An enhanced mobile traffic offloading mechanism based on multiple attributes of user preferences

• Main Author: Noryusra, Md Rosele
• Format: Dissertation
• Language: English; English; English
• Published: 2025
• Subjects: TK6570 Mobile Communication System.
• Online Access: https://etd.uum.edu.my/11913/1/depositpermission.pdf; https://etd.uum.edu.my/11913/2/s828038_01.pdf; https://etd.uum.edu.my/11913/3/s828038_02.pdf; https://etd.uum.edu.my/11913/

Recent technological advancements have significantly increased the Internet’s demand, resulting in an increased load on cellular networks. Thus, the Mobile Network Operator (MNO) needs to offload part of the users to the WiFi networks. However, indiscriminate offloading without considering user preferences and mobility can degrade satisfaction and increase unnecessary offloading costs. Current offloading techniques often focus on either network or user-centric criteria, neglecting the interplay between them. This study aims to minimise the offloading ratio and cost while maximizing the offloaded user satisfaction. Thus, this study presents a novel mobility-aware offloading framework based on an Infinite-Horizon Discounted Markov Decision Process (IHDMDP). The model integrates location, user velocity, network condition, and user preferences to guarantee a rational offloading decision. By placing mobility at the core of its strategy, the approach ensures that users are only offloaded when their movement patterns and network conditions align, thereby maintaining high satisfaction and reducing costs. The model is then validated through sensitivity analysis and evaluated using performance metrics, including user satisfaction, offloading ratio, and costs. The study reveals that IHDMDP significantly outperforms other alternative approaches by a 40% increase in user satisfaction while simultaneously reducing offloading ratio and cost by over 80%, demonstrating its efficiency and cost-effectiveness. Simulation results indicate that users located in stronger signal zones are optimally chosen for offloading, whereas the model avoids transferring users from marginal zones to prevent satisfaction degradation. The study highlights the importance of user profiling and network quality parameters when selecting a suitable user to be offloaded. It has also contributed significantly to mobile network management, and this study has the potential to improve the efficiency of cellular networks.