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.
To take both latency and resource utilization into account, a predictive uplink resource allocation scheme is introduced in @cite_5 for event monitoring applications, where correlated traffic characteristics are exploited to proactively assign uplink grants to devices in lieu of waiting for them to reactively request. However, without a precise prediction strategy, it will deteriorate the resource utilization due to the wrong pre-allocation decision. Hence, pre-allocation is the key to low-latency guarantees but it must be in place to ensure efficiency.
@cite_5
2001710019
Some M2M applications such as event monitoring involve a group of devices in a vicinity that act in a co-ordinated manner. An LTE network can exploit the correlated traffic characteristics of such devices by proactively assigning resources to devices based upon the activity of neighboring devices in the same group. This can reduce latency compared to waiting for each device in the group to request resources reactively per the standard LTE protocol. In this paper, we specify a new low complexity predictive resource allocation algorithm, known as the one way algorithm, for use with delay sensitive event based M2M applications in the LTE uplink. This algorithm requires minimal incremental processing power and memory resources at the eNodeB, yet can reduce the mean uplink latency below the minimum possible value for a non-predictive resource allocation algorithm. We develop mathematical models for the probability of a prediction, the probability of a successful prediction, the probability of an unsuccessful prediction, resource usage wastage probabilities, and mean uplink latency. The validity of these models is demonstrated by comparison with the results from a simulation. The models can be used offline by network operators or online in real time by the eNodeB scheduler to optimize performance.