Imagine a world where every gadget, from smart traffic signals to health trackers you wear, can communicate effortlessly. This idea is central to Massive Machine Type Communication (mMTC), a foundation of 5G technology and the upcoming 6G networks.

In simpler terms, mMTC aims to connect an astonishing number of Internet of Things (IoT) devices—up to 1 million per square kilometer—allowing them to send tiny amounts of data intermittently. This ability is vital for developments in various fields, including transportation, agriculture, and telemedicine.

One of the main strategies to achieve this extensive connectivity is through what’s called “grant-free” communication. In traditional cellular systems, devices must ask for permission from a base station before they can send data. In contrast, grant-free methods let devices transmit information without needing permission first.

This approach streamlines communication, making it easier and reducing the workload on both the devices and the base stations. However, there’s a significant drawback: when a lot of devices try to send data at the same time, it can cause data collisions, leading to congestion and failures in communication.

To address these challenges, a research group led by Professor Shigeo Shioda from Chiba University in Japan has created a detailed analytical model to assess how well grant-free communication systems perform. Their study, published in Computer Communications, investigates the well-known grant-free protocol called “slotted ALOHA” within the densely populated landscape of IoT devices.

Other contributors to the research included Yuki Ichimura, also from Chiba University, and Professor Takeshi Hirai from The University of Osaka’s Graduate School of Information Science and Technology.

This publication builds on a previous study that won the Best Paper Award at ACM MSWiM 2023, an esteemed international conference focused on wireless communication performance modeling.

The research team’s method involved a complex analytical model based on stochastic geometry, a mathematical approach used to study systems with randomly arranged components. They modeled a scenario where both base stations and IoT devices were distributed in a random yet statistically predictable way.

They then evaluated three versions of slotted ALOHA: a basic form, one enhanced with an interference cancellation technique called “NOMA,” and another that allows devices to adjust their signal strength through power control. Their analysis centered on two key metrics: the success rate of data transmission and the throughput of the base station (the amount of data it can receive successfully in a given time).

The results unveiled a range of complex interactions for the different ALOHA models. While the interference cancellation method improved the base station’s throughput by up to 20% in certain situations, it didn’t solve the “near-far problem.” This term describes the scenario where devices closer to the base station are more likely to succeed in transmission, while those further away struggle.

Interestingly, the research showed that the interference cancellation technique worked best for devices positioned at medium distances, not those very close or very far from the base station. Power control did manage to mitigate the near-far issue and provide fairer access for all devices, but it also led to a noticeable dip in the overall network performance.

“Our research highlights that ALOHA-based systems contend with a fundamental trade-off between fairness—ensuring all devices have equal chances to communicate regardless of their proximity to the base station—and throughput, which aims to maximize data reception from as many devices as possible,” says Prof. Shioda.

“Achieving both fairness and maximum throughput is inherently challenging.” This points to a significant issue in designing future IoT networks, indicating that relying solely on grant-free methods might not be the best way to ensure both high performance and equitable access.

These findings will provide essential insights for the development of IoT systems. Grasping the fundamental trade-offs in communication methods is key to designing next-generation networks that are both effective and fair.

“We have illuminated the inherent issues in IoT networks that are vital for the societies of the future. These challenges arise from grant-free communication schemes and might be addressed through grant-based systems. We plan to investigate this further in upcoming research,” concludes Prof. Shioda.

Exciting possibilities in these future societies include vehicle-to-everything communication, where cars and road systems share data, and remote medical services through wearable devices, where reliable communication is crucial for monitoring health metrics.