Browsing by Author "Jaffri, Zain ul Abidin"

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Economical and sustainable power solution for remote cellular network sites through renewable energy
(IEEE, 2017) Kabir, Asif; Kitindi, Edvin J.; Jaffri, Zain ul Abidin; Rehman, Gohar; Ubaid, Faisal Bin; Iqbal, M. Shahid
Mobile network operators (MNOs) are always striving to enhance the network coverage and for provision of services to remote rural areas. However, power supply to the infrastructures is a main challenge to the MNOs especially in terms of sustainability, economic optimum and green energy in developing countries like Pakistan for the growth of cellular networks. Renewable energy (RE) based solutions for cellular operators not only provide numerous profits but it also reduces the overall CO2 emissions. This paper presents the idea of the PV-Solar system along with grid power to provide economic and environmental friendly energy model for the remote base station and community. Feasibility of the proposed system is checked via HOMER software. Analysis and simulation results shows that the proposed model is optimal and energy efficient solution for next-generation cellular network (5G) in context with different scenarios.
User aware edge caching in 5G wireless networks
(IJCSNS, 2018) Kabir, Asif; Iqbal, M.Shahid; Jaffri, Zain ul Abidin; Rathore, Shoujat Ali; Kitindi, Edvin J.; Rehman, Gohar
Wireless technology has become an ultimate weapon in today’s world. Caching has emerged as a vital tool in modern communication systems for reducing peak data rates by allowing popular files to be pre-fetched and then stored at the edge of the network. Caching at small cell base stations has recently been proposed to avoid bottlenecks in the limited capacity backhaul connection link to the core network. For predicting the popularity of the content, we need to analyze the behavior of the user, understanding collectively the behavior beneficial for content trend forecasting and improve network performance. The proposed model predicts the intensity of human emotions through social media (Twitter) and the classifier evaluates the features which are related to user behaviors and, finally, values of features are pushed to the user profile. We further demonstrate how emotions extracted from Twitter can be utilized to improve the forecasting, describing things in a new way which can further be exploited as an optimization basis for network performance enhancement.