Showing papers by "Wan Haslina Hassan published in 2022"

Measurement analysis and performance evaluation of mobile broadband cellular networks in a populated city

Abstract: Enhanced mobile broadband is an essential target in fifth-generation (5G) networks with higher demands among service consumers. It offers very high-speed Internet connections for several geographical areas: urban, suburban, and rural. Many mobile network operators (MNOs) continuously monitor the quality of service in terms of multiple services to guarantee high network performance. To the best of our knowledge, no extensive studies and analyses are conducted on mobile broadband (MBB) services that cover various implementation scenarios and several performance metrics. This study comprehensively analyzes the existing MBB performance in an urban area: Cyberjaya City, Malaysia. The measurement data were collected through drive tests from various MNOs supporting 3G and 4G technologies: Maxis, Celcom, Digi, U Mobile, and Unifi. Several performance metrics, such as signal quality, throughput (downlink and uplink), ping, and handover, were measured during the drive tests. The data measurements were conducted in two scenarios: outdoor and indoor environments. Measurement results of the outdoor drive test demonstrate that the maximum average throughput with downlink and uplink data rates is 14.3 and 7.1 Mbps, respectively, whereas the minimum average ping and loss are 36.5 ms and 0.14, respectively, for all MNOs. However, the in-building measurements achieve an acceptable overall average data rate of 2 Mbps. This paper provides several suggestions and recommendations for MBB providers to improve their performance networks and quality of experience to meet customers’ satisfaction. Several limitations not considered in this study and can point to possible future work are presented.

Time series forecasting model of future spectrum demands for mobile broadband networks in Malaysia, Turkey, and Oman

Abstract: Mobile broadband (MBB) services are rapidly growing, causing a massive increase in mobile data traffic growth. This surge in data traffic is due to several factors (such as the massive increase of subscribers, mobile applications, etc.) which have led to the need for more bandwidth. Mobile service providers are constantly improving their network efficiency by upgrading current networks and investing in newer mobile network generations. However, these improvements will not be enough to accommodate the future spectrum demands. This paper proposes a time series forecasting model to analyze future spectrum demands based on the spectrum efficiency growth of MBB networks. This model depends on two key input data: the average spectrum efficiency per site and the number of sites per technology. The model is used to predict the spectrum efficiency growth of three countries (Turkey, Malaysia, and Oman) from 2015 to 2025. The proposed model is compared with various traditional statistical models such as the Moving Average (MA), Auto-Regression (AR), Autoregressive–Moving-Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA). The forecasted results indicate that the average spectrum efficiency and growth will continue to rise multiple times by 2025 compared to 2015. The data from this prediction model can be used as input data to forecast the required spectrum needed in future for any specific country. This study further contributes to the network planning of future mobile networks for Fifth Generation (5G) and Sixth Generation (6G) technology. The proposed model obtains higher accuracy (by 90%) compared to other models. The proposed model is also applicable to any country, especially when new wireless communication technologies emerge in future. It is customizable and scalable since spectrum regulators can add additional metrics that positively contribute towards accurately estimating future spectrum efficiency growth.

An Overview of Artificial Intelligence for 5G/6G Wireless Networks Security

Abstract: Fifth-generation (5G) wireless technologies provide a higher capability for millions of devices to be connected with low latency and high-speed transmission. However, having a wide range of enabling technologies brings additional threats. In this context, network security and privacy play an important role for 5G and beyond networks. In 5G networks, artificial intelligence (AI) techniques are useful for improving security. This paper provides an overview of AI-powered intelligent security for 5G and beyond wireless networks. We review several studies on wireless networks that utilize AI features to improve additional security vulnerabilities depending on future technologies at the physical and network levels. The paper also attempts to shed light on potential future research avenues leading to developing security and privacy for 6G networks.