This paper analysis and investigate the performance of Mobile Broadband (MBB) cellular networks based on the drive tests for suburban areas (at four states) in Malaysia. The data were collected from three main national Mobile Network Operators (MNOs) by using unbranded Samsung Galaxy S6 smartphone handsets, while the period of data collection was between January and February. Two MBB services were considered which are the video streaming and web browsing. For each MNO, the performance data of one MBB service was collected through one a dedicated smartphone. One smartphone was used to browse three different webpages, and One smartphone was employed to stream two YouTube videos with two different resolutions. The study considered four MBB Key Performance Indicators (KPIs), namely: latency, coverage, speed and satisfaction. As per the results, the performance of Fourth Generation (4G) is found superior than that of Third Generation (3G) networks. For instance, a vMOS score of above 3.3 was achieved by 4G networks for MBB video-streaming service, while, score of below 2.6 was attained by 3G networks across all the four studied areas. In addition, it was observed that an enhancement factor of up to 2.86 and 2.83 in download speed was presented by 4G technology in case of video streaming and web page browsing respectively as compared to 3G technology. Examining the performance of current MBB networks is supportive before the deployment of the 5G network. The efficient development of 5G networks in Malaysia can be realized through these study findings, where the existing 4G infrastructures will contribute to supporting the 5G and 6G networks.

This survey paper offers a comprehensive review of the recent advances and applications of Machine Learning (ML) approaches in the interdisciplinary field of bioengineering, specifically in the realm of biosignal processing. Biosignals, including electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG), are inherently complex, presenting significant challenges such as noise, artifacts, variability, and nonlinearity in their processing. However, ML has shown promise in overcoming these hurdles, enabling the extraction of useful features and insights from these signals. The paper outlines how ML is leveraged for processing, analyzing, classifying, and interpreting biosignals for various applications, such as diagnosis, monitoring, rehabilitation, and brain-computer interfaces. Additionally, it discusses the ongoing challenges and potential future directions of ML applications in this field. Through this review, we aim to highlight the critical role of ML in enabling adaptive, personalized, and intelligent systems that interact with biosignals in real-time, with potential implications for improving patient outcomes in various medical conditions.