Energy-aware and spectrum-efficient massive random access mechanism for ultra-reliable low latency communications

With the growing demand for Internet of Things (IoT) applications, supporting massive access to the media is a necessary requirement in 5G cellular networks. Accommodating the stringent requirements of Ultra-Reliable Low Latency Communications (URLLC) is a challenge in massive access to the medium. The random-access procedure is of the most challenging issues in massive IoT (mIoT) networks with URLL requirements as a high number of channel access requests result in high channel access latency or low reliability. In previous works, some solutions have been proposed to solve this challenge including grant-free access, priority-based access, and grouping nodes to restrict random access requests to groups’ leaders. Particularly, previous idea that is based on grouping, clusters the devices with similar reaction against an event to a group, which is not always applicable for various IoT applications. This research proposes a novel device grouping to improve the random-access procedure of mIoT devices with URLLC requirements. In the proposed method, device grouping is accomplished based on the analysis of devices’ traffic. A similarity index is used to obtain the similarity of time series made from historical traffic patterns of devices and then, an innovative algorithm is proposed to group the devices based on this index. Grouping devices based on similar traffic patterns, provides access to the media with less complexity and more efficiency for a large number of devices. Performance of the proposed approach is evaluated using simulations and real traffic dataset. The evaluation results show higher suitability of proposed method compared to the baseline mechanism of LTE and the previous method in terms of access failures (which affects delay and reliability) and energy consumption. For a usual setting, the channel access failure decreases by about 94 % compared to the previous method and by 0.88 % compared to LTE. The energy consumption also improves by about 1.8 % compared to LTE and by 1.2 % compared to previous method. Moreover, the results show that the proposed method is appropriate for IoT applications with regular traffic patterns.