نوع مقاله : مقاله پژوهشی

نویسندگان

• سید محسن صفوی کوهساره ¹
• سید امین حسینی سنو ²
• امیرحسین مهاجرزاده ³

¹ دانشجوی دکتری رشته کامپیوتر، دانشگاه فردوسی مشهد، مشهد، ایران

² استاد گروه مهندسی کامپیوتر، دانشگاه فردوسی مشهد، مشهد، ایران نویسنده مسئول: hosseini@um.ac.ir

³ استادیار گروه مهندسی، دانشکده سیستم و کامپیوتر ، دانشگاه صحار، عمان

چکیده

شاید بتوان گفت که مهمترین هدف اپراتورهای تلفن همراه، بیشینه کردن بهره‌وریشان است. فارغ از هزینه های عملیاتی و سرمایه گذاری، بهره برداری حداکثری از منابع موجود می تواند آنها را به این هدف نایل کند. بدین منظور، اپراتورها برای پر کردن ظرفیت های خالی شبکه خود در ساعات کم بار بودن آن، بسته های داده با قیمت های مناسب ارائه می دهند تا کاربران را برای استفاده از شبکه در این ساعات، تشویق کنند. آنها این بسته ها را بر اساس میزان متوسط بار ترافیکی کل شبکه خود در ساعات مختلف روز تنظیم می کنند. اما به این نکته مهم توجه نمی کنند که در نواحی مختلف جمعیتی یک شهر، الگوی ترافیکی در ساعات مختلف متفاوت است. در این مقاله، به صورت اتوماتیک، نواحی مختلف جمعیتی تشخیص داده شده اند. این کار به کمک خوشه بندی روی الگوی ترافیکی انجام شده است. با تشخیص نواحی جمعیتی و با توجه به الگوی ترافیکی ناحیه مربوطه، برای کاربران، نسبت به منطقه ای که بیشتر در آن حضور دارند، نحوه تخصیص بسته مناسب بررسی و تحلیل شده است. در ادامه، دیگر کاربردهای قابل استفاده این خوشه بندی برای ارائه سرویس های مختلف بیان شده است و نهایتا نتیجه گیری انجام گشته است.

کلیدواژه‌ها

• اپراتور شبکه موبایل
• بیشینه سازی بهره‌وری
• بسته داده
• خوشه بندی
• الگوی ترافیکی

موضوعات

• رویکردهای مدیریتی در حوزه کسب‌ وکار هوشمند

عنوان مقاله [English]

A Novel Method for Recommending Data Plans by Mobile Operators to Maximize Financial Efficiency

نویسندگان [English]

• seyed Mohsen Safavi koohsareh ¹
• seyed amin hosseini sano ²
• Amirhossein Mohajerzadeh ³

¹ Ph.D. Student in Computer Engineering,, Ferdowsi University of Mashhad ,Mashhad,, Iran

² Associate Professor, Engineering Dept., Faculty of Computer Engineering Ferdows i University of Mashhad Iran Corresponding Author: hosseini@um.ac.ir

³ Amir Hossein Mohajerzadeh Assistant Professor, Engineering Dept., Faculty of System and Computer Engineering, Sohar University: Oman

چکیده [English]

The primary objective of mobile network operators is arguably to maximize their efficiency. Beyond operational and investment costs, maximizing the utilization of available resources can help them achieve this goal. To this end, operators offer discounted data plans during off-peak hours to encourage users to utilize the network during these times. These data plans are typically based on the average traffic load across the entire network at different times of the day. However, they often overlook the fact that traffic patterns can vary significantly across different population areas within a city at various times. In this paper, different population areas are automatically identified using clustering based on traffic patterns. By identifying these areas and considering the traffic patterns specific to each area, the allocation of appropriate data plans for users, based on the regions they frequent, is analyzed and discussed. Additionally, other potential applications of this clustering method for offering various services are presented, followed by a conclusion.

Introduction

The number of cellular network users and their required bandwidth are continuously increasing (Ericsson, 2022). However, limited wireless frequency bands constrain network capacity, prompting operators to deploy dense base stations to reuse radio frequencies in smaller coverage areas, thereby enhancing capacity. Operators plan for peak usage, leading to base station layouts that often remain underutilized for extended periods, resulting in inefficient use of capital (equipment) and operational (energy and maintenance) costs (Liu et al., 2023). To address this, operators offer discounted plans during low-traffic periods but overlook the varying traffic patterns across urban areas, which could enable tailored offers for different regions. This paper proposes a hierarchical clustering-based method to identify and segment urban areas, design region-specific traffic-based plans, and target appropriate users. The main contribution is improving efficiency by maximizing the utilization of existing cellular networks without expanding capacity, benefiting both operators through increased revenue and users through enhanced satisfaction.

Methodology

The best approach to evaluate proposed solutions in cellular networks is to use real-world datasets from mobile operators. Cellular network logs are vast, contain sensitive user and network information, and require algorithms capable of handling large-scale data. In this study, we use a publicly available dataset (Barlacchi et al., 2015) containing telecommunication, weather, news, social media, and power grid data from Milan and Trentino, Italy, spanning November 1, 2013, to June 1, 2014. Our focus is on telecommunication data, specifically Call Detail Records (CDRs), to evaluate the proposed method.
The dataset is processed and analyzed using Python and libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib. The proposed method involves clustering base stations based on traffic patterns, designing region-specific data plans, and targeting users during low-traffic periods.
3.1. Traffic Pattern-Based Region Identification
As mentioned earlier, traffic patterns of cellular base stations vary across urban areas. These patterns are heavily influenced by the stations' locations. For example, base stations in residential areas exhibit different traffic patterns compared to those in commercial, transportation, or recreational zones (Xu et al., 2017).
Figure 1: Traffic Patterns of Base Stations in Three Different Population Zones
 
Figure 1 illustrates the traffic patterns of base stations in three different population zones over a week. Zone 3 likely corresponds to recreational areas like amusement parks, with higher traffic on weekends. Zone 1 may represent office areas, with reduced traffic on weekends, while Zone 2 could be industrial or transit areas with consistent traffic throughout the week.
To separate these zones, hierarchical clustering is employed (Abubakar et al., 2022). Instead of using Euclidean distance, which fails to distinguish adjacent zones with different traffic patterns, we use traffic time series as the clustering criterion. The chosen algorithm is agglomerative hierarchical clustering (Kassambara, 2017), as shown in Figure 2. Base stations first remove noise from their data and send average traffic data to a central node every x minutes. At the central node, Euclidean distance is used to measure traffic similarity between stations, reducing dimensionality from two dimensions (time series traffic volume) to one (distance between clusters). Over 80% of time series similarity studies use this metric, though some employ deep learning for feature extraction to improve clustering.
The Euclidean distance between two base stations' traffic time series Q and C is calculated as:




1


 




To mitigate sensitivity to variations, preprocessing steps include removing outliers, adjusting offsets, and smoothing noise using moving averages (Keogh & Pazzani, 1998).
The hierarchical clustering dendrogram (Figure 2) determines the optimal number of clusters by identifying the best cut-off line. Two strategies are proposed:

Predefine the number of clusters based on comprehensive traffic pattern analysis and use k-means clustering.
Use silhouette scoring to dynamically determine the optimal number of clusters based on traffic similarity.

We adopt the second approach, using average silhouette scores (Almeida et al., 2015) to select the optimal number of clusters. This method eliminates the need for predefined cluster counts and provides precise cluster identification.
Once clusters are identified, data plans are designed for each cluster based on their traffic patterns.
3.2. Designing Data Plans
For each cluster, the average traffic profile is calculated, and data plans are designed inversely proportional to traffic volume. The number of offers q in time interval t is determined by:




2


 




where A and B are the traffic range bounds, S is the current traffic, N is the maximum number of offers, and C is the minimum (0).
Alternative models, such as linear, exponential growth/decay, and logarithmic growth/decay, are also explored (Safavi et al., 2024), as shown in Figures 5 and 6.
3.3. Targeting Users
Users with higher overlap with low-traffic periods are prioritized for data plan offers. A user’s average monthly presence in low-traffic intervals is used to rank them. The longest data plans are assigned to users with the highest presence in low-traffic periods, ensuring efficient resource allocation.

Results

Simulations represents the method proposed in this paper, utilize 100% of the network's bandwidth capacity.
results demonstrate the optimal utilization of existing equipment and resources, which directly correlates with increased operator profitability. Those also show that the proposed method can maximize resource efficiency by approximately 40%, representing the highest possible improvement in network resource utilization
We can conclude, the proposed method significantly enhances resource utilization and operator profitability by fully leveraging network capacity. While other scenarios improve resource usage to some extent, only the proposed method achieves 100% utilization, highlighting its effectiveness in optimizing network performance and operational efficiency.
Keywords: Mobile Network Operator, Maximizing the Utilization, Cellular Data Plan, Clustering, Traffic Pattern.
 
 
 
 

کلیدواژه‌ها [English]

• mobile network operator
• maximizing the utilization
• cellular data plan
• clustering
• traffic pattern