Ahmed Elhamy Mostafa joined the ECE department in September 2015 and officially started his research about “Medium Access Control and Resource Allocation for Massive IoT” in January 2016. He recently won second place in the ECE Heat portion of the 3 Minute Thesis competition for his presentation surrounding his research called, “The Middle-person in the Smart City”.
Essentially, the goal of his research is to enable the Internet of Things (IoT) in 5G communication systems. This can be done by providing channel and power allocation algorithms to support the IoT applications that require providing energy-efficient connectivity to a large number of devices simultaneously. These algorithms achieve the goals of energy-efficiency and massive connectivity by combining communication technologies (e.g., non-orthogonal multiple access (NOMA) and backscatter communications) and mathematical tools (e.g., optimization and machine learning).
The key finding of his team’s research is to design channel and power allocation algorithms based on maximizing the connectivity, which means maximizing the number of IoT devices that meet a certain performance requirement (e.g., minimum data rate threshold). In these solutions, IoT devices are encouraged to share communication channels using NOMA. The research proposes an algorithm that matches the IoT devices that can share the same channel while satisfying performance and power budget requirements and taking the deployment and channel status information into account.
This is important because in human-to-human communications (H2H), main applications, such as video streaming, require a higher communication quality (i.e., higher data rate). In their research, they highlight that for some categories of IoT applications (e.g., environmental sensing), providing a higher communication quality (i.e., higher data rate) is not the major objective. It is more important to provide a minimum communication quality for the largest number of devices, and so, he and his team primarily develop channel and power allocation algorithms that are more suitable for these IoT applications accordingly.
His team’s research shall encourage the industry to adopt NOMA and backscattering in the 5G communication standards to support massive IoT. In addition, some mobile applications may be updated/developed to help with IoT data transfer to provide energy-efficient connectivity for the IoT devices.
The team’s next steps include enhancing their proposed algorithms, which are built using mathematical models, by utilizing the data from IoT networks to develop data-driven algorithms using machine learning tools (e.g., deep learning and deep reinforcement learning). Ahmed has also earned a place to compete in the UBC 3MT semi-finals taking place on March 10!