A Novel Link Adaptation Technique for LTE based Machine Type Communication

Long Term Evolution (LTE) based Machine Type Communication (MTC) provides connectivity to a large number of low power devices for machine-to-machine (M2M) and Internet of Things (IOT) applications. MTC based communication uses data repetition across Transmit Time Intervals (TTIs) to achieve coverage enhancement with reduced power. Choosing the right repetition rate based on coverage requirements helps to achieve optimum RB utilization across a high number of devices. Therefore, repetition rate control can be considered as an additional controllable factor of LTE link adaptation algorithms in addition to variation of power and Modulation and Coding Scheme (MCS). The existing link adaptation techniques discussed in the literatures have not majorly considered gain due to repetitions and discusses only the other two dimensional factors. This paper proposes a novel Link Adaptation (LA) technique for MTC Downlink/Uplink transmission wherein, a multi-dimensional Kalman filter is used to predict based on channel conditions (target vs observed BLER an optimal value of MCS and Repetition Rate (MRR) index. Effectiveness of repetition with different MCS values has been compared for different LTE channel models through simulations. The proposed idea can further be extended to LA optimization in 5G New Radio (NR) systems. It is observed from simulation results that, using proposed algorithm, significant gain in throughput is achieved under specific traffic and channel conditions.