Fixed telephone network planning

Fixed telephone network planning

Group Member: • Afnan Salem – 0562374 • Isra Yuosef – 0562391 • Samar Faisal – 0562384 • Nehal Mahmoud – 0562394 • Weam Minshawi - 0643184 Supervisor: Dr-Lamia Fattouh

Agenda… • Introduction. • Problem definition. • Survey. • Needs of project • Solution. • About our program. • Result of output. • Test. • Limitations. • Conclusion. • Future work.

Introduction • With existing telephone networks nearing saturation and demand for wire and wireless services continuing to grow, telecommunication engineers are looking at technologies that will deliver sites and can satisfy the required capacity and coverage constraints. • The city data is given as a map of streets, intersection nodes coordinates, distribution of the subscribers’ loads within the city. The available cable sizes. • The process of network planning is divided into two sub problems: 1. Determining the location of the exchanges. 2. Construction of subscriber network lines from exchange to subscribers to satisfy optimization criteria and design constraints.

The Problem definition: • In many developing countries, there is tremendous demand for new business and residential telephone service. • The continuous Increasing of number of subscribers make congestion in MSAN and cause degradation in grade of service and in sometimes impossible to add new subscribers which lead to using the mobile tower. • Also, the existing of the natural obstacle is affecting on distribution the MSAN on the regions. • The responsible operator is looking to rapidly provide thousands of new subscribers with high-quality telephone service and provide the right equipment, at the right place, and at the right time to provide an acceptable grade of service with minimum number of MSANs.

The survey Telephone Network plan: • The main concept of this project is planning and drawing telephone network in area that is doesn't has telephone service. It is achieving two aims: • the first one is determining the best place of the central depending on specific criteria’s and conditions by using several of logical operations. • The second one is determining the shortest path to connect the subscriber whit the central by using shortest path algorithms. All that operation is depending on some information entered by the user. Comparison with our project: This program is different from our project because we are using the clustering technique to create groups of objects based on their features in such a way that the objects belonging to the same group’s that is after the user entered some information about assigning area using the shortest path to connect MSAN with subscribers. Finally it is planning the telephone network and prints the report about the cost.

PlanAir: • It uses a two phase clustering algorithm for wireless local loop (WLL) communication system planning that are introduced for cells determination and the locating of base station sites. • The clustering process employs two well-known algorithms: • K-Means. • Agglomerative algorithms. • The data to be clustered is the distribution of the subscribers requesting service in addition to the types of service they request (voice, ISDN, etc.). • The PlanAir software employs this algorithm in a planning subsystem alongside a GIS subsystem to attain the required planning. • The system determines both the cell size and its RF planning through the determination of its antenna height, power and type. • The RF planning is also obtained to maximize the number of subscribers within the call. These are obtained under the constraints of coverage, quality and capacity

Comparison with our project: The main different which is distinguish our project than PlanAir project is using the wire in specific fiber optics rather than wireless local loop (WLL).fiber optics is a new technology which transferring data at speeds from 622 Mbps to 2.5 Gbps per second to users and 155 Mbps to 622 Mbps to the network but WLL old technology which the telecommunication company does not use it now. In other hand in our project we use DBscan clustering algorithm we talked about it at section 2.9 but this project use k-mean algorithm which is a partition-based algorithm that takes the number of the clusters as input parameter. The algorithm organizes the objects into exactly k partitions where each partition represents a cluster.

Efficient Network Planning Using CSPw-ClARANS Clustering Algorithm: • This program use CSPw-ClARANS (Clustering with weighted Shortest Path- CLARANS) Clustering Algorithm to solve the problem of network planning and the construction of optimal cable network layouts where the weights used are the subscriber loads. Comparison with our project: • The main different which is distinguish our clustering algorithm than their clustering when applying CLARANS algorithm it clusters all regions even if it is empty or does not have density but the DBScan interest in the density by clustering it in groups.

The needs of project: There are so many reasons to building our project: • The network planning engineers need for program that support network planning to save the time and effort. • The mistakes from bad planning caused: • Increasing the cost and budget of network and decreasing the benefits. • Degrade the services. • the noise subscriber (in obstacle area) that may be on out of coverage area of MSAN. So we are going to computerize the network planning to make the planning more efficient and provide the best service to subscribers.

Solution • we use some of computer sciences to make our software more efficient and suitable to plan the telephone network. • In our project we use a combination between different branches of computer sciences: • IS (Information System): GIS (Graphical Information System) • AI (Artificial Intelligent): Data Mining Algorithms • Shortest Path Algorithm • The used tools in our program: • Database : SQL 2008 • Prigramming language: Visualbasic.NET. 2008

GIS • GIS is an information system to store map‘s information. • A map made up of entities (points, lines, polygons) that have geographic positions on the city as well as relationships to all the other entities within the layer (topology). • The data point is the projection of streets’ squares on the map. A single data point has: position (X, Y). • The line which represent the streets between two points with its related information such as (street load and length). • The candidate MSANs positions represented by coordinates. Each MSAN has the max and min capacity and limit of coverage area. • The mobile tower position represented by coordinates. • Core point: A point that represent the best location for the MSAN in that area(It is appear after clustering).

Data Mining Algorithms • Clustering is one of the data mining techniques that discover hidden information in large amount of data. Clustering objects means to categorize them based on their feature vectors. • We use two algorithms : • Density-based algorithms : DBScan (Density Based Spatial Clustering of Applications with Noise) is one of Density-based algorithms clustering algorithm which is divide the subscribers into clusters by detect arbitrary form of clusters and it can filter out the noise. • hierarchical algorithms: Agglomerative clustering algorithm is a hierarchical algorithms which it associates the points to appropriate cluster.

DBScan Algorithm The reasons for choosing DBScan algorithm: • It is a fundamental density-based clustering algorithm. • It regards clusters as dense regions of objects in the data space which are separated by regions of low density objects. • It exploits the benefit of the natural approach clustering those objects together that forms a dense region. • It is efficient even for large spatial databases. • It is able to quickly discover clusters of arbitrary size and shape. • It is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS and it outperforms CLARANS by a factor of more than 100 in terms of efficiency.

Agglomerative algorithm • It is a bottom-up approach that starts with the points as individual clusters. • At each step, merge the closest pair of clusters until only one cluster (or K-clusters) left.

Shortest Path Algorithm • Dijkestra is the shortest path algorithm that calculate the distance from one source to all destinations. • Dijkstra's algorithm has a run time of O (n2) and it can quickly finds the shortest path from a chosen source to a given destinations. • So, we use it instead of Floyd's algorithm because it is an O(n3) algorithm for finding all pairs of shortest paths.

Implementation DBScan Algorithm: • In network planning the cable length must be at maximum 2.5 km .So, we make the value of EPS take the value of shortest path from core (MSAN) to the most remote point (subscribers). The original DBSCAN Algorithm uses Euclidian distance (that means the direct distance between the MSAN and nodes); since in the real city the MSAN should be at the edge of the street whether in a garden or any position specific for that purpose, the cable should be connected directly from MSAN to nodes nearby and from node to each other. In this case the shortest path algorithm is more accurate than Euclidian distance i.e. if we depending on Euclidian distance in our calculation the distance which usually small value) between a node and one of the core (MSAN)this node may be appear as member of this cluster although the exact distance out of the cluster border consequently Euclidian distance give a wrong result .To apply shortest path we selected Dikjstra algorithm.

The DBScan classes of node from our project point review are: • Core point which is a subset of candidate MSAN location. • Noise point it is a node that impossible link to the MSAN supposed to follow it (because the load of the MSAN exceed maximum capacity 1536). In real planning all subscribers must be served so noise point is served using the mobile tower which can serve at maximum 100 subscribers because that number is the maximum subscriber who can be served by mobile tower and the engineers taking in account growth the subscriber in the future. So we conclude 100 is the optimum value of the minimum point. • Border point that belong to ascertain cluster. • Although minimum capacity of the core (MSAN) is 256 fixed phone lines (the number of subscribers) we consider the minimum number as 100 because the.

Agglomerative clustering technique: • The agglomerative is hierarchal clustering technique .It starts with the points as individual clusters and at each step merge the closest pair of clusters depends on a notion of cluster proximity. In our project, after we distribute the nodes into different clusters (each cluster has served by one MSAN) and maybe there are two cores (MSANs) are close in distance (less than 1.25 Km).So, we need to decrease the cost of constructing a new MSAN if one of them can hold the loads (subscribers) of two MSANs.

Dijkstra algorithm: • In the project, we used this algorithm to calculate shortest path from one source to many destinations; in the DBSCAN we need to calculate the shortest path from MSAN to all nodes (the reason is to determine the suitable MSANs that serve at least 100 as a minimum number of subscribers) and from one node to all MSANs (the reason is to determine the nearest suitable MSAN that will serve this node). Based on shortest we calculate the load of MSAN.

Pseudo code of our project For (i=1 to candidate No.) Calculate the shortest path from switch (i) to each node(also, the load will calculate through it). If (shortest path < 2.5) Then calculate the load = load +current load of node. If (sum of load >= 256) Then add switch to cores. End for For each node in the city select the best switch for it by calculating the shortest path between the nodes and each switch (path < 2.5) Calculate the load of each core Agglomerative: For each tow clusters If the shortest path between the cores < 1.25 Then If (sum of two clusters' load <1536) Then First assume that the first core is core of the two clusters And apply the previous algorithm for each node in the tow clusters. Second assume that the second core is core of the two clusters And apply the previous algorithm for each node in the tow clusters. If (the two cores are suitable to be a core for all nodes of tow clusters) Then Calculate the summation of (load X distance) for each core And choose the appropriate core depend on minimum value. Else let the suitable one the core for tow clusters.

About our Program • Input of Program • The map. • Node (subscriber). • Candidate MSAN. • Streets and its information(load). • The real length of first street to compute the pixel measurement scale. • Mobile Tower. • Output of Program • Planned network that contain: • Suitable selected MSAN from the candidate one. • Mark the MSAN that has exceed its load and recommend to support it with mobile tower. • The group of subscribers for each MSANs. • The number of clusters that network need. • Associate the noise node with nearest mobile tower. • In Some situation; merge two cluster into one

Result of O/P • Here is the map and its GIS component before plan

Here is the map after apply cluster algorithm and Dijkestra with out agglomerative

Here is the network after applying agglomerative algorithm

Another net work that have some MSAN with exceed load Before plan After plan

Testing • After planning is completed, the expert is commending on the result of applying the algorithms from side of accuracy of planning and the consumed time to accomplish this process in relation of the time consumed by using the manual way.

Limitation The most effected limitation on our project was the time, which it prevents us to make many ideas and works that we want to do. The limited time that we have is avoided us to expand our project to manipulate a large region. Also, it is discourage us to experiment more than one algorithm and comparing between the results of them then use the effective one.

conclusion Clustering analysis is one of the major tasks in various research areas. The clustering aims at identifying and extracting significant groups in underlying data. Based on certain clustering criteria the data are grouped so that data points in a cluster are more similar to each other than points in different clusters. In our project we studied the problem of Increasing the number of subscribers make congestion in MSAN and cause degradation in grade of service and in some time impossible to add new subscribers which lead to using the mobile tower. Also, the existing of the natural obstacle is affecting on distribution the MSAN on the regions.

Future work • In the future, we want to improve our system and add some feature to it. • We have some general idea about what we want to do: • 1- Comparing between clustering algorithms practically and determine which supposed to give more precise result. • 2- Expand our project to manipulate much more regions that clarify the efficient factor of the algorithm.

Thank you

Fixed telephone network planning