Predictive allocation of resources in the LTE uplink based on maximum likelihood estimation of event propagation characteristics for M2M applications

We propose a predictive resource allocation scheme for the LTE uplink based upon Maximum Likelihood Estimation of event propagation characteristics for M2M/Smart Grid applications. The LTE eNodeB estimates the inter-sensor propagation time of a disturbance using the pattern and timing of received Scheduling Requests (SRs) from sensors and then proceeds to predict the time at which the disturbance will reach downstream sensors, facilitating predictive uplink grants for these sensors in order to reduce the mean latency of their uplink data packets by up to 50% (according to a performance analysis) compared to the existing standard reactive LTE uplink resource allocation scheme. A further benefit is that when a predictive resource allocation is successful, the sensor does not need to send an SR, thereby freeing up uplink resources which can be critical with M2M communications. We consider various transition strategies from the estimation to prediction phases which reflect the compromise between estimation speed and accuracy, and also examine the concept of early and late prediction.