Data-Oriented Analysis of Uplink Transmission in Massive IoT System With Limited Channel Information

Abstract

Recently, the paradigm of massive ultra-reliable low-latency Internet of Things (IoT) communications (URLLC-IoT) has gained growing interest. Reliable delay-critical uplink transmission in vehicular IoT is a challenging task since low-complex devices typically do not support multiple antennas or demanding signal processing tasks. However, in many IoT services, the data volumes are small and deployments may include massive number of devices. For this kind of setup, we consider on a clustered uplink transmission with two cooperation approaches: First, we focus on scenario where location-based channel knowledge map (CKM) is applied to enable cooperation. Second, we consider a scenario where scarce channel side-information is applied inuplink transmission. In both scenarios we also model and analyse the impact of erroneous channel information. As being different from the existing literature, in the performance evaluation, we apply the recently introduced data-oriented approach in the context of short-packet transmissions over vehicular IoT networks. Specifically, it introduces a transient performance metric for small data transmissions the so-called delay outage rate (DOR), where the amount of data and available bandwidth play crucial roles. Results show that cooperation between clustered IoT devices may provide notable benefits in terms of increased range. It is noticed that the performance is heavily depending on the strength of the static channel component in the CKM-based cooperation. Also, it is shown that the channel side-information based cooperation is robust against changes in the radio environment but sensitive to possible errors in the channel side-information. Even with large IoT device clusters, side-information errors may set a limit for the use of services assuming high-reliability and low-latency where DOR is the relevant metric. The analytical derivations are validated through corresponding Monte Carlo numerical simulations, with only minor differences at low probability values.