Cognitive Random Access for Internet-of-Things Networks

TL;DR

This paper proposes a cognitive random access scheme for IoT networks with spectrum sensors, using conditional interference distribution to adapt transmission probabilities and improve spectral efficiency.

Contribution

It introduces a novel cognitive random access method based on CID that accounts for spatial interference correlation in sensor-enabled IoT CR networks.

Findings

Improves area spectral efficiency over conventional ALOHA.

Effectively adapts transmission probability based on sensor interference measurements.

Demonstrates the benefit of CID-based adaptation in spectrum sharing.

Abstract

This paper focuses on cognitive radio (CR) internet- of-things (IoT) networks where spectrum sensors are deployed for IoT CR devices, which do not have enough hardware capability to identify an unoccupied spectrum by themselves. In this sensor- enabled IoT CR network, the CR devices and the sensors are separated. It induces that spectrum occupancies at locations of CR devices and sensors could be different. To handle this difference, we investigate a conditional interference distribution (CID) at the CR device for a given measured interference at the sensor. We can observe a spatial correlation of the aggregate interference distribution through the CID. Reflecting the CID, we devise a cognitive random access scheme which adaptively adjusts transmission probability with respect to the interference measurement of the sensor. Our scheme improves area spectral efficiency (ASE) compared to a conventional ALOHA and an adaptive transmission scheme which attempts to send data when the sensor measurement is lower than an interference threshold.