Data-oriented Coordinated Uplink Transmission for Massive IoT System

TL;DR

Results show that cooperation between clustered IoT devices may provide notable benefits in terms of increased range, but it is noticed that the performance is heavily depending on the strength of the static channel component in the CKM based cooperation.

Abstract

Recently, the paradigm of massive ultra-reliable low-latency IoT communications (URLLC-IoT) has gained growing interest. Reliable delay-critical uplink transmission in 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. 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 in transmission. In both scenarios we also model and analyse the impact of erroneous information. In the performance evaluation we apply the recently introduced data-oriented approach that has gathered significant attention in the context of short-packet transmissions. Specifically, it introduces a transient performance metric for small data transmissions, 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. 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. Analytic results are verified against simulations, showing only minor differences at low probability levels.

Figures (10)

• Figure 1: The illustration of DOR and outage probability.
• Figure 2: Illustration of IoT system: IoT devices in a cluster communicate with the ICU, coordinated by a CH.
• Figure 3: Illustration of the communication protocol depicting the coordination phase, where the CH prepares selected IoT devices for transmission and engages in control messaging with the ICU.
• Figure 4: Outage probability for CKM-based phasing when $|\delta|=20$ and Gaussian phase error with standard deviation $\sigma_\epsilon=20^{\circ}$ is assumed. Curves: $\nu=-6$dB (+), $\nu=-3$dB ($\nabla$), $\nu=0$dB (*), $\nu=3$dB (o), $\nu=6$dB (x), $\nu=9$dB ($\Delta$). Ticks refer to simulated values and solid curves are plotted using analytic formulae. The dotted curve refers to the outage probability when selection diversity over 20 devices is applied and dashed curve refers to the Rayleigh fading case.
• Figure 5: Outage probability for CKM-based phasing when $|\delta|=20$, $\nu=6$dB and $\sigma_\epsilon=1^{\circ}$ ($\Delta$), $\sigma_\epsilon=10^{\circ}$ (x), $\sigma_\epsilon=20^{\circ}$ (o) and $\sigma_\epsilon=30^{\circ}$ (*). Ticks refer to simulated values and solid curves are plotted using analytic formulae.