Compressive Sensing for Massive Machine-to-Machine Connections in 5G Mobile Communications

Title of Project

Compressive Sensing for Massive Machine-to-Machine Connections in 5G Mobile Communications

Name of Advisor/s

Wei Xiang

College

College of Science & Engineering

Summary of Project

Machine-to-Machine (M2M) communication is expected to grow exponentially in the near future, fostered by the massive deployment of sensors, actuators, RFID tags, smart metering, and other Machine-Type Devices (MTDs). Future M2M communications need to support a massive number of devices communicating with each other with little or no human intervention. Random access techniques were originally proposed to enable M2M multiple access, but suffer from severe congestion and access delay in an M2M system with a large number of devices. On the other hand, compressive sensing (CS) theory has been widely used in wireless networks, and has shown great performance improvements in terms of network lifetime, energy efficiency and overall system throughput. This projects involves investigating novel multiple access scheme for M2M communications based on compressive sensing theory to efficiently minimize the access delay and satisfy the delay requirement for each device.

Key Words

Compressive sensing, Machine-to-Machine Communications, Multiple Access

Would suit an applicant who

has completed a first-class Honours or a Masters degree in Electrical and Electronic, Communications, Computer Systems, or Software Engineering