# Asynchronous Ad Hoc Networks with Wireless Powered Cognitive Communications

## TL;DR

This paper models and analyzes an asynchronous IoT environment with primary grid-powered ad hoc networks and secondary wireless-powered cognitive networks, providing analytical expressions for key performance metrics using stochastic geometry.

## Contribution

It introduces a novel analytical framework for asynchronous primary and secondary ad hoc networks with energy harvesting, deriving performance metrics with closed-form expressions.

## Key findings

- Closed-form expressions for coverage probability and spatial throughput.
- Insights into the impact of system parameters on network performance.
- Analytical characterization of asynchronous cognitive ad hoc networks.

## Abstract

Over the recent years, the proliferation of smart devices and their applications has led to a rapid evolution of the concept of the Internet of Things (IoT), advancing large scale machine type networks which are characterized by sporadic transmissions of short packets. In contrast to typical communication models and in order to capture a realistic IoT environment, we study an asynchronous channel access performed by a primary ad hoc network underlaid with a cognitive secondary wireless-powered ad hoc network. Specifically, we consider that the primary transmitters are connected to the power grid and employ asynchronous transmissions. On the other hand, the cognitive secondary transmitters have radio frequency energy harvesting capabilities, and their asynchronous channel access is established based on certain energy and interference based criteria. We model this sporadic channel traffic with time-space Poisson point processes and by using tools from stochastic geometry, we provide an analytical framework for the performance of this asynchronous system. In particular, we provide closed-form expressions for the information coverage probability and the spatial throughput for both networks and we derive the meta distribution of the signal-to-interference-plus-noise ratio. Finally, we present numerical results and provide important insights behind the main system parameters.