What are the key spectrum management challenges posed by the Internet of Things?
Posted on: February 18, 2019

The Internet of Things (IoT) has been rightly touted as the next revolution in the mobile ecosystem, and—like any revolution—it presents many challenges. As vendors demand lower-priced IoT components, the IoT will be populated by a massive number of low-cost, thin-margin, spectrum-using devices manufactured by vendors new to the wireless space. Many of these offerings will be one-off, non-compliant devices that use out-of-spec frequencies, bandwidths, and power levels.

Various protocols governing these aspects of spectrum management have been developed over the years to support IoT, but none of them have gained mass-market penetration. One limiting factor to their adoption has been a lack of ubiquitous coverage. Cellular networks provide adequate coverage, but the deployed protocols have proven to be inefficient for many IoT uses. As a result, power consumption expenses are too high for many lower-power devices.

In recent years, 3rd Generation Partnership Project (3GPP) standards such as LTE-M (LTE modified for IoT) and NarrowBand IoT (NB-IoT) have been modified to support IoT devices more efficiently. Nationwide IoT networks utilizing LTE-M are just starting to be deployed, and will support a variety of services including utility meters, vending machines, etc. This will result in a massive number of devices sending small bursts of traffic, which will increase the overall utilization of the spectrum.

While these new cellular standards and technologies will increase the number of IoT devices, they won’t be the only protocols in use. Existing protocols will continue to be used where ubiquitous coverage is not needed, leveraging unlicensed spectrum in many cases.

Regardless of the protocol, efficient spectrum sharing will continue to be challenged by increased utilization of the spectrum, the distributive nature of the IoT devices, and more short bursts of traffic, which will make it harder to sense and predict the spectrum utilization. Advanced machine-learning algorithms will be required to detect the patterns in traffic and appropriately determine opportunities to share the spectrum.

Based on LGS CEO Kevin Kelly interview with ‘Microwaves & RF’ magazine

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