Analysis of Status Update in Wireless Networks with Successive Interference Cancellation

TL;DR

This paper addresses a random multiple access adaptive system, in which a large number of devices send sporadic messages in non-periodic pattern, and highlights the potential of Successive Interference Cancellation and identifies an adaptive parameter setting to maximize its benefits as the level of contention on the shared channel varies.

Abstract

Data collection in an IoT environment requires simple and effective communication solutions to address resource constraints, ensure network efficiency, while achieving scalability. Efficiency is evaluated based on the timeliness of collected data (Age of Information), the energy spent per delivered unit of data, and the effectiveness in utilizing spectrum resources. This paper addresses a random multiple access adaptive system, in which a large number of devices send sporadic messages in non-periodic pattern. In particular, our analysis highlights the potential of Successive Interference Cancellation and identifies an adaptive parameter setting to maximize its benefits as the level of contention on the shared channel varies. An analytical model is defined, easily scalable with the number of nodes and yielding all the relevant metrics. Evidence of the accuracy of the model is given by comparing predicted results against simulations. The model is utilized to assess the trade-off between Age of Information and energy consumption, revealing a sharp relationship between the two. The considered approach lends itself to many generalizations and applications to massive machine-type communications and IoT networks.

Figures (15)

• Figure 1: Definition of the inter-departure time $Y$, the idle time between a departure and the end of the slot where the subsequent arrival occurs, $R$, and the time, where the node is busy contending and eventually transmitting, $C$. The dashed area highlights the time slot(s) where the node is backlogged, but backing off. The dark shaded area highlight the time slot where the node transmits.
• Figure 2: Definition of the time slot where an arrival occurs, $\hat{X}$, the time span since the first arrival in that slot (denoted with $A_1$) until the end of that slot, $V^\prime$, and the time span since the last arrival in that slot (denoted with $A_3$) until the end of that slot, $V$. The shaded area highlights the portion of the slot time where the tagged node is active, hence consuming energy.
• Figure 3: Optimal target SNIR $\gamma^*_k$ (normalized with respect to $\gamma_{max}$), which maximizes the sum-rate, as a function of number of backlogged nodes $k$ ($\gamma_{\text{max}} = 31$, $\epsilon = 0.1$).
• Figure 4: Optimal transmission probability $p^*_k$, which maximizes the sum-rate, as a function of number of backlogged nodes $k$ ($\gamma_{\text{max}} = 31$, $\epsilon = 0.1$).
• Figure 5: Transmission time $T_k$ as a function of number of backlogged nodes $k$ ($\gamma_{\text{max}} = 31$, $\epsilon = 0.1$).