Network Demand Research Articles

Overview of Space-Based Laser Communication Missions and Payloads: Insights from the Autonomous Laser Inter-Satellite Gigabit Network (ALIGN)

This paper examines the growing adoption of laser communication (lasercom) in space missions and payloads for identifying emerging trends and key technology drivers of future optical communications satellite systems. It also presents a comprehensive overview of commercially available and custom-designed lasercom terminals, outlining their characteristics and specifications to meet the evolving demands of global satellite networks. The analysis explores the technical considerations and challenges associated with integrating lasercom terminals into LEO constellations and the Inter-satellite communications service provision in LEO due to their power, size, and weight constraints. By analyzing advancements in CubeSat lasercom technology designed to cater for the emergence of future mega constellations of interacting small satellites, the paper underscores its promising role in establishing high-performance satellite communication networks for future space exploration and data transmission. In addition, a brief overview of our ALIGN planned mission is provided, which highlights the main key operational features in terms of PAT and link budget analysis.

Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework

This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets.

Assessing the Profit Impact of ARIMA and Neural Network Demand Forecasts in Retail Inventory Replenishment

This study investigates the integration of demand forecasting and inventory replenishment strategies to enhance retail profitability. A deterministic optimal replenishment model is utilized to analyze the predictive performance of various neural network architectures and ARIMA models using real sales data. The predictive accuracy and subsequent influence on optimal firm profits over a multi-period planning horizon is assessed. The Integer Programming model devised optimizes daily replenishment across multiple retail routes, taking into account sales revenue, supply costs, inventory holding, sales loss, and transportation expenses. The study is distinctive in its dual assessment: it evaluates both the accuracy of forecasting methods and their direct impact on profitability through systematic inventory decisions. Neural network architectures selected for minimizing error in product sales predictions have 6% lower mean squared error compared to Akaike Information Criterion minimizing ARIMA models. For longer horizon predictions necessary in performance gap grows larger, e.g., with %60 difference for predictions 15 days ahead. Predictions reflect as 1.6% higher profits on average, when neural network predictions and more efficient longer planning horizons of the optimization model are preferred. Planning 30 days ahead, optimizing with neural network predictions elicits 2.3% higher profits compared to profits attainable based on ARIMA predictions. Our findings illustrate how different forecasting methods can affect firm profitability by shaping inventory replenishment strategies. By merging mathematical optimization with time series forecasting, this research provides a comprehensive evaluation of how advanced predictive technologies can enhance retail inventory practices and improve profitability.

Knowledge Distillation and Student-Teacher Learning for Weed Detection in Turf

Abstract Machine vision-based herbicide applications relying on object detection or image classification deep convolutional neural network (DCNN) demand high memory and computational resources, resulting in lengthy inference times. To tackle these challenges, this study assessed the effectiveness of three teacher models, each trained on datasets of varying sizes, including D-20k (comprising 10,000 true positive and true negative images) and D-10k (comprising 5,000 true positive and true negative images). Additionally, knowledge distillation was performed on their corresponding student models across a range of temperature settings. After the process of student-teacher learning, the parameters of all student models were reduced. ResNet18 not only achieved higher accuracy (ACC≥0.989) but also maintained higher frames per second (FPS≥742.9) under its optimal temperature condition (T=1). Overall, the results suggest that employing knowledge distillation on the machine vision models enabled accurate and reliable weed detection in turf while reducing the need for extensive computational resources, thereby facilitating real-time weed detection and contributing to the development of smart, machine vision-based sprayers.

DAP-CBR: enhancing Bitcoin block propagation efficiency using dynamic compact block relay’s prefilling of transactions

This study examines the potential of BIP-152’s Compact Block Relay (CBR) to enhance the Bitcoin network. This work explores the block propagation efficiency through dynamic prefilling of transactions. In addition, an enhanced CBR model is proposed to reduce superfluous transaction requests, thus improving the block distribution process. The analysis considers the impact of the dynamically prefilled transactions on Bitcoin network scalability, comparing the advantages and disadvantages of this approach. We also conduct a comparative study of fixed-size and dynamically sized prefilled transactions to highlight the importance of adapting to network demands. Prefilling a fixed number of transactions without considering demand can cause inefficiencies and strain the network with unnecessary bandwidth use. Indiscriminate prefilling exacerbates these issues by inflating data packets unnecessarily, increasing latency and reducing network responsiveness. Our research indicates that the proposed solution can significantly reduce the number of round-trips between network nodes by an average of 29.77% and block reconstruction latency by 39.10% when compared with the CBR.

Improving Optical Transmission Performance in Multi-Channel Networks Utilizing Convolutional Neural Network-Enabled Digital Signal Processing to Mitigate Four-Wave Mixing

ABSTRACT The rapid growth of online services and the global transition to digital platforms, accelerated by the COVID-19 pandemic and the emergence of AI-based services, have led to a surge in demand for high-capacity optical communication networks (OCNs). However, this transition exacerbates the issue of fiber nonlinearity, notably four-wave mixing (FWM), which can significantly impact signal integrity in long-distance OCN transmissions with multiple channels. To address this challenge, this paper presents a novel approach leveraging a convolutional neural network (CNN)-based digital signal processing (DSP) model to mitigate the nonlinear effects of FWM on signal quality. The proposed CNN model, designed with three convolutional layers and trained on a comprehensive dataset of simulated OCN transmissions, demonstrates significant improvement in reducing nonlinear distortions caused by FWM. Our method is compared with traditional compensation techniques, such as digital back-propagation and Volterra series-based equalizers, showing a reduction in power penalties by 1 dB and an enhancement in the power budget by 2 dB. Simulation results indicate that the bit error rates (BERs) remain consistently below the critical threshold of 10−9 across various transmission distances, outperforming conventional methods. The simulation analysis, conducted on a 100 km multi-channel OCN testbed, confirms the CNN equalizer’s efficacy in reducing crosstalk and improving power efficiency, with a demonstrated 15% reduction in required launch power. Moreover, the analytical models used to evaluate the CNN-based DSP model show high accuracy in predicting performance, underscoring the model’s capability to achieve high-quality transmission with lower power requirements. These findings advocate for the integration of our CNN-based DSP model into OCNs, offering a fruitful solution for managing multi-channel, long-distance, and high-data-rate optical transmissions in real-world systems.

Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment

Recently, the Wireless Body Area Networks (WBAN) have become a promising and practical option in the tele-care medicine information system that aids for the better clinical monitoring and diagnosis. The trend of using Internet of Things (IoT) has propelled the WBAN technology to new dimension in terms of its network characteristics and efficient data transmission. However, these networks demand the strong authentication protocol to enhance the confidentiality, integrity, recoverability and dependability against the emerging cyber-physical attacks owing to the exposure of the IoT ecosystem and the confidentiality of biometric data. Hence this study proposes the Fog based WBAN infrastructure which incorporates the hybrid symmetric cryptography schemes with the chaotic maps and feed forward networks to achieve the physiological data info security without consuming the characteristics of power hungry WBAN devices. In the proposed model, scroll chaotic maps are iterated to produce the high dynamic keys streams for the real time applications and feed-forward layers are leveraged to align the complex input-output associations of cipher data for subsequent mathematical tasks. The feed forward layers are constructed which relies on the principle of Adaptive Extreme Learning Machines (AELM) thereby increasing randomness in the cipher keys thereby increasing its defensive nature against the different cyber-physical attacks and ensuring the high secured encrypted-decrypted data communication between the users and fog nodes. The real time analysis is conducted during live scenarios. BAN-IoT test beds interfaced with the heterogeneous healthcare sensors and various security metrics are analysed and compared with the various residing cryptographic algorithms. Results demonstrates that the recommended methodology has exhibited the high randomness characteristics and low computational overhead compared with the other traditional BAN oriented cryptography protocol schemes

A machine learning approach to forecast 5G metrics in a commercial and operational 5G platform: 5G and mobility

The demand for more secure, available, reliable, and fast networks emerges in a more interconnected society. In this context, 5G networks aim to transform how we communicate and interact. However, studies using 5G data are sparse since there are only a few number of publicly available 5G datasets (especially about commercial 5G network metrics with real users). ►In this work, we analyze the data of a commercial 5G deployment with real users, and propose forecasting techniques to help understand the trends and to manage 5G networks. We propose the creation of a metric to measure the traffic load. We forecast the metric using several machine learning models, and we choose LightGBM as the best approach. We observe that this approach obtains results with a good accuracy, and better than other machine learning approaches, but its performance decreases if the patterns contain unexpected events. Taking advantage of the lower accuracy in the performance, this is used to detect changes in the patterns and manage the network in real-time, supporting network resource elasticity by generating alarms and automating the scaling during these unpredictable fluctuations. ►Moreover, we introduce mobility data and integrate it with the previously traffic load metric, understanding its correlation and the prediction of 5G metrics through the use of the mobility data. We show again that LightGBM is the best model in predicting both types of 5G handovers, intra- and inter-gNB handovers, using the mobility information through Radars in the several roads, and lanes, near the 5G cells.

Beyond half-mile circle: Measuring the impact of subway expansion on home-based travels in Beijing, China

Subways play an important role in supporting travel needs, yet whether the benefits of subway expansion can justify the substantial investments remains a much-debated question. Existing studies have arbitrarily defined the catchment area of a subway station using walking distance and have disproportionately focused on commuting trips, potentially underestimating broader subway effects. To address these limitations, this study employed the distance-band approach to accurately define the treatment area of subway stations while incorporating both local and network effects. Based on longitudinal mobile signaling data, we used the difference-in-differences approach to estimate the average treatment effects of 32 new subway stations on different types of home-based travels in Beijing, China. We found that the opening of new subway stations has led to significant increases in subway usage, trip duration, and trip distance within a 2000-metre radius, and that commuting trips have been more affected than home-based non-commuting trips, except during the COVID-19 pandemic. Moreover, the new subway stations also enhanced the connectivity of the city-wide network, influencing the travel behavior of residents in the vicinity of existing subway stations. We further found that subway expansion is particularly beneficial for women, younger residents, and residents of low-rent neighborhoods. According to these findings, planning new subway stations in Beijing and other densely populated megacities requires an integrated approach that considers the relationships between the existing transport network and diverse travel demand.

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.

Real-time demonstration of 64 × 200 Gbps UDWDM-PON downstream transmission based on silicon photonic integrated transceiver

Coherent detection, high-order modulation, and dense wavelength division multiplexing (DWDM) technologies provide solutions for increasing bandwidth demand of future access networks. We experimentally demonstrate a real-time 64 × 200-Gb/s coherent ultra-dense wavelength-division (UDWDM) coherent passive optical networks (PONs) at 75-GHz channel spacing. The demonstrated downlink transmission is realized with the dual-polarization QPSK (DP-QPSK) modulation scheme. The cost is reduced by using silicon photonic integrated coherent transceiver modules and wider grid AWGs. The power budget is evaluated when all 200 channels are launched at C band. The power budget is 26 dB after 20-km G.652D standard single mode fiber (SSMF) transmission without amplifier at the receiver side under soft-decision FEC (SD-FEC) threshold of 1 × 10−2, and can achieve 32.5 dB with pre-amplified semiconductor optical amplifier (SOA) used at the receiver side.

Optimized design of horseshoe-and-spur filterless networks leveraging point-to-multipoint coherent pluggable transceivers

Cutting-edge network architectures and solutions are needed to empower operators to address capacity demands in metro and access networks efficiently. The horseshoe topology, along with similar topologies, is commonly employed in metro-aggregation segments due to its compatibility with the hub-and-spoke traffic pattern present in these networks and the survivability that they can provide. A filterless architecture can enhance cost-effectiveness by replacing active elements with passive components. Moreover, supporting coherent-based point-to-multipoint transceivers—enabled by digital subcarrier multiplexing (DSCM)—can yield additional cost savings. It is noteworthy that telecommunication network topologies often evolve to accommodate more end (leaf) nodes, extending the original horseshoe with spurs or small and short trees. This paper targets these types of networks and proposes combining the utilization of coherent pluggable transceivers leveraging DSCM to guarantee transparent communication between the hub and leaf nodes while adopting different filterless node architectures with selective amplifier deployment. Moreover, it discusses the potential advantages of the architecture using an exact optimization framework tailored to various network sizes and scenarios, which accounts for the amplifiers’ placement and the available types of power splitters/combiners/couplers. The results demonstrate that strategically deploying add/drop and express amplifiers, along with optimizing coupler ratios, can effectively meet design requirements while minimizing the number of optical amplifiers required.

Scottish Crofting and an Alternative to Capitalism

ABSTRACT Small-scale farming communities in Scotland engaged in a combination of food sovereignty, agroecology, or land sovereignty are, to some extent, offering an alternative to capitalism. These strategies have not only made them more resilient to the recent crises of capitalism but have done so by reducing their dependency upon it. During these crises, such as the COVID-19 pandemic, they were not only more able to sustain themselves when long food supply chains collapsed but they also increased their autonomy and sustainability through an increased demand for local food networks. What is more, in certain areas, these strategies have transformed social relations and, at times, revealed unalienated practices such as gift economies. This research was conducted over a 15-month period and primarily consisted of seasonal interviews with 14 small-scale food producers in Scotland. This paper will examine the manner in which the agricultural practices of these communities offer a glimpse of what an alternative to capitalism might look like through an analysis that draws on the work of James C. Scott, Marcell Mauss and John Holloway.

Simplifying IP multimedia systems by introducing next-generation networks with scalable architectures

The IP Multimedia Subsystem (IMS) is essential to providing multimedia services over IP networks. However, maintaining its scalability in the face of increasing demand and changing next-generation networks is still a significant concern. In light of growing loads and service diversity, scalable solutions are required, as this article explores the shortcomings of the IMS designs in use today. To present an innovative, scalable architecture for IMS that integrates cutting-edge computer science techniques, enabling effective service delivery in next-generation networks. By bridging the gap between the static nature of previous IMS designs and the dynamic demands of contemporary telecommunications, this article hopes to deliver results. A mixed-method approach was utilized, integrating evaluations of architectural flexibility with network loads and service needs analysis. In order to evaluate the suggested designs' performance to current benchmarks, the study used simulations to predict the architectures' performance under various scenarios. Compared to conventional IMS frameworks, the suggested design showed outstanding scalability, enabling a tenfold increase in simultaneous connections and services. It notably improved fault tolerance and service delay and increased flexibility for network modifications and service diversification. Next-generation network demands are met by IMS designs that use modern computer science methodologies to improve scalability and flexibility significantly. More practical, scalable, and reliable service delivery methods will be possible thanks to the foundation our work creates for future research into dynamic, resilient telecommunications infrastructures.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder.

An individual's risk of substance use disorder (SUD) is shaped by a complex interplay of potent biosocial factors. Current neurodevelopmental models posit vulnerability to SUD in youth is due to an overreactive reward system and reduced inhibitory control. Having a family history of SUD is a particularly strong risk factor, yet few studies have explored its impact on brain function and structure prior to substance exposure. Herein, we utilized a network control theory approach to quantify sex-specific differences in brain activity dynamics in youth with and without a family history of SUD, drawn from a large cohort of substance-naïve youth from the Adolescent Brain Cognitive Development Study. We summarize brain dynamics by calculating transition energy, which probes the ease with which a whole brain, region or network drives the brain towards a specific spatial pattern of activation (i.e., brain state). Our findings reveal that a family history of SUD is associated with alterations in the brain's dynamics wherein: i) independent of sex, certain regions' transition energies are higher in those with a family history of SUD and ii) there exist sex-specific differences in SUD family history groups at multiple levels of transition energy (global, network, and regional). Family history-by-sex effects reveal that energetic demand is increased in females with a family history of SUD and decreased in males with a family history of SUD, compared to their same-sex counterparts with no SUD family history. Specifically, we localize these effects to higher energetic demands of the default mode network in females with a family history of SUD and lower energetic demands of attention networks in males with a family history of SUD. These results suggest a family history of SUD may increase reward saliency in males and decrease efficiency of top-down inhibitory control in females. This work could be used to inform personalized intervention strategies that may target differing cognitive mechanisms that predispose individuals to the development of SUD.

Designing green logistics networks under carbon tax policy: Post-COVID condition

This paper proposes a fuzzy-based method for estimating demand in forward and reverse logistics networks (F&RLNs) subject to carbon pricing in the post-COVID era. We investigate the complex relationships between carbon prices, the reverse logistics network (RLN), and the ever-changing product demand by utilizing fuzzy inference systems (FIS) capabilities. The case study from Mexico confirms the effectiveness of the proposed technique. The findings demonstrate the precision and predictive power of our proposed FIS-based method and show how well it can predict the number of items in demand and the number of goods that will be returned and subject to carbon taxation in the post-COVID era. The results illustrate the significant impact that carbon pricing has on the RLN and the associated product demand. For logistics managers seeking to make informed decisions about the establishment and operation of forward and RLNs within the carbon pricing paradigm, this empirical data provides insightful information in the post-COVID era.

A Multi-Objective Direction of Arrival Estimation Technique MinimizingEnergy Consumption in Wireless Sensor Network

Wireless Sensor Network (WSN) demand for secure communication is challenging due to its limited resource constraints. Hence, this research developed an effective distributed DOA estimation model through Direction Optimization Integrated Ranking Voting (DOIRV) to improve the performance of WSNs. The developed model comprises the multi-objective optimization model with the Whale technique for the WSN. The constructed DOIRV model uses objective function estimation with branch-and-bound for effective data transmission. The developed DOIRV model uses the Artificial Intelligence-based machine learning model for the DOA routing path computation and estimation. Finally, the DOIRV estimation is evaluated for the performance analysis with the consideration of the Cramer-Rao analysis for the data transmission in the WSN model. Simulation results stated that the proposed weighted technique significantly improves the network performance such as packet delivery ratio, throughput, and network delay. Analysis of the technique expressed that the proposed DOIRV technique exhibits effective performance rather than the conventional technique in terms of network delay, throughput, and packet delivery ratio. The comparative analysis stated that the performance of the proposed DOIRV model is ~13% higher than the conventional technique in terms of packet delivery ratio and ~18% higher for the throughput.

Towards sustainable industry 4.0: A survey on greening IoE in 6G networks

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly increased energy consumption, carbon emissions, and global warming. IoE applications in Industry 4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and centralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving to sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability challenges. Many advanced technologies and strategies efficiently solve issues by enhancing connectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising approach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and reducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive overview of how advanced technologies can contribute to green IoE in the 6G network of Industry 4.0 applications. This survey provides a comprehensive overview of advanced technologies, including Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and Machine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G networks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications and analyse the challenges and opportunities to make IoE greener using the discussed technologies.

Design and simulation of an enhanced optical demultiplexer using photonic crystal resonators

The purpose of this paper is to examine the design, simulation, and optimization of a demultiplexer optical system using photonic crystal resonators. Utilizing the distinct properties of photonic crystals, the proposed device achieves high-quality resonances, efficient wavelength separation, and minimal crosstalk. These characteristics are crucial for enhancing the performance of dense wavelength division multiplexing (DWDM) systems, which require precise and efficient channel separation to manage increasing data traffic. Extensive simulations conducted with the RSoft CAD tool explore the impact of varying resonator parameters such as refractive index, geometric dimensions, and material composition on the device's performance. The results indicate substantial improvements in transmission efficiency and quality factor, with average values of 98.675% and 3131.125, respectively. Furthermore, the ability to fine-tune these parameters allows for the customization of the demultiplexer to specific wavelength ranges, making it highly adaptable for various telecommunications applications. This research aims to propel the development of compact, high-performance photonic devices that meet the growing demands of modern communication networks, ultimately contributing to more efficient and reliable optical communication systems.