Multiband Massive IoT: A Learning Approach to Infrastructure Deployment

TL;DR

This paper proposes two adaptive online methods for optimizing base station placement and frequency assignment in a multiband IoT network to maximize packet decoding probability, applicable across various environments.

Contribution

It introduces model-based and model-free online approaches for joint BS placement and band assignment in multiband IoT networks, enhancing PDP performance.

Findings

Significant improvement in packet decoding probability over baseline methods

Model-based approach has lower training complexity but may be less adaptable

Performance closely approaches the theoretical upper bound

Abstract

We consider a novel ultra-narrowband (UNB) low-power wide-area network (LPWAN) architecture design for uplink transmission of a massive number of Internet of Things (IoT) devices over multiple multiplexing bands. An IoT device can randomly choose any of the multiplexing bands to transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. Our main objective is to maximize the packet decoding probability (PDP) by optimizing the placement of the BSs and frequency assignment of BSs to multiplexing bands. We develop two online approaches that adapt to the environment based on the statistics of (un)successful packets at the BSs. The first approach is based on a predefined model of the environment, while the second approach is measurement-based model-free approach, which is applicable to any environment. The benefit of the model-based approach is a lower training complexity, at the risk of a poor fit in a model-incompatible environment. The simulation results show that our proposed approaches to band assignment and BS placement offer significant improvement in PDP over baseline random approaches and perform closely to the theoretical upper bound.