This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.
This paper presents a machine learning-based approach to identify and separate partial discharge (PD) and two typical pulse interference (PI) signals in the vehicle-mounted cable terminations of high-speed electric multiple units (EMUs). First, a test platform was established to…
In the wake of the COVID-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police’s Directorate of Cyber Crime is expe…
Wireless power transfer (WPT) has become a crucial feature in numerous electronic devices, electric appliances, and electric vehicles. However, traditional design methods for WPT suffer from numerous drawbacks, such as time-consuming computations and high error counts due to inac…
Free space optical (FSO) communication, which has the potential to meet the demand for high-data-rate communications between satellites and ground stations, requires accurate alignment between the transmitter and receiver to establish a line-of-sight channel link. In this paper,…
This work presents the development of a wrist-worn wireless sensor system for high-accuracy indoor human zone tracking. The proposed system employs machine learning techniques to combine data from multiple sources, including a Received Signal Strength Indicator (RSSI) from wirele…
With the rapid expansion and ubiquitous presence of the Internet of Things (IoT), the proliferation of IoT devices has reached unprecedented levels, heightening concerns about IoT security. Intrusion detection based on deep learning has become a crucial approach for safeguarding…