Driven by mission-critical applications in modern industrial systems, the 5th generation (5G) communication system is expected to provide ultra-reliable low-latency communications (URLLC) services to meet the quality of service (QoS) demands of industrial applications. However, these stringent requirements cannot be guaranteed by its conventional dynamic access scheme due to the complex signaling procedure. A promising solution to reduce the access delay is the pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, which however may lead to low spectrum utilization if the allocated resource blocks (RBs) are not used. In this paper, we aim to address this issue by developing DPre, a predictive pre-allocation framework for uplink access scheduling of delay-sensitive applications in industrial process automation. The basic idea of DPre is to explore and exploit the correlation of data acquisition and access behavior between nodes through static and dynamic learning mechanisms in order to make judicious resource per-allocation decisions. We evaluate the effectiveness of DPre based on several monitoring applications in a steel rolling production process. Simulation results demonstrate that DPre achieves better performance in terms of the prediction accuracy, which can effectively increase the rewards of those reserved resources.
5G has received increasing attention in industrial automation due to deterministic medium access. To provide critical QoS guarantees based on SPS, a series of massive access management (MAM) based on clustering are proposed in @cite_4 @cite_13 @cite_2 . MTC devices in the same cluster are only allowed to access within an allocated access grant time interval (AGTI). @cite_4 , the service rate of AGTIs is computed to ensure enormous QoS characteristics of different constant-rate MTC clusters. To guarantee statistical delay requests, a scheme is proposed in @cite_13 to get the minimum constant service rate through effective bandwidth. Due to the unpredictable nature of sporadic traffic in factory, an adaptive MAM in @cite_2 utilizes historical observations to allocate AGTIs thus avoiding any reliance on prior stochastic assumptions. However, previous AGTI-based works overlook the fact that AGTIs are too scarce to allocate for each device. Also, we cannot ensure that devices in a cluster will be triggered concurrently within an AGTI.
@cite_13 @cite_4 @cite_2
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Providing LTE connectivity to emerging Machine-to-Machine (M2M) applications imposes several challenges to the operation and optimization of current and future 3GPP mobile broadband standard releases. Scheduling in an efficient way M2M traffic over the existing LTE MAC infrastructure is decisive for the smooth evolution towards an M2M-enabled LTE system. The large number of connecting devices compared to classical LTE terminals, and their vastly diverse quality-of-service requirements, call for the design of new packet scheduling schemes tailored to the M2M paradigm. To this end, we propose low complexity and signaling scheduling policies which periodically grant access to the M2M devices. In particular, we first propose an analytical model for predicting the QoS performance of M2M services when the fixed periodic scheduling algorithm is employed. Next we propose a modification to this scheme, which exploit queueing-dynamics and finally we examine QoS-differentiation issues when devices are grouped into clusters. Interesting performance-complexity trade-offs are exposed. The results of our study may aid the system designer in tuning and optimizing M2M traffic scheduling. Realizing machine-to-machine (M2M) communications requires to construct and to manage scrupulous connections (logically and physically) from devices controllers to an enormous number of devices. By leveraging existing cellular infrastructures providing higher layers connections, the most challenging task is to efficiently manage massive accesses on the air interface. Consequently, in this letter, a massive access management (MAM) is proposed, which provides the most critical guarantees of quality-of-service (QoS), for devices. By deriving sufficient conditions of QoS guarantees, we show that the proposed MAM can effectively satisfy diverse QoS requirements, thus enabling the M2M communications over 3GPP scenarios. Machine-to-machine (M2M) communications have many applications, such as in smart grid, health monitoring alerting, and intelligent transportation. Due to unique characteristics that are different from the traditional human-to-human communications, numerous design challenges have to be tackled in M2M. A critical issue is that of dealing with the massive access of machine-type communication (MTC) devices while guaranteeing the desired quality of service (QoS). In this paper, we group MTC devices into clusters based on their QoS requirement. By utilizing the time-controlled M2M feature, we formulate the problem of massive access with QoS guarantee as a queuing problem. We derive an overflow probability estimation model by applying a large deviation principle and then propose an online-measurement-based adaptive massive access management for deciding the amount of resources allocated for each cluster. The proposed method bases the decision on the observed traffic workload with no prior knowledge of its statistics. We conduct the simulations in multicluster scenarios with distinct maximum tolerable delay and corresponding acceptable probability in a long-term evolution system. The simulation results show the adaptability of the proposed massive access management regime to the variations in the traffic workload; thus, it is capable of accommodating dynamic propagation conditions and unknown traffic characteristics, particularly in burst-by-burst communications.