed by experienced engineers, utilizing their expert knowledge and extensive experience. This method requires considerable human resources and time, as well as demanding proficiency prerequisites. To address this issue, we propose an intelligent alarm analysis assistant, “AlarmGPT,” for optical networks, utilizing a generative pre-trained transformer (GPT) and LangChain. The proposed AlarmGPT exhibits a high level of semantic comprehension and contextual awareness of alarm data, significantly enhancing the model’s ability of interpreting, classifying, and solving alarm events. Through verification of extensive alarm data collected from real optical transport networks (OTNs), the usability of AlarmGPT has been validated in the tasks of alarm knowledge Q&A, alarm compression, alarm priority analysis, and alarm diagnosis. This method has the potential to significantly reduce the labor and time required for alarm processing, while also lowering the experiential requisites incumbent upon network operators.

Techno-economic modeling of a preventive-corrective strategy based on black-start services to resilience enhancement of distribution networks

High-impact with low-probability (HILP) events such as natural disasters or cyber-physical attacks severely affect power systems. The distribution networks experience significant challenges during HILP events. So, strategies need to be developed to enhance distribution networks' resilience. Hence, in this paper, a resilience enhancement strategy based on the potential of multi-microgrids is developed. The proposed strategy has a two-layered structure consisting of preventive and corrective measures within the hierarchical centralized energy management framework. The preventive measures are based on the black-start bilateral contract between the active distribution network operator and the multi-microgrids owner. The corrective measures are based on islanding microgrids during the event. The LP-Metric-based multi-objective optimization approach is employed for economic-resilient operational planning of the active distribution network. Moreover, a scenario-based stochastic approach is utilized to deal with renewable resources and load uncertainties. On the modified IEEE 33-bus active distribution network, the effectiveness of the proposed strategy is evaluated using the supplied load as a resilience index. The proposed black-start strategy has resulted in an increase of 181.57 % in the supplied load of the distribution network, while a reduction of 83.12 % in its curtailed load has been achieved. Consequently, the resilience index increased by 84 %. Additionally, the distribution system operation cost was reduced by 87.44 %, while the multi-microgrid owner's profit increased by 1004.68 %. The obtained results demonstrate that distribution system resilience has been enhanced within an economic framework, which validates the effectiveness of the proposed strategy.

LoRaWAN: The dilemma of optimal transmission parameter

Low-Power Wide-Area Network (LPWAN) technologies, particularly LoRaWAN, have gained significant attention for their ability to facilitate long-range communication with low power consumption, making them ideal for Internet of Things (IoT) applications. However, achieving optimal performance in LoRaWAN systems requires careful selection of transmission parameters such as spreading factor, bandwidth, and coding rate. This paper addresses the dilemma faced by network designers and operators in determining the optimal transmission parameters for LoRaWAN deployments. We provide an overview of the key parameters and their impact on network performance, considering factors such as latency, packet delivery rate, and energy efficiency. Furthermore, we discuss the trade-offs involved in parameter selection. Through simulations, we evaluate the performance of different parameter configurations under various network conditions and provide insights into achieving the best balance between coverage, capacity, and energy consumption in LoRaWAN deployments. Findings from the study established that using high coding rate and bandwidth only provides minimal improvement in packet delivery rate particularly at large distances but at the cost energy consumption while spreading factor remains the most important transmission parameter in LoRa networks.

Mobile Network Subscribers Market Share Evolution Of Telecommunication Companies In Senegal From 1996 To 2022

This research examines the evolution of market shares of telecommunications companies in Senegal over a period of 26 years, from 1996 to 2022. The study focuses on subscriber’s market share as a key indicator of mobile network operators' performance and competitiveness. It analyzes the mix market share for the three main mobile network operators (ORANGE, FREE, and EXPRESSO) consolidation periods, key success factors, and challenges faced by these companies in a dynamic and competitive environment.The analysis reveals a significant evolution in the telecommunications landscape in Senegal, marked by the emergence of new players, the advancement of technologies, the diversification of offerings, and the increase in connectivity. Major historical operators had to contend with growing competition, stimulating innovation and service improvement to meet changing consumer needs.Key events such as the introduction of 3G and 4G technology, had a significant impact on market share distribution and companies' differentiation strategies. Regulatory policies such as mobile telephony licensing, investments in infrastructure, brand changes, order entry, strategic partnerships, and operators’ number of years of experience also influenced the sector's competitive dynamics.In conclusion, this research highlights the complex and dynamic evolution of market shares of telecommunications companies in Senegal, highlighting the technological, strategic, and regulatory factors that have shaped this sector over the years.

Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests

Superelastic shape memory alloy (SMA) wires and rods possess unique deformation and energy dissipation capabilities. For the assessment of their stress response, commonly cyclic tensile tests are conducted. An important but subtle parameter in this procedure is the martensite evolution. In scenarios where conducting thermal experiments is impractical, inverse modeling from cyclic tests serves as a viable alternative. However, employing constitutive models in this process presents distinct challenges, such as parameter identification and calibration, or numerical stability issues. To address these challenges, this paper proposes a data-driven method based on a physics-informed deep operator network (DeepONet) to estimate the martensite evolution. Constraint with a stress equation, the network requires only strain–stress data for training and provides stress responses in addition to the martensite evolution. From the training data, the network learns to consider the effects included in the response. The DeepONet can be coupled with experiments to provide online estimates from noisy sensor-based strain inputs, while remaining numerically stable. Moreover, this approach avoids the need for separate parameter identification or calibration. This paper details this method and evaluates its performance through experiments conducted on superelastic SMA wires. Furthermore, as an alternative approach, training using a constitutive model is provided.

Enhancing privacy and security in 5G networks with an anonymous handover protocol based on Blockchain and Zero Knowledge Proof

The latest advancement in cellular technology, 5G, has the ability to facilitate various applications such as Device-to-Device (D2D) communication, vehicular communications, and the Internet of Everything (IoE). However, the technology is faced with challenges related to user privacy, resource management, handover authentication, and security of the air interface and internet channels. Moreover, the use of specialized base stations such as wireless Access Points (AP) and Road-Side Units (RSU) in vehicular communications may be owned by subscribers outside formal network operators. The conventional handover authentication schemes have a lengthy latency period that conflicts with the 5G quality of service. To tackle these challenges, this paper presents an anonymous scheme that utilizes Blockchain and Zero Knowledge Proof (ZKP) to ensure efficient privacy in the handover protocol of 5G Networks. The proposed scheme has been thoroughly evaluated for security and privacy and has shown to be resistant to identity catching, location area tracing, and replay attacks while achieving Forward/Backward secrecy. Furthermore, the performance evaluation of the proposed scheme indicates that it is efficient and speedy as it involves a limited number of messages, low computation, and does not necessitate the involvement of the Home Network (HN) in the authentication or handover protocols. A diligently crafted BAHO authentication protocol, following cryptographic principles, guarantees robust security against the Real-or-Random model. This results in a seamless and secure transition for User Equipment, upholding confidentiality, integrity, and authenticity during handovers between Access Points.

Dynamic Coalition Formation among IoT Service Providers: A Systematic Exploration of IoT Dynamics Using an Agent-Based Model.

This paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem's complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm's performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem's dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration.

Enhancement of technical, economic & environmental benefits in multi-point PV & wind-based DG integrated radial distribution network using Aquila optimizer

Due to a larger R/X ratio, active power losses are higher in distribution networks (DNs) than in transmission networks. Due to the ever-increasing load demand, fossil fuel-based natural resources are becoming depleted, which is of great concern to network operators. Therefore, renewable energy sources (RES) are an effective solution to the growing energy issue and environmental pollution. The intermittent natures of PV and wind are characterized using probability density functions (PDFs). This study compares how well a radial distribution network performs when maximizing a constrained multi-objective function comprising voltage profile, economy, and emission benefit indices by optimal sizing and siting of distributed generators (DGs) using the Aquila optimizer (AO) algorithm. DGs based on photovoltaic / wind energy are injected at single, double, and triple points of standard IEEE 33, 69, 85, and 141 bus test systems. A statistical analysis of the results obtained by AO and some effective algorithms has been performed. The uniqueness of the work comes from the lack of prior literature reporting on simultaneous evaluations of technical, economic, and environmental consequences during optimal DG sizing and sitting. The maximum improvement ratios of the overall objective observed are 15.37, 15.69, 23.82, and 14.99 for the IEEE 33, 69, 85, and 141 bus DNs, respectively, concerning the base objective value. The active power losses corresponding to the best objective function of IEEE 33, 69, 85, and 141 bus DNs are reduced by 45.09 %, 43.24 %, 37.53 %, and 4.17 %, respectively.

Validation of mobile phone use recall in the multinational MOBI-kids study.

Potential differential and non-differential recall error in mobile phone use (MPU) in the multinational MOBI-Kids case-control study were evaluated. We compared self-reported MPU with network operator billing record data up to 3 months, 1 year, and 2 years before the interview date from 702 subjects aged between 10 and 24 years in eight countries. Spearman rank correlations, Kappa coefficients and geometric mean ratios (GMRs) were used. No material differences in MPU recall estimates between cases and controls were observed. The Spearman rank correlation coefficients between self-reported and recorded MPU in the most recent 3 months were 0.57 and 0.59 for call number and for call duration, respectively. The number of calls was on average underestimated by the participants (GMR = 0.69), while the duration of calls was overestimated (GMR = 1.59). Country, years since start of using a mobile phone, age at time of interview, and sex did not appear to influence recall accuracy for either call number or call duration. A trend in recall error was seen with level of self-reported MPU, with underestimation of use at lower levels and overestimation of use at higher levels for both number and duration of calls. Although both systematic and random errors in self-reported MPU among participants were observed, there was no evidence of differential recall error between cases and controls. Nonetheless, these sources of exposure measurement error warrant consideration in interpretation of the MOBI-Kids case-control study results on the association between children's use of mobile phones and potential brain cancer risk.

Economic Alternatives for the Provision of URLLC and eMBB Services Over a 5G Network

This research work analyzes economic alternatives for the provision of ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) services by mobile network operators over the same fifth-generation (5G) network. Two business models are proposed to provide the two services to end users. Concretely, a monopoly is a single operator who offers both services, and a duopoly is two different operators that share network resources and offer one service each. In addition, two types of network scenarios for resource sharing are studied. Specifically, a shared network (SN) is a type of network scenario allowing resources to be shared between the two services without priority. A differentiated network (DN) is a type of network scenario that allows resources to be shared between the two services with a priority to URLLC service using network slicing (NS). Regarding the economic aspects, the incentive is modeled through the user’s utility and the operator’s benefit. At the same time, game theory is used to model the strategic interaction between users and operators, and queuing theory is used to model the interaction between the two services. We conclude that the monopoly social welfare (SW) is closer to the SW of the social optimum than the duopoly SW. In addition, the DN scenario to offer the services through NS is more suitable than the SN scenario since the point of view of service prices, user utilities, and operator benefit.

A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks.

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future.

Integration of computer networks and artificial neural networks for an AI-based network operator

This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outline the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.

Experimental test results of an automatic voltage regulator with independent phase voltage controller

The growing number of distributed renewable energy sources and dynamic constant-power loads (e.g. electric vehicle charging stations) pose new challenges for network operators. These changes result in alterations to network load profiles and load flows, leading to greater voltage volatility. One effective solution to these problems can be the use of automatic voltage regulators (AVRs), which stabilize and symmetrize voltage output, whether at distribution transformers (DTs) or elsewhere in the distribution network. The device developed by the authors consists of two bidirectional power converters and three single-phase transformers connected in series to the low-voltage grid as a stabilizer. The proposed control system provides accurate and fast regulation of the AVR’s output voltage (within the range of ±10% of the nominal grid voltage), with each phase being independently adjusted, regardless of the type of power load. The article includes test results demonstrating selected functionalities of the developed AVR. The physical model of the device discussed in the article is a research component of the LINTE2 laboratory of the Gdansk University of Technology.

Mobile Data Network Performance Review – A Perspective of Zambia

High speed mobile internet results in better user experience and satisfied subscriber. However factors that affect the download speed include transfer technology, number of subscribers sharing the connection, device capability and radio conditions at the subscriber location. This research was conducted to review the mobile internet performance in Zambia and determine its conformity to the regulator, ZICTA, QoS regulation. Two sets of empirical data were collected in the capital city, first in 2021 during the covid pandemic period and second in 2024. The data was compared to check for network operator developments. The results reveal that two MNOs acquired new 2600MHz spectrum, however only one MNO drew maximum benefit resulting in improved 4G download speeds. This improvement was despite an increase in market share over the same period. The second operator that deployed the 2600MHz LTE TDD mode spectrum recorded a degradation in overall downloads speeds when compared to the initial data set in spite of a reduction in market share over the same period. Thus the steps to improve the LTE network are to add more spectrum to at least reach the 3GPP Rel 10 standard of 5 carrier configuration (5CC) to reach a total 100MHz and configure network features like Carrier Aggregation (CA) to provide for higher throughputs for capable devices. Other ways to expand LTE network include add more antennas to allow for higher spectral efficiency using 8 x 8 MIMO or more and thirdly deploy Small cells solution to enhance coverage and capacity in both indoor and outdoor environment.

Deep operator networks for bioheat transfer problems with parameterized laser source functions

To numerically characterize optothermal interactions at the laser-tissue interface during tissue imaging or surgery, the bioheat transfer partial differential equation (PDE) needs to be solved with a laser excitation source function. Considering various possible experimental scenarios, the PDEs become parametric and need to be solved for several optical and laser parameters. Classical numerical solvers that are used for performing such simulations are computationally expensive and time consuming. In this work, we use the recent deep operator network (DeepONet) framework for learning the mapping between infinite dimensional function spaces of the parameterized laser source functions and the solutions of the parameterized bioheat transfer PDE. We propose a new neural network architecture, Res-DeepONet, for training a purely physics-informed DeepONet with laser heating functions comprising of Gaussian beam profiles. We also showcase the versatility and robustness of our network architecture within purely data-driven and hybrid training frameworks for applications where labeled ground truth data may be accessible. The proposed neural network architecture exhibits excellent generalization performance on unseen Gaussian source functions with 10× higher accuracy when compared to traditional feedforward network architectures. Furthermore, zero-shot performance studies highlight the exceptional extrapolation capabilities of our network with 50× higher accuracy compared to traditional DeepONet architectures at inference times that are two orders of magnitude faster than classical solvers.

Decomposing total power flows into contributions originating from power sources in loads, generators, and network elements

To thoroughly analyze the conditions in power lines, transformers or loads, it is essential to have knowledge of their active and reactive power flow contributions from each power source. Understanding these contributions allows for a detailed analysis of the impact of active and reactive power flow contribution from each power source on active and reactive power losses in each network element, as well as the determination of power participation of each source to every load. This paper provides the necessary equations and an adequate procedure for calculating power flow contributions. The method is suitable for meshed network and considers shunt elements, avoiding problematic simplifications. Furthermore, it ensures impartiality towards any network user, making it well-suited for application in deregulated environments. By offering valuable information useful, this method supports the creation of transparent and non-discriminatory agreements between network operator and users.