Energy Efficiency and Latency Optimization for IoT URLLC and mMTC Use Cases

The Internet of Things (IoT) is an essential part of 5G, Beyond-5G (B5G), and 6G systems; it has several applications in two of the principal 5G use cases, namely ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC), and in their successors within B5G and 6G: extreme ultra-reliable low-latency communication (eURLLC) and ultra-massive machine-type communication (umMTC). IoT systems, which are characterized by narrow bandwidths, have stringent requirements owing to the specific nature of their applications and use cases. The purpose of this study is to investigate and jointly optimize the energy efficiency (EE) and latency through resource allocation for IoT cellular systems. With regard to the contributions, in this study we investigated the optimization of EE in narrowband IoT systems, compared resource unit configurations (RUCs), jointly formulated the optimization of EE and latency, and introduced a suboptimal but efficient algorithm. More precisely, as the EE performance of various resource unit configurations has not been exhaustively investigated in the current state of the art, we analyzed and compared the EE of RUCs. The results show vast differences in performance between RUCs. For example, in terms of EE, the best RUC has an EE more than 80 times higher than the worst, which illustrates the importance of this investigation. We then proposed a scheduler based on the shortest job first (SJF) for minimum latency allocation, and another scheduler based on a joint evaluation of EE and latency. With respect to conventional techniques, these schedulers achieve a better trade-off between latency reduction and gain in terms of EE for a wider range of parameter configurations in multi-cellular layouts. The study demonstrates that in the presence of repetitions, algorithms that achieve high EE will mostly achieve low latency.