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.
Introducing low-power Small Cells (SCs) in Heterogeneous Networks (HetNets) deployment can help in satisfying the increasing demands of mobile data rates. On/Off switching of SCs is one of the techniques introduced in LTEAdvanced to improve user throughput in HetNets via reducing interference. In this paper, we propose a novel traffic-aware user association technique which increases the throughput achieved by dynamic SC on/off switching by (12.7% to 24.7%) and reduces the energy consumed per bit by (41.1% to 60.7%) at different traffic loads. The proposed algorithm takes into consideration multiple factors including traffic load and dynamic on/off switching delays of SCs. The new technique also outperforms existing static on/off techniques at most traffic loads.
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