Using Modified Partitioning Around Medoids Clustering Technique in Mobile Network Planning

نویسندگان

  • Lamiaa Fattouh Ibrahim
  • Manal Hamed Al Harbi
چکیده

Optimization mobile radio network planning is a very complex task, as many aspects must be taken into account. Deciding upon the optimum placement for the base stations (BS’s) to achieve best services while reducing the cost is a complex task requiring vast computational resource. This paper introduces the spatial clustering to solve the Mobile Networking Planning problem. It addresses antenna placement problem or the cell planning problem, involves locating and configuring infrastructure for mobile networks by modified the original Partitioning Around Medoids PAM algorithm. M-PAM (Modified Partitioning Around Medoids) has been proposed to satisfy the requirements and constraints. Implementation of this algorithm to a real case study is presented. Experimental results and analysis indicate that the M-PAM algorithm is effective in case of heavy load distribution, and leads to minimum number of base stations, which directly affected onto the cost of planning the network. Key-words: clustering techniques, network planning, cell planning and mobile network

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Enhancing Clustering Algorithm to Plan Efficient Mobile Network

With the rapid development in mobile network effective network planning tool is needed to satisfy the need of customers. However, deciding upon the optimum placement for the base stations (BS) to achieve best services while reducing the cost is a complex task requiring vast computational resource. This paper addresses antenna placement problem or the cell planning problem, involves locating and...

متن کامل

Parallelising the k-Medoids Clustering Problem Using Space-Partitioning

The k-medoids problem is a combinatorial optimisation problem with multiples applications in Resource Allocation, Mobile Computing, Sensor Networks and Telecommunications. Real instances of this problem involve hundreds of thousands of points and thousands of medoids. Despite the proliferation of parallel architectures, this problem has been mostly tackled using sequential approaches. In this p...

متن کامل

K-medoids Clustering Using Partitioning around Medoids for Performing Face Recognition

Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different...

متن کامل

Image Compression Using Partitioning Around Medoids Clustering Algorithm

Clustering is a unsupervised learning technique. This paper presents a clustering based technique that may be applied to Image compression. The proposed technique clusters all the pixels into predetermined number of groups and produces a representative color for each group. Finally for each pixel only clusters number is stored during compression. This technique can be obtained in machine learni...

متن کامل

Centre-based Hard Clustering Algorithms for Y-str Data

This paper presents Centre-based hard clustering approaches for clustering Y-STR data. Two classical partitioning techniques: Centroid-based partitioning technique and Representative object-based partitioning technique are evaluated. The k-Means and the k-Modes algorithms are the fundamental algorithms for the centroid-based partitioning technique, whereas the k-Medoids is a representative obje...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • CoRR

دوره abs/1302.6602  شماره 

صفحات  -

تاریخ انتشار 2012