⚡ electrical engineering

Adaptive Contention-based Random Access for Uplink Reporting in 3GPP Ambient IoT Networks

This paper proposes an energy-harvesting-aware access control mechanism for 3GPP Ambient IoT networks that dynamically regulates device contention via reader-broadcasted probabilities, thereby significantly improving uplink reporting efficiency and scalability in dense, battery-less deployments by minimizing collisions and paging rounds.

Original authors: David E. Ruiz-Guirola, Samer Nasser, Bikramjit Singh, Henrique Duarte Moura, Andrey Belogaev, Jeroen Famaey, Efstathios Katranaras, Mahdi Shahabi, Onel L. A. Lopez

Published 2026-05-07

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: David E. Ruiz-Guirola, Samer Nasser, Bikramjit Singh, Henrique Duarte Moura, Andrey Belogaev, Jeroen Famaey, Efstathios Katranaras, Mahdi Shahabi, Onel L. A. Lopez

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: The "Battery-Free" Party

Imagine a massive party where thousands of guests (these are your Ambient IoT devices) are trying to talk to the host (the Reader).

The catch? These guests don't have batteries. Instead, they run on energy harvesting. Think of them like solar-powered calculators or wind-up flashlights. They only have enough energy to speak when they've "charged up" enough from their surroundings (like light or radio waves). If they haven't charged up, they are asleep and can't hear the host.

The goal of this paper is to figure out the best way for the host to get a report from these guests without causing a chaotic mess.

The Problem: The "Shout-Down" Effect

In the current standard system, the host shouts out a "Wake Up!" signal (a Paging Message).

The Chaos: Every guest who happens to be awake and charged up tries to shout back their report at the exact same time.
The Collision: If two or more guests shout at the same time, their voices overlap, and the host hears nothing but noise. This is called a collision.
The Energy Trap: When a guest shouts and gets no answer (because of a collision), they have to try again later. But shouting takes energy. If they keep shouting and failing, they run out of their tiny stored energy and go back to sleep. This creates a vicious cycle: the more guests there are, the more they collide, the more energy they waste, and the fewer reports get through.

The Solution: The "Traffic Light" System

The authors propose a smart new rule for the host. Instead of just shouting "Wake up!" and hoping for the best, the host acts like a traffic controller.

Before the guests try to speak, the host sends a message that says: "Okay, I see about 50 of you are awake and charged. Since I only have space for 8 people to speak at once, I'm going to tell you all to wait with a probability of X%."

This is the EH-aware (Energy-Harvesting aware) Access Control.

The Math: The host estimates how many guests are likely to have enough energy (based on how long it's been since they last spoke and how fast they charge).
The Rule: The host calculates a "green light" percentage. If there are too many guests, the host tells most of them to sit tight. If there are few guests, the host lets more of them try.
The Result: The host ensures that, on average, only about one guest tries to speak per "slot" (time/frequency opportunity). This drastically reduces the chance of two people shouting over each other.

The Analogy: The Coffee Shop Line

Imagine a very popular coffee shop (the Reader) with only a few baristas (the Access Slots).

The Old Way (Naive Baseline): The manager yells, "Who wants coffee?" Everyone who is awake rushes the counter at once. It's a disaster. People bump into each other, orders get mixed up, and everyone gets frustrated and leaves (collisions).
The Static Way (Slotted ALOHA): The manager says, "Okay, let's split the crowd evenly." It's better, but if a sudden rush of 100 people shows up, the line is still too long, and people still bump into each other.
The New Way (EH-Aware Control): The manager looks at the crowd and the weather (energy availability). If it's a rainy day and people are slow to charge up, the manager lets more people in. If it's a sunny day and everyone is energetic and ready, the manager puts up a sign saying, "Only 1 out of every 10 people can approach the counter right now." This keeps the line moving smoothly, prevents bumping, and ensures almost everyone gets their coffee eventually without wasting their energy waiting in a jam.

What the Results Show

The researchers ran computer simulations to test this idea. Here is what they found:

Fewer Collisions: As the number of devices grew from 10 to 100, the old methods got worse and worse (more shouting, more noise). The new method kept the noise level almost exactly the same, no matter how big the crowd got.
Faster Success: Because there was less noise, the devices didn't have to keep trying and failing. They got their reports through much faster.
Less Waiting: The devices needed far fewer "rounds" of the host asking for reports before they successfully got their message across.

The Bottom Line

This paper proves that for battery-free devices, the person in charge (the Reader) needs to be a good traffic cop. By sending a simple message that tells devices how likely they are to try speaking, the system can handle huge crowds without crashing. It saves energy, reduces waiting time, and makes the whole network much more reliable.

Technical Summary: Adaptive Contention-based Random Access for Uplink Reporting in 3GPP Ambient IoT Networks

Problem Statement
Ambient Internet of Things (A-IoT) envisions ultra-low-power, battery-less devices powered by energy harvesting (EH). A critical challenge in these networks is the intermittent availability of devices due to fluctuating energy storage. Unlike traditional IoT, A-IoT devices may miss paging signals or fail to complete uplink reporting attempts because their capacitor voltage drops below the minimum threshold ( $V_{min}$ ) required for transmission. Furthermore, when devices do attempt access via Contention-Based Random Access (CBRA), they face collisions, particularly in dense deployments. Repeated access attempts during network congestion deplete stored energy, leading to increased latency and wasted resources. Existing 3GPP standardization efforts have defined the paging-triggered CBRA framework, but they lack reader-side control strategies that explicitly account for this intermittent energy availability to regulate contention.

Methodology
The authors propose a system model where a reader polls a set of $N$ A-IoT devices using a Bernoulli process. The communication follows a Device-to-Reader (D2R) phase where devices compete for Access Occasions (AOs) using a slotted ALOHA-like mechanism. The core of the methodology is an EH-aware access control mechanism implemented at the reader side.

System Modeling: The device's energy state is modeled using a capacitor charging/discharging model based on harvested current and load consumption. The device's operational state is represented as a two-state Markov chain (Idle vs. RA), conditioned on the device being energy-available ( $V \ge V_{min}$ ).
Adaptive Control Algorithm: Instead of a fixed access probability, the reader broadcasts an access probability $q_\tau$ $q_{τ}$ in each paging message.
- The reader estimates the number of energy-available devices ( $\hat{K}_{pe}(\tau)$ ) in the target group using historical data (voltage reports and attempt counts from successful transmissions) and the known EH profile.
- The access probability is calculated as $q_\tau = \min(1, R / \hat{K}_{pe}(\tau))$ , where $R$ is the number of available AOs.
- This aims to keep the expected number of contenders per AO close to one, maximizing success probability while minimizing collisions.
Evaluation: The proposed method is evaluated via Monte Carlo simulations against three baselines:
- Ideal Benchmark: Collision-free assignment (theoretical upper bound).
- Naive Baseline: All energy-available devices attempt access ( $q=1$ ).
- Static Slotted-ALOHA: Fixed probability $q = R/N$ .

Key Contributions

EH-Aware Access Control: The paper introduces a lightweight, reader-side mechanism that dynamically adjusts the access probability based on predicted device energy levels, aligning with the ongoing 3GPP A-IoT framework.
Analytical Approximation: The authors provide a compact approximation for the per-round CBRA success probability, modeling the joint effect of intermittent energy availability and reader-side control.
Performance Characterization: The study quantifies the trade-offs between reliability, resource efficiency, and access delay in dense A-IoT deployments under varying energy constraints.

Results
Numerical simulations demonstrate that the proposed EH-aware control significantly outperforms the baselines, particularly as the number of devices ( $N$ ) increases:

Collision Probability: The naive baseline ( $q=1$ ) suffers from rapidly increasing collision probabilities as $N$ grows, approaching saturation. The static slotted-ALOHA baseline reduces collisions but maintains a relatively high, constant collision level. In contrast, the proposed EH-aware method maintains a nearly constant, low collision probability across the entire range of device densities.
Resource Efficiency: The mean number of successful MSG2 receptions per used AO remains stable under the proposed method. The naive scheme shows a significant decline in efficiency due to resource consumption by collided attempts.
Access Delay: The mean number of paging rounds required for a successful report grows exponentially for the naive baseline as $N$ increases. The proposed method keeps this growth much more gradual, requiring substantially fewer paging rounds for successful reporting compared to both fixed baselines.

Significance
The paper concludes that lightweight, reader-side access control is a critical mechanism for reliable and resource-efficient reporting in A-IoT environments. By explicitly accounting for intermittent energy availability, the proposed method prevents the random-access pool from being overloaded. This approach improves scalability in dense deployments, reduces the latency associated with repeated access attempts, and minimizes energy waste, thereby addressing a key gap in current 3GPP A-IoT standardization regarding contention regulation.