Enhancements to the long term evolution (LTE) standard for facilitating the Internet of things (IoT)

The world is evolving towards an Internet of Things (IoT) where a large number of devices interact to realize different applications that constitute smart electricity grids, intelligent transportation systems, ubiquitous healthcare solutions, etc. Machine Type Communications (MTC) provide the substrate for the connectivity and service mechanisms of these devices. Many services associated with the MTC applications such as smart metering and location tracking require the cellular network as the backbone for communication and are instrumental in driving the growth of the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) / LTE-Advanced (LTE-A) standards. A substantial number of MTC User Equipment (UE) hosting IoT applications are expected to be low cost, low data rate devices requiring prolonged battery life. In the downlink, the current LTE/LTE-A standards adopt Discontinuous Reception (DRX) mechanism for power reduction, which requires the UE to wake up periodically to check for a paging message from the base station. The LTE/LTEA standardization activities have identified that intricate paging decode procedures increase the energy consumption for low complexity MTC UEs, necessitating enhancements to the current mechanisms. This encourages us to investigate novel energy efficient mechanisms for LTE MTC systems. Specifically, we develop DRX with Quick Sleeping Indication (QSI), which enables the MTC UEs to go back to sleep quickly and save power, when there is no impending page from the base station. We also design the enhanced Primary Synchronization Signal (ePSS) for faster timing resynchronization, which can be used as QSI for additional improvements in the downlink energy efficiency of MTC UEs in low coverage. Further, LTE/LTE-A standardization activities in the uplink are examining different procedures to reduce UE data retransmissions for improved energy efficiency. To this end, we develop a Maximum Likelihood (ML) based uplink Carrier Frequency Offset (CFO) estimation technique for the LTE/LTE-A base station, which is robust and accurate in low coverage, enhancing the uplink energy efficiency of MTC UEs. The MTC mechanisms described in this thesis are not only simple to implement, but also require minimal changes to the present LTE/LTE-A standardization framework, promoting smooth integration into the current LTE/LTE-A networks.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat