An Improved Simulation Based Method forSelection of Cell in Cellular Network

An Improved Simulation Based Method forSelection of Cell in Cellular Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Improved Simulation Based Method forSelection of Cell in Cellular Network Kalpesh Popat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3993313/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In Cellular networks, selection of cell is a process which will help in determining the exact location of the cellular phone. Various services related to mobile network are dependent on selection of cells only. Factors like radio propagation, directional beamforming, and handover processes play a significant role in determining the overall performance of the network. Researchers and network operators continuously work on optimizing cell selection strategies to minimize disruptions and provide high-quality service to end users. Cell Selection plays a vital role in the whole cellular network system. In this paper Cell selection performance has been evaluated by using 9 working simulation models with 9 different scenarios from simple and small network to high congestion mobile network. Qualnet Software is employed for conducting analyses that take into account factors such as the quantity of nodes, simulation duration, and the volume of calls between multiple mobile devices. Cellular networks Cell Selection Fast Cell Selection Mobile Management Performance Evaluation. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION A heterogeneous network is made up of various connected mobile nodes and links of various sorts. These interconnected structures hold a wealth of information that can be utilised to mutually improve nodes and linkages and spread the information from node to the other [1]. The development of heterogeneous cellular networks (Fig. 1 ) has gained traction in industry and academia, garnering the attention of standardisation groups such as 3GPP (Third Generation Partnership Project) LTE (Long Term Evolution) and IEEE 802.16j, whose goals include expanding the capacity and coverage of cellular networks [2]. The fast development of tiny cells is causing the cellular network to become random and heterogeneous. The multi-tier heterogeneous network (HetNet) is designed to meet the huge connectivity requirements of growing cellular networks. Cellular networks are typically described by deterministically putting each tier, such as pico, macro and relay nodes (Fig. 2 ), on a grid that ignores the nodes' spatial unpredictability [3]. As the need for wireless data reaches historic heights, cellular service providers are looking at new ways to meet it. One potential option is the deployment of small cells with lower consumption of power and smaller covering regions. Small cells, which utilise the same spectrum as existing macro cells, allow for greater bandwidth re-usage, potentially leading to higher user rates. The coverage area for small cells is greatly decreased because their signals are weaker and the incremental benefit of placing each small cell is limited[5]. The process through which it will decide which cell(s) will offer its service to which mobile station is known as cell selection. Optimization in this process is a critical step toward making the most of both present and the future of the cellular networks[6]. Cell selection (Fig. 4 ). refers to the method that allows each mobile station to receive services. It is vital to optimise the procedure in order to maximise the current and future performance of cellular networks[7]. Cell selection is the procedure of deciding which cell will serve each mobile station (Fig. 3). The purpose is to identify a high-profit subgroup of clients who can be completely pleased by the proposed study[8]. Cell selection has a significant impact on the overall system's effectiveness. The overall performance of the cellular network can be determined by how effectively the network uses Cell Selection. 2. RELATED WORK Make use of the mechanism All-or-Nothing Demand Maximization (AoNDM) will determine the assignment of mobile nodes to base stations in a service-oriented cellular network environment, where many base stations are simultaneously satisfied. It is feasible to make the most profit from clients who are completely satisfied. The study's findings suggest that a theoretical method based on algorithms can manage capacity, demands, and interference, resulting in a better solution for cell planning challenges and greater scalability[8]. This is a key step toward more targeted 4G network planning. the downlink rate distribution under an extended cell-selection model, which specifically differentiates among long- term channel impacts including such shadowing and path-loss, and small-scale impacts such as fading. The authors offered an equivalent explanation of such a generic cell selection model and demonstrated that the impact of shadowing may be explored similarly by correctly scaling transmit powers. Using this comparable interpretation, research of the influence of shadowing on load balancing was conducted, and it was discovered that in some regimes, shadowing automatically balances load across several tiers, reducing the requirement for artificial cell selection bias[10]. A comprehensive examination of a fresh method for cell selection in 4th generation cellular networks. The suggested mechanism, in contrast to the existing cell selection protocol, is worldwide, provides a performance guarantee, and incorporates many of the predicted 4G technologies[6]. The major enabling technology for meeting the high data rate needs of future generations of wireless networks is dense deployment of tiny cells. In this situation, heterogeneous broadband solutions will be employed to connect small cells to the main network, and accessibility and backhaul must be optimised together to make the best use of available resources. The suggested study substitutes a strategy that optimises network ergodic capacity for the traditional SINR-based association criterion. As a result, it modelled the links between cell load, backhaul restrictions, resource allocation, and ergodic capacity analytically [11]. In order to balance the workload among heterogeneous cells and improve resource usage and consumer fulfilment in terms of both data rate and EMF exposure, a novel cell association paradigm for heterogeneous cellular networks (HetNets) is proposed. Two heuristic strategies are demonstrated to solve the cell selection system's General Assignment Problem (GAP) with a low level of complexity. The study's findings show that the suggested substitutes significantly outperform legacy association schemes [15]. Examples of contexts in which the usage of mobile codes is particularly practical include autonomous decentralised systems. For instance, given their capacity to decentralise processing, adapt to system autonomy, allow for flexible management of installed code, and support user interaction, mobile codes appear to be very advantageous when designing highly scalable distributed systems in a large, heterogeneous, and multi-organizational distributed environment [13]. Asynchronous Transmission Mode (ATM) networks have been chosen for usage in Broadband Integrated Service Digital Networks (B-ISDN) because they can accommodate a variety of services, including phone, data, video, etc. a queuing model for ATM networks that takes into account three different forms of traffic, such as voice, data, and video. We examine a discrete time single-server (GI/1/1) queuing system that has three infinitely large priority queues. The waiting time distribution for each class of packets is explicitly derived[14] the idea of mobile ad hoc networking (MANET) and highlights some of its potential applications in the future. The study also discusses two of the technological difficulties that MANET presents, namely Geocaching and QoS[15]. In many ad hoc sensor network scenarios, security is a top concern. A security strategy must include the detection of malicious nodes. The suggested method employs fuzzy logic to detect node attacks and other harmful activity. The proposed study will both identify the network assault and offer a solution to shorten the network execution time. In order to secure Mobile Adhoc Networks, the project's goal is to provide security. The suggested work employs the AODV (Ad-hoc On-demand Distance Vector) algorithm.[16]. Depending on the kind of session the mobile nodes are having, it employs a home agent to choose the best network. It chooses the best route inside the chosen ideal network using a route selection algorithm. In the Present work, two distinct position-based ant colony routing algorithms are suggested for mobile ad-hoc networks Before a session begins, each routing algorithm chooses the best possible route[17]. Cell selection, which comprises spotting and decoding the Primary Synchronization Signal (PSS) and the Secondary Synchronization Signal, is the first step in the strategy (SSS). The most important control parameters in the Master Information Block (MIB), such as system bandwidth, are then extracted from the Physical Broadcast Channel (PBCH), enabling the configuration and operation of the other channels in the cell. The other features of the cell's configuration can be discovered by listening to the unencrypted System Information Blocks (SIB), which a passive radio sniffer can intercept. In order to send and receive user traffic at this point, the UE establishes a genuine network connection using a randomised access procedure and a NAS (Non-Access Stratum) process[16]. Factors affecting Cell Selection Now, here it is necessary to discuss about the various factors which will affect the Cell Selection. It includes Mobility pattern and Call arrival pattern. Mobility Pattern Mobility pattern of the user is the most important factor in determining the cell selection. Think of a general condition in which user will change the location and select new cells during the business hours or working hours if the user is in that kind of profession. But if the user is doing a job in which he / she has to work at single place then in that situation the change in location as well as cell selection is comparatively less in numbers. Various models for Mobility patterns are given below (1) Memory less (Random Walk) Movement Model [18] (2) Markovian Movement Model [19] (3) Shortest Distance Model [20] (4) Gauss Markov Model [21] (5) Activity based model [22] (6) Mobility Trace [23] (7) Fluid-Flow Model [24] Call Arrival Pattern Time is the important factor to describe the Call Arrival Pattern or rate at which the user will receive the calls. User always gets more number of call during working hours as compared to non-working hours. Various models for Call Arrival Pattern are given below (1) Poisson Model [25] (2) Call Arrival Trace [26] 3. PROPOSED METHODOLOGY Here in this paper a novel Cell Selection method has been proposed in which the data has been collected through simulation model by considering various situations in which various models are covered like (1) Walking mobility, (2) Driving Mobility (3) Mobility with Low Congestion (4) Mobility with Medium Congestion and (5) Mobility with High Congestion. Here the data collected based on simulation carried out through Qualnet® Software and results are used to do the performance analysis. The major points that are required to be taken care here are (1) No. of nodes, (2) Simulation time (3) Call between no. of cell phones. 4. SIMULATION In this paper the simulation has been carried out through Qualnet® Software to test various possibilities of environment in which the Cell Selection can be applicable to the Cellular network. For simulation purpose the base environment taken is given in Fig. 5 . Above Fig. 5 shows the Base Diagram used for simulation purpose. It contains total 8 cellular nodes, one base station and one cloud network. The same network environment has been used through-out the whole simulation process. In the above Fig. 6 it shows a scenario in which there is No Congestion as well as very low mobility among nodes. There is GSM call going on in between node 7 and node 11. The call has been started after 10 seconds and the call duration is 300 seconds. Here only two MS are busy i.e. MS-7 and MS-11. MS-7 moves 2898 meters in 300 seconds while MS-11 moves 3019 meters in 300 seconds. In the above Fig. 7 it shows a scenario in which there is Moderate Congestion as well as moderate mobility among nodes. There is GSM call going on in between node 7 to node 11 and node 14 to node 15. The call has been started after 10 seconds and the call duration is 300 seconds. Here total four MS are busy i.e. MS-7, MS-11, MS-14 and MS-15. MS-7 moves 5439 meters in 300 seconds. MS-11 moves 5296 meters in 300 seconds. MS-14 moves 4887 meters in 300 seconds. MS-15 moves 5270 meters in 300 seconds. In the above Fig. 8 it shows a scenario in which there is High Congestion and high mobility among nodes. There is GSM call going on in between node 7 to node 11, node 14 to node 15 and node 8 to node 12 respectively. The call has been started after 10 seconds and the call duration is 300 seconds. Here total six MSs are busy i.e. MS-7, MS-11, MS-14,MS-15, MS-8 and MS-12. MS-7 moves 9071 meters in 300 seconds. MS-11 moves 8778 meters in 300 seconds. MS-14 moves 7713 meters in 300 seconds. MS-15 moves 7753 meters in 300 seconds. MS-8 moves 7375 meters in 300 seconds. MS-12 moves 7213 meters in 300 seconds. 5. PERFROMANCE ANALYSIS Through the above simulation process following performance analysis can be derived and summarised in a tabular format : Table 1 Performance Analysis for No Congestion and Low Mobility Sr. No. Node No. No.ofCell Selection Attempts No. of Cell Selection Failures No. of Cell Re- selection Attempts 1 7 6 0 4 2 8 0 0 0 3 9 0 0 0 4 11 5 0 3 5 12 0 0 0 6 13 0 0 0 7 14 0 0 0 8 15 0 0 0 Table 2 Performance Analysis for Moderate Congestion and Moderate Mobility Sr. No. Node No. No.ofCell Selection Attempts No. of Cell Selection Failures No. of Cell Re- selection Attempts 1 7 4 1 2 2 8 0 0 0 3 9 0 0 0 4 11 5 0 3 5 12 0 0 0 6 13 0 0 0 7 14 3 1 1 8 15 4 0 2 Table 3 Performance Analysis for High Congestion and High Mobility Sr. No. Node No. No.ofCell Selection Attempts No. of Cell Selection Failures No. of Cell Re- selection Attempts 1 7 4 1 4 2 8 3 0 2 3 9 0 0 0 4 11 5 1 3 5 12 3 0 2 6 13 0 0 0 7 14 4 1 2 8 15 4 1 3 Here in above Table 1 , 2 and 3 it shows the performance analysis with different congestion and mobility patterns. 6. CONCLUSION AND FUTURE SCOPE This research work is focused on building highest quality links between cells and base stations specially in diverse cellular networks. Discussion on the addressed problem followed by explanation of cell section process is done. Proposed solution is based on performing analysis of data - that is diverse in nature. Data is collected through simulations with different mobility and congestion levels. Qualnet Software is used to perform analysis considering number of nodes, simulation time and calls between number of cell phones. It can be seen from the results that our proposed solution performs best in all the different combinations of mentioned parameters. This will involve deploying the solution in a live network and collecting actual data from diverse cellular environments. Real-world data analysis will provide more robust and practical insights into the performance of the given solution. Further the proposed solution can be tested on Real-world data in which a network with 1000 + nodes in which the Cell Selection process can give better results. Declarations Conflict of Interest: On behalf of all authors, the corresponding author states that there is no conflict of interest. Funding: There is no funding in this work from any agency. Author Contribution This research work is focused on building highest quality links between cells and base stations specially in diverse cellular networks. Discussion on the addressed problem followed by explanation of cell section process is done. Proposed solution is based on performing analysis of data - that is diverse in nature. Data is collected through simulations with different mobility and congestion levels. Qualnet Software is used to perform analysis considering number of nodes, simulation time and calls between number of cell phones. It can be seen from the results that our proposed solution performs best in all the different combinations of mentioned parameters. This will involve deploying the solution in a live network and collecting actual data from diverse cellular environments. Real-world data analysis will provide more robust and practical insights into the performance of the given solution. Further the proposed solution can be tested on Real-world data in which a network with 1000+ nodes in which the Cell Selection process can give better results. Data Availability: All the data used in this manuscript is available with me. References J. Han, M. Kamber, and J. Pei, “Data Mining Trends and Research Frontiers,” in Data Mining , Elsevier, 2012, pp. 585–631. Y. Q. (Editor) Rose Qingyang Hu (Editor), Heterogeneous Cellular Networks . . M. M. Fadoul, “Modeling multi-tier heterogeneous small cell networks: rate and coverage performance,” Telecommun. 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Silvestre, “A Lightweight Fluid Model for Mobile Ad hoc Distributed Systems,” in 2022 IEEE Symposium on Computers and Communications (ISCC) , Jun. 2022, pp. 1–7, doi: 10.1109/ISCC55528.2022.9912980. E. Khezri, E. Zeinali, and H. Sargolzaey, “A Novel Highway Routing Protocol in Vehicular Ad Hoc Networks Using VMaSC-LTE and DBA- MAC Protocols,” Wirel. Commun. Mob. Comput. , vol. 2022, pp. 1–11, Jan. 2022, doi: 10.1155/2022/1680507. B. Yuksel, M. Cingoz, G. Karabulut, and S. Oktug, “Call arrival model for GSM network including handover,” in 2009 IEEE 3rd International Symposium on Advanced Networks and Telecommunication Systems (ANTS) , Dec. 2009, pp. 1–3, doi: 10.1109/ANTS.2009.5409848. Additional Declarations No competing interests reported. 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INTRODUCTION","content":"\u003cdiv\u003e\n \u003cp\u003eA heterogeneous network is made up of various connected mobile nodes and links of various sorts. These interconnected structures hold a wealth of information that can be utilised to mutually improve nodes and linkages and spread the information from node to the other [1]. The development of heterogeneous cellular networks (Fig. \u003cspan\u003e1\u003c/span\u003e) has gained traction in industry and academia, garnering the attention of standardisation groups such as 3GPP (Third Generation Partnership Project) LTE (Long Term Evolution) and IEEE\u003c/p\u003e\n \u003cp\u003e802.16j, whose goals include expanding the capacity and coverage of cellular networks [2]. The fast development of tiny cells is causing the cellular network to become random and heterogeneous. The multi-tier heterogeneous network (HetNet) is designed to meet the huge connectivity requirements of growing cellular networks. Cellular networks are typically described by deterministically putting each tier, such as pico, macro and relay nodes (Fig. \u003cspan\u003e2\u003c/span\u003e), on a grid that ignores the nodes\u0026apos; spatial unpredictability [3].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cp\u003eAs the need for wireless data reaches historic heights, cellular service providers are looking at new ways to meet it. One potential option is the deployment of small cells with lower consumption of power and smaller covering regions. Small cells, which utilise the same spectrum as existing macro cells, allow for greater bandwidth re-usage, potentially leading to higher user rates. The coverage area for small cells is greatly decreased because their signals are weaker and the incremental benefit of placing each small cell is limited[5].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cp\u003eThe process through which it will decide which cell(s) will offer its service to which mobile station is known as cell selection. Optimization in this process is a critical step toward making the most of both present and the future of the cellular networks[6]. Cell selection (Fig. \u003cspan\u003e4\u003c/span\u003e). refers to the method that allows each mobile station to receive services. It is vital to optimise the procedure in order to maximise the current and future performance of cellular networks[7]. Cell selection is the procedure of deciding which cell will serve each mobile station (Fig. 3). The purpose is to identify a high-profit subgroup of clients who can be completely pleased by the proposed study[8]. Cell selection has a significant impact on the overall system\u0026apos;s effectiveness. The overall performance of the cellular network can be determined by how effectively the network uses Cell Selection.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"2. RELATED WORK","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMake use of the mechanism All-or-Nothing Demand Maximization (AoNDM) will determine the assignment of mobile nodes to base stations in a service-oriented cellular network environment, where many base stations are simultaneously\u003c/p\u003e \u003cp\u003esatisfied. It is feasible to make the most profit from clients who are completely satisfied. The study's findings suggest that a theoretical method based on algorithms can manage capacity, demands, and interference, resulting in a better solution for cell planning challenges and greater scalability[8]. This is a key step toward more targeted 4G network planning. the downlink rate distribution under an extended cell-selection model, which specifically differentiates among long- term channel impacts including such shadowing and path-loss, and small-scale impacts such as fading. The authors offered an equivalent explanation of such a generic cell selection model and demonstrated that the impact of shadowing may be explored similarly by correctly scaling transmit powers. Using this comparable interpretation, research of the influence of shadowing on load balancing was conducted, and it was discovered that in some regimes, shadowing automatically balances load across several tiers, reducing the requirement for artificial cell selection bias[10].\u003c/p\u003e \u003cp\u003eA comprehensive examination of a fresh method for cell selection in 4th generation cellular networks. The suggested mechanism, in contrast to the existing cell selection protocol, is worldwide, provides a performance guarantee, and incorporates many of the predicted 4G technologies[6]. The major enabling technology for meeting the high data rate needs of future generations of wireless networks is dense deployment of tiny cells. In this situation, heterogeneous broadband solutions will be employed to connect small cells to the main network, and accessibility and backhaul must be optimised together to make the best use of available resources. The suggested study substitutes a strategy that optimises network ergodic capacity for the traditional SINR-based association criterion. As a result, it modelled the links between cell load, backhaul restrictions, resource allocation, and ergodic capacity analytically [11].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn order to balance the workload among heterogeneous cells and improve resource usage and consumer fulfilment in terms of both data rate and EMF exposure, a novel cell association paradigm for heterogeneous cellular networks (HetNets) is proposed. Two heuristic strategies are demonstrated to solve the cell selection system's General Assignment Problem (GAP) with a low level of complexity. The study's findings show that the suggested substitutes significantly outperform legacy association schemes [15].\u003c/p\u003e \u003cp\u003eExamples of contexts in which the usage of mobile codes is particularly practical include autonomous decentralised systems. For instance, given their capacity to decentralise processing, adapt to system autonomy, allow for flexible management of installed code, and support user interaction, mobile codes appear to be very advantageous when designing highly scalable distributed systems in a large, heterogeneous, and multi-organizational distributed environment [13]. Asynchronous Transmission Mode (ATM) networks have been chosen for usage in Broadband Integrated Service Digital Networks (B-ISDN) because they can accommodate a variety of services, including phone, data, video, etc. a queuing model for ATM networks that takes into account three different forms of traffic, such as voice, data, and video. We examine a discrete time single-server (GI/1/1) queuing system that has three infinitely large priority queues. The waiting time distribution for each class of packets is explicitly derived[14] the idea of mobile ad hoc networking (MANET) and highlights some of its potential applications in the future. The study also discusses two of the technological difficulties that MANET presents, namely Geocaching and QoS[15]. In many ad hoc sensor network scenarios, security is a top concern. A security strategy must include the detection\u003c/p\u003e \u003cp\u003eof malicious nodes. The suggested method employs fuzzy logic to detect node attacks and other harmful activity. The proposed study will both identify the network assault and offer a solution to shorten the network execution time. In order to secure Mobile Adhoc Networks, the project's goal is to provide security. The suggested work employs the AODV (Ad-hoc On-demand Distance Vector) algorithm.[16]. Depending on the kind of session the mobile nodes are having, it employs a home agent to choose the best network. It chooses the best route inside the chosen ideal network using a route selection algorithm. In the Present work, two distinct position-based ant colony routing algorithms are suggested for mobile ad-hoc networks Before a session begins, each routing algorithm chooses the best possible route[17].\u003c/p\u003e \u003cp\u003eCell selection, which comprises spotting and decoding the Primary Synchronization Signal (PSS) and the Secondary Synchronization Signal, is the first step in the strategy (SSS). The most important control parameters in the Master Information Block (MIB), such as system bandwidth, are then extracted from the Physical Broadcast Channel (PBCH), enabling the configuration and operation of the other channels in the cell. The other features of the cell's configuration can be discovered by listening to the unencrypted System Information Blocks (SIB), which a passive radio sniffer can intercept. In order to send and receive user traffic at this point, the UE establishes a genuine network connection using a randomised access procedure and a NAS (Non-Access Stratum) process[16].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFactors affecting Cell Selection\u003c/strong\u003e \u003cp\u003eNow, here it is necessary to discuss about the various factors which will affect the Cell Selection. It includes Mobility pattern and Call arrival pattern.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMobility Pattern\u003c/strong\u003e \u003cp\u003eMobility pattern of the user is the most important factor in determining the cell selection. Think of a general condition in which user will change the location and select new cells during the business hours or working hours if the user is in that kind of profession. But if the user is doing a job in which he / she has to work at single place then in that situation the change in location as well as cell selection is comparatively less in numbers. Various models for Mobility patterns are given below\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e(1) Memory less (Random Walk) Movement Model [18] (2) Markovian Movement Model [19]\u003c/p\u003e \u003cp\u003e(3) Shortest Distance Model [20] (4) Gauss Markov Model [21]\u003c/p\u003e \u003cp\u003e(5) Activity based model [22] (6) Mobility Trace [23]\u003c/p\u003e \u003cp\u003e(7) Fluid-Flow Model [24]\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCall Arrival Pattern\u003c/strong\u003e \u003cp\u003eTime is the important factor to describe the Call Arrival Pattern or rate at which the user will receive the calls. User always gets more number of call during working hours as compared to non-working hours. Various models for Call Arrival Pattern are given below\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e(1) Poisson Model [25]\u003c/p\u003e \u003cp\u003e(2) Call Arrival Trace [26]\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"3. PROPOSED METHODOLOGY","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHere in this paper a novel Cell Selection method has been proposed in which the data has been collected through simulation model by considering various situations in which various models are covered like (1) Walking mobility, (2) Driving Mobility (3) Mobility with Low Congestion (4) Mobility with Medium Congestion and (5) Mobility with High Congestion. Here the data collected based on simulation carried out through Qualnet\u0026reg; Software and results are used to do the performance analysis. The major points that are required to be taken care here are (1) No. of nodes, (2) Simulation time (3) Call between no. of cell phones.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"4. SIMULATION","content":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn this paper the simulation has been carried out through Qualnet\u0026reg; Software to test various possibilities of environment in which the Cell Selection can be applicable to the Cellular network.\u003c/p\u003e\n \u003cp\u003eFor simulation purpose the base environment taken is given in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAbove Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the Base Diagram used for simulation purpose. It contains total 8 cellular nodes, one base station and one cloud network. The same network environment has been used through-out the whole simulation process.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn the above Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e it shows a scenario in which there is No Congestion as well as very low mobility among nodes. There is GSM call going on in between node 7 and node 11. The call has been started after 10 seconds and the call duration is 300 seconds. Here only two MS are busy i.e. MS-7 and MS-11. MS-7 moves 2898 meters in 300 seconds while MS-11 moves 3019 meters in 300 seconds.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn the above Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e it shows a scenario in which there is Moderate Congestion as well as moderate mobility among nodes. There is GSM call going on in between node 7 to node 11 and node 14 to node 15. The call has been started after 10 seconds and the call duration is 300 seconds. Here total four MS are busy i.e. MS-7, MS-11, MS-14 and MS-15. MS-7 moves 5439 meters in 300 seconds. MS-11 moves 5296 meters in 300 seconds. MS-14 moves 4887 meters in 300 seconds. MS-15 moves 5270 meters in 300 seconds.\u003c/p\u003e\n \u003cp\u003eIn the above Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e it shows a scenario in which there is High Congestion and high mobility among nodes. There is GSM call going on in between node 7 to node 11, node 14 to node 15 and node 8 to node 12 respectively. The call has been started after 10 seconds and the call duration is 300 seconds. Here total six MSs are busy i.e. MS-7, MS-11, MS-14,MS-15, MS-8 and MS-12. MS-7 moves 9071 meters in 300 seconds. MS-11 moves 8778 meters in 300 seconds. MS-14 moves 7713 meters in 300 seconds. MS-15 moves 7753 meters in 300 seconds. MS-8 moves 7375 meters in 300 seconds. MS-12 moves 7213 meters in 300 seconds.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. PERFROMANCE ANALYSIS","content":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThrough the above simulation process following performance analysis can be derived and summarised in a tabular format :\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePerformance Analysis for No Congestion and Low Mobility\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSr. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNode No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.ofCell\u003c/p\u003e\n \u003cp\u003eSelection\u003c/p\u003e\n \u003cp\u003eAttempts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Cell\u003c/p\u003e\n \u003cp\u003eSelection\u003c/p\u003e\n \u003cp\u003eFailures\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Cell Re-\u003c/p\u003e\n \u003cp\u003eselection\u003c/p\u003e\n \u003cp\u003eAttempts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePerformance Analysis for Moderate Congestion and Moderate Mobility\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSr. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNode No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.ofCell\u003c/p\u003e\n \u003cp\u003eSelection\u003c/p\u003e\n \u003cp\u003eAttempts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Cell\u003c/p\u003e\n \u003cp\u003eSelection\u003c/p\u003e\n \u003cp\u003eFailures\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Cell Re-\u003c/p\u003e\n \u003cp\u003eselection\u003c/p\u003e\n \u003cp\u003eAttempts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePerformance Analysis for High Congestion and High Mobility\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSr. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNode No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.ofCell\u003c/p\u003e\n \u003cp\u003eSelection\u003c/p\u003e\n \u003cp\u003eAttempts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Cell\u003c/p\u003e\n \u003cp\u003eSelection\u003c/p\u003e\n \u003cp\u003eFailures\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Cell Re-\u003c/p\u003e\n \u003cp\u003eselection\u003c/p\u003e\n \u003cp\u003eAttempts\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eHere in above Table\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e it shows the performance analysis with different congestion and mobility patterns.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"6. CONCLUSION AND FUTURE SCOPE","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis research work is focused on building highest quality links between cells and base stations specially in diverse cellular networks. Discussion on the addressed problem followed by explanation of cell section process is done. Proposed solution is based on performing analysis of data - that is diverse in nature. Data is collected through simulations with different mobility and congestion levels. Qualnet Software is used to perform analysis considering number of nodes, simulation time and calls between number of cell phones. It can be seen from the results that our proposed solution performs best in all the different combinations of mentioned parameters. This will involve deploying the solution in a live network and collecting actual data from diverse cellular environments. Real-world data analysis will provide more robust and practical insights into the performance of the given solution. Further the proposed solution can be tested on Real-world data in which a network with 1000\u0026thinsp;+\u0026thinsp;nodes in which the Cell Selection process can give better results.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest:\u003c/h2\u003e \u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThere is no funding in this work from any agency.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis research work is focused on building highest quality links between cells and base stations specially in diverse cellular networks. Discussion on the addressed problem followed by explanation of cell section process is done. Proposed solution is based on performing analysis of data - that is diverse in nature. Data is collected through simulations with different mobility and congestion levels. Qualnet Software is used to perform analysis considering number of nodes, simulation time and calls between number of cell phones. It can be seen from the results that our proposed solution performs best in all the different combinations of mentioned parameters. This will involve deploying the solution in a live network and collecting actual data from diverse cellular environments. Real-world data analysis will provide more robust and practical insights into the performance of the given solution. Further the proposed solution can be tested on Real-world data in which a network with 1000+ nodes in which the Cell Selection process can give better results.\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eAll the data used in this manuscript is available with me.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e J. Han, M. Kamber, and J. Pei, \u0026ldquo;Data Mining Trends and Research Frontiers,\u0026rdquo; in \u003cem\u003eData Mining\u003c/em\u003e, Elsevier, 2012, pp. 585\u0026ndash;631.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Y. Q. (Editor) Rose Qingyang Hu (Editor), \u003cem\u003eHeterogeneous Cellular Networks\u003c/em\u003e. .\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e M. M. Fadoul, \u0026ldquo;Modeling multi-tier heterogeneous small cell networks: rate and coverage performance,\u0026rdquo; \u003cem\u003eTelecommun. Syst.\u003c/em\u003e, vol. 75, no. 4, pp. 369\u0026ndash;382, Dec. 2020, doi: 10.1007/s11235-020-00680-y.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e J. Yang, Z. Pan, H. Xu, and H. Hu, \u0026ldquo;Joint Optimization of Pico-Base- Station Density and Transmit Power for an Energy-Efficient Heterogeneous Cellular Network,\u0026rdquo; \u003cem\u003eFutur. Internet\u003c/em\u003e, vol. 11, no. 10, p. 208, Sep. 2019, doi: 10.3390/fi11100208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e K. Balachandran, J. H. Kang, K. Karakayali, and K. Rege, \u0026ldquo;Cell selection with downlink resource partitioning in heterogeneous networks,\u0026rdquo; \u003cem\u003eIEEE Int. Conf. Commun.\u003c/em\u003e, 2011, doi: 10.1109/iccw.2011.5963548.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e D. Amzallag, R. Bar-Yehuda, D. Raz, and G. Scalosub, \u0026ldquo;Cell selection in 4G cellular networks,\u0026rdquo; \u003cem\u003eIEEE Trans. Mob. 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Oktug, \u0026ldquo;Call arrival model for GSM network including handover,\u0026rdquo; in \u003cem\u003e2009 IEEE 3rd International Symposium on Advanced Networks and Telecommunication Systems (ANTS)\u003c/em\u003e, Dec. 2009, pp. 1\u0026ndash;3, doi: 10.1109/ANTS.2009.5409848.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cellular networks, Cell Selection, Fast Cell Selection, Mobile Management, Performance Evaluation.","lastPublishedDoi":"10.21203/rs.3.rs-3993313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3993313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Cellular networks, selection of cell is a process which will help in determining the exact location of the cellular phone. Various services related to mobile network are dependent on selection of cells only. Factors like radio propagation, directional beamforming, and handover processes play a significant role in determining the overall performance of the network. Researchers and network operators continuously work on optimizing cell selection strategies to minimize disruptions and provide high-quality service to end users. Cell Selection plays a vital role in the whole cellular network system. In this paper Cell selection performance has been evaluated by using 9 working simulation models with 9 different scenarios from simple and small network to high congestion mobile network. Qualnet Software is employed for conducting analyses that take into account factors such as the quantity of nodes, simulation duration, and the volume of calls between multiple mobile devices.\u003c/p\u003e","manuscriptTitle":"An Improved Simulation Based Method forSelection of Cell in Cellular Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-01 11:14:12","doi":"10.21203/rs.3.rs-3993313/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a888d0f2-0f1d-42cf-b5a3-7968e642d879","owner":[],"postedDate":"March 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-01T14:14:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-01 11:14:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3993313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3993313","identity":"rs-3993313","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}