May 31, 2026
5G or Bluetooth: Best for Industrial Automation?
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5G handovers are critical for ensuring AMRs (Autonomous Mobile Robots) operate smoothly in factories, warehouses, and other industrial settings. Poor handovers can disrupt tasks, delay safety commands, and cause costly downtime. Here’s what you need to know:
When managed well, 5G handovers ensure uninterrupted AMR operations, saving time, reducing costs, and improving overall efficiency.
5G Handover KPIs for AMRs: Key Metrics & Benchmarks
Keeping an eye on the right metrics can mean the difference between smooth and interrupted AMR operations. When an AMR moves across a cell boundary, each stage of the handover process can introduce delays. One critical metric to monitor is user-plane interruption time, which averages around 35ms during intra-gNB handovers.
Handover latency consists of two main phases. The first is preparation time, which starts when the AMR sends a Measurement Report and ends when it receives the RRC Reconfiguration command from the network. The second is execution time, which begins upon receiving that command and concludes when the handover is successfully completed.
A key metric here is user-plane interruption time – the brief period during which the AMR receives no data from either the source or target cell. In mobility tests for 5G, intra-gNB handovers show interruption times of approximately 35ms, staying within strict safety limits. However, delays caused by RACH access can add an extra 20–35% to the execution time.
Beyond latency, packet loss and failure rates also play a major role in handover performance.
Packet loss is a common issue, especially in high-density environments where frequent handovers occur. It happens when in-transit data is dropped during short disconnections. Poorly configured TTT (Time-to-Trigger) and HOM (Handover Margin) settings can lead to premature or delayed handovers, often resulting in Radio Link Failure (RLF).
The challenge grows in heterogeneous networks, where handovers can increase by 17% compared to macro-cell-only setups, raising the risk of failures. That said, intelligent handover mechanisms have shown promise, cutting packet loss for high-mobility devices by up to 73% and improving success rates by 80% compared to traditional approaches.
To gauge overall reliability, Mobility Robustness Metrics are particularly useful:
| Metric | What It Measures | Why It Matters for AMRs |
|---|---|---|
| Handover Rate (HOR) | Percentage of successful handovers | A baseline indicator of how well the network supports mobility |
| Ping-Pong Rate (HOPP) | Rate of immediate return to the source cell | High rates can cause jitter and delays in AMR commands |
| Unnecessary Handover Rate (UHO) | Handovers that fail to improve signal quality | Consumes signalling resources; averages 14.83% in static-threshold networks |
In some commercial networks, ping-pong rates have reached as high as 72.45%. For AMRs, this could lead to constant switching between base stations, disrupting stable connections and overall operations.
Finally, evaluating network performance during AMR movement – focusing on throughput, latency, and jitter – helps complete the picture.
Performance during movement is just as critical as during handovers. Throughput often drops near cell edges, where handovers typically occur. For example, tasks like live video streaming from onboard cameras – used for remote monitoring or quality checks – can suffer from frame loss or buffering during these drops.
End-to-end latency and jitter are equally important. Jitter, caused by variations in packet arrival times, can disrupt navigation systems that rely on consistent sensor data. Increased jitter near cell boundaries may indicate the need for fine-tuning handover parameters. Metrics like RSSI, RSRQ, and SINR are helpful for assessing connection quality after a handover.
Navigating dense factory floors, metal racking, and fleets of moving robots presents significant hurdles for 5G handover performance.
In ultra-dense 5G networks, AMRs experience handovers far more often than in traditional macro-cell setups. This constant switching pushes the AMR’s modem into high-power mode for ongoing signalling, leading to faster battery depletion. Over the course of a shift, this frequent handover activity shortens battery life and operating time.
To tackle this, the 5G RRC-Inactive state was introduced. This state allows the AMR to reduce heavy signalling during brief pauses or low-activity periods, cutting energy use while still enabling quick reconnections when needed. For fleets where uptime is critical, integrating RRC-Inactive state logic offers a practical way to conserve battery life without sacrificing performance.
The challenge doesn’t end there. Higher speeds mean AMRs cross cells more frequently, further increasing battery drain. This issue also places additional strain on network resources, as explored in the next section.
Every handover generates control messages – measurement reports, authentication processes, and RRC reconfigurations. In a facility with dozens of AMRs operating simultaneously, these signalling demands can consume a large portion of the available 5G bandwidth, putting stress on core network components like the Access and Mobility Function (AMF).
"Increasing the number of handovers and authentications between BSs increases the number of controlling messages which subsequently causes the reduction in the operational performance of the system and increasing the delay." – Springer Nature, Journal on Wireless Communications and Networking
The problem becomes worse with ping-pong handovers – when an AMR rapidly switches back and forth between two base stations. This doesn’t just waste signalling resources; it also disrupts the network’s efficiency.
"This phenomenon [ping-pong handover] increases overall handover time and signalling overhead, leading to signal interruptions, decreased handover efficiency, and higher costs associated with dense network deployment." – MDPI Computers
One effective solution is to adopt Xn-based handovers, where control messages are exchanged directly between base stations (gNodeBs) rather than being routed through the core network. These handovers are about six times faster compared to core-network-based (NG) handovers. For larger AMR fleets, group handover protocols can also help. By treating clusters of robots moving together as a single entity, group handovers can significantly reduce the overall signalling load on the network.
Accurate tracking and fine-tuning of handover metrics are essential for keeping Autonomous Mobile Robot (AMR) operations running smoothly. Here’s how to approach this effectively.
To measure handover performance accurately, you need reliable diagnostic tools. For instance:
A typical workflow might start with RF monitoring to identify weak zones, followed by capturing RRC MeasurementReports to confirm handover triggers. Engineers then check mobility parameters (e.g., A3 event offset set to 3 dB and TTT at 160 ms), before conducting drive tests to validate handover points.
Private 5G networks now use Network Management Systems (NMS) with gRPC-based streaming telemetry, offering performance updates every 1–10 seconds. This near-real-time monitoring allows teams to quickly spot and address issues, such as sudden spikes in handover failures, before they disrupt AMR operations.
Key handover KPIs to monitor include:
| Handover KPI | Definition | Typical Threshold |
|---|---|---|
| HO Execution Time | Time between RRC Reconfiguration and HO Success Event | <50 ms for AMRs |
| User-Plane Interruption | Duration when no data is received by the AMR during handover | <50 ms |
| Intra-gNB HO Success Rate | Percentage of successful intra-gNB handovers | ≥ 98.0% |
| Inter-gNB HO Success Rate | Percentage of successful inter-gNB handovers | ≥ 96.0% |
These benchmarks help evaluate handover performance, with real-world tests confirming their validity.
For example, the 5GMED project conducted mobility drive tests at the Castelloli test track in January 2023. Using QXDM and a Quectel RG520N-EU evaluation board, the team achieved a stable user-plane handover interruption time of around 35 ms, well within the target of <50 ms for mobile robots. Interestingly, 35% of the total handover execution time was attributed to RACH access delay, underscoring the importance of optimising RACH processes, such as through Contention-Free Random Access (CFRA), alongside radio parameter adjustments.
Once KPIs are evaluated, controlled testing with specialised tools can further enhance handover performance.
Firecell’s Orion Labkit (priced at £11,900 with a £5,580 annual fee) is ideal for indoor testing over areas up to 1,000 m². It allows engineers to observe 5G modem behaviour during cell reselection and connected handovers in a controlled environment.
For larger-scale testing, the Pegasus Network supports deployments over 10,000 m² with up to ten 5G access points. This solution includes an SLA and a user-friendly management interface. Firecell’s network monitoring tools provide direct visibility into KPIs, enabling adjustments tailored to specific RF environments, whether in a steel-framed warehouse, port, or factory.
"Having full visibility on the core and radio access network (RAN) and their different interfaces is unique and one of the key factors behind NIST choosing Firecell’s Labkit." – Dr. Richard Candell, Leader of the Industrial Wireless Systems Project, NIST
Firecell also offers a free site survey and spectrum check before deployment. This helps identify interference sources, such as cranes, forklifts, or metal racking, that could disrupt handovers. Combined with Quality of Service (QoS) prioritisation (ensuring safety and command signals take precedence over less critical data) and SIM-based per-device identity management, this approach provides a structured path from initial testing to a fully optimised 5G environment for AMR fleets.
For autonomous mobile robots (AMRs), a failed handover isn’t just a minor network issue – it can lead to an emergency stop, delays, or even safety risks. The difference between a fleet moving smoothly at 2.5 m/s and one slowed down to 1.5 m/s often comes down to how well handovers are managed.
The importance of handover performance becomes clear when looking at the key metrics: handover latency, packet loss, throughput, and success rates. These indicators determine if a 5G network can handle the demands of industrial mobility. For example, maintaining an interruption time under 50 ms and achieving an inter-gNB Handover Success Rate of at least 96% aren’t just technical goals – they’re essential for keeping safety systems and motion controls functioning properly on the factory floor.
Private 5G networks also bring an edge by requiring five to 20 times fewer access points compared to Wi-Fi, which reduces handover frequency and boosts reliability. This kind of dependable, low-interruption performance is critical in environments where downtime directly impacts productivity. Features like make-before-break connectivity, prioritisation of critical commands, and SIM-based identity further ensure consistent performance that Wi-Fi simply can’t match.
Ping-pong handovers occur when a device constantly shifts back and forth between two network cells. This behaviour is often triggered by temporary signal fluctuations or incorrect network configurations. A common culprit is a low handover offset, which can lead to transitions happening too early. In dual-connectivity scenarios, improper thresholds for adding or removing network legs can also result in this back-and-forth movement. Fine-tuning parameters such as the A3 offset and time-to-trigger (TTT) can help stabilise the connection and minimise these repetitive switches.
To cut down RACH delay during AMR handovers, the priority should be on making the switchover process smoother by reducing contention and preamble wait times. The random access procedure is often the main cause of delays, so refining this step is crucial. Additionally, positioning network functions closer to the edge can help minimise backhaul delays. Firecell’s private 5G solutions provide secure and high-performance connectivity, tailored to support these improved handover processes in industrial environments.
To assess 5G handover KPIs for AMRs, consider using tools such as QXDM/QCAT for UE-side logging, Keysight Nemo for outdoor testing, Wireshark NR for packet inspection, and specialised drive-test equipment. The key metrics to monitor include:
For connected-mode timing, focus on tracking critical aspects like handover preparation, execution, and user-plane interruption times. Tools like QXDM and Nemo are particularly useful for this analysis.
