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.
From another view, the works in @cite_21 @cite_1 aim to increase resource utilization considering the coarse granularity of resources allocated for individual node. The authors in @cite_21 propose an adaptive SPS scheme to adjust the resources allocated in the next transmission via buffer reports. To further utilize unused resources, resource delegation as a promising technique can increase overall throughput by leveraging from D2D @cite_1 , where partially unused scheduling grants that were originally assigned in a semi-persistent manner could be granted to other devices. However, additional access delays may be brought in with extra D2D and buffer information exchange. Moreover, due to the small data transmissions feature in factory, BS can support an unique small resource size for MTC devices so that packets can be transmitted within a RB @cite_4 .
@cite_21 @cite_1 @cite_4
2508597706 2122616116
Next generation cellular networks are envisioned to widely enable machine-to-machine (M2M) communication. For many applications in the M2M domain, deterministic communication latencies and high reliability are of very high importance. As a consequence, device-to-device (D2D) communication is a expected to play a key role in order to avoid from having to route the entire data through a centralized base station. Considering the large number of devices but relatively small payload lengths of many M2M applications, LTE currently fails to efficiently utilize the available radio resources. This is primarily because the granularity of resources that can be allocated to a single user is too coarse, resulting in parts of the allocation being wasted. In this paper, we present a hierarchical radio resource management scheme that allows ordinary cellular users to reuse and exploit the otherwise unused portion of a D2D scheduling grant. We show that our resource reuse scheme improves the uplink throughput of cellular users between 10 and 30 , depending on the actual number of D2D users, compared to current allocation schemes. 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.