BAT: A Balanced Alternating Technique for M2M Uplink Scheduling over LTE

Abstract

Machine-to-Machine (M2M) scheduling over Long Term Evolution (LTE) networks is an essential research area for future communications. This is due to the strong expectations that M2M communications will be a main element of the overall traffic over 5G networks. The diversity of M2M applications strongly motivates studying the problem of resource allocation in the uplink direction where the M2M traffic is dominant. M2M communications impose requirements that differ from those required by Human-to-Human (H2H) communications. We present a classification of M2M scheduling techniques from the perspective of these requirements. We then propose an M2M uplink scheduling algorithm that offers a balance between throughput and delay requirements. It is also adaptive to traffic characteristics since it considers both channel state and system deadlines in an adjustable manner according to network needs. Finally, we conduct experiments to compare the performance of the proposed technique to that of other schedulers that belong to the different M2M scheduler categories.