# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
###### Abstract  
The prominence of Machine-to-Machine (M2M) communications in the future wide area communication networks place various challenges to the cellular technologies such as the Long Term Evolution (LTE) standard, owing to the large number of M2M devices generating small bursts of infrequent data packets with a wide range of delay requirements. The channel structure and Quality of Service (QoS) framework of LTE networks fail to support M2M traffic with multiple burst sizes and QoS requirements while a bottleneck often arises from the limited control resources to communicate future uplink resource allocations to the M2M devices. Moreover, many of the M2M devices are battery-powered and require a low-power consuming wide area technology for wide-spread deployments. To alleviate these issues, in this article we propose an adaptive semi-persistent scheduling (SPS) scheme for the LTE uplink which caters for multi-service M2M traffic classes with variable burst sizes and delay tolerances. Instead of adhering to the rigid LTE QoS framework, the proposed algorithm supports variation of uplink allocation sizes based on queued data length yet does not require control signaling to inform those allocations to the respective devices. Both the eNodeB and the M2M devices can determine the precise uplink resource allocation related parameters based on their mutual knowledge, thus omitting the burden of regular control signaling exchanges. Based on a control parameter, the algorithm can offer different capacities and levels of QoS satisfaction to different traffic classes. We also introduce a pre-emptive feature by which the algorithm can prioritize new traffic with low delay tolerance over ongoing delay-tolerant traffic. We also build a model for incorporating the Discontinuous Reception (DRX) mechanism in synchronization with the adaptive SPS transmissions so that the UE power consumption can be significantly lowered, thereby extending their battery lives. The simulation and performance analysis of the proposed scheme shows significant improvement over the traditional LTE scheduler in terms of QoS satisfaction, channel utilization and low power requirements of multi-service M2M traffic. LTE; Machine-to-Machine; Internet of Things; packet scheduling; channel utilization; DRX; low-power M2M; QoS +
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The 3 GPP has launched specifications defining the service requirements and corresponding system improvements for MTC to design significant device and network modifications in the latest releases of LTE [3,4]. The key challenges for massive M2M deployment include small and intermittent data bursts with variable application-specific delay and reliability requirements generated by massive number of autonomous devices. Unlike the current human-centric traffic, they incur a higher traffic volume on the uplink and the limited Quality of Service (QoS) framework supported by LTE evidently fails to address their requirements. The initial near simultaneous access attempts taken by a huge number of devices have led most of the research initiatives to focus on the random access procedure, and propose several prioritization/multiple access methods [5, 6, 7]. Nevertheless, a major system bottleneck for realizing M2M-enabled LTE networks has been paid insufficient attention which is the uplink grant conveying process in LTE and its dependency on downlink control channel resources. Once the devices are connected after successful random access procedure, each device initiated/terminated transmission has to be scheduled in the time-frequency resource grid by the LTE packet scheduler and these scheduling grants must be signaled beforehand via the Physical Downlink Control Channel (PDCCH). However, the PDCCH resources are limited as compared to the data channel resources. Therefore, in case of M2M communications involving massive number of uplink requests, the scarcity of PDCCH resources are very likely to cause under-utilization of the system capacity [8,9].