# When Simple Is Enough: Resource-Aware Heuristic versus DDQN for O-RAN Mobility Robustness

DevFeed: [When Simple Is Enough: Resource-Aware Heuristic versus DDQN for O-RAN Mobility Robustness](<https://devfeed.tech/articles/when-simple-is-enough-resource-aware-heuristic-versus-ddqn-for-o-ran-mobility-robustness-100183.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.32257>)

Author: Ali Deylamy, Nima Afraz

Published: 2026-10-26T04:00:00Z

Content type: comparison

Language: en

Sources: [arXiv: Networking](<https://devfeed.tech/sources/arxiv-networking.md>)

Topics: [MPLS-TE](<https://devfeed.tech/topics/mpls-te.md>), [computational complexity](<https://devfeed.tech/topics/computational-complexity.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>)

Tags: [comparison](<https://devfeed.tech/tags/comparison.md>), [complexity](<https://devfeed.tech/tags/complexity.md>), [cs-ni](<https://devfeed.tech/tags/cs-ni.md>), [mobility](<https://devfeed.tech/tags/mobility.md>), [outage](<https://devfeed.tech/tags/outage.md>), [performance](<https://devfeed.tech/tags/performance.md>), [resource](<https://devfeed.tech/tags/resource.md>), [robustness](<https://devfeed.tech/tags/robustness.md>), [simulation](<https://devfeed.tech/tags/simulation.md>)

## AI overview

In the three evaluated scenarios, the resource- and mobility-aware heuristic had the lowest outage and ranked first in the CRITIC-TOPSIS comparison. It combines radio quality, target-cell resources, UE mobility, and service class, with weights optimized offline using NSGA-II; its online complexity is linear and its decisions are interpretable. DDQN-Multi improved target feasibility under congestion, but required offline training and produced more handovers and ping-pong events. The comparison used paired system-level simulations with identical UE trajectories, traffic assignments, and ray-traced radio inputs. These findings indicate a favorable performance-complexity trade-off for the heuristic under the scenarios studied.

## Source excerpt

arXiv:2610.32257v1 Announce Type: new Abstract: Open Radio Access Network (O-RAN) enables mobility-management xApps, but a learned controller is not necessarily the most effective or efficient handover policy. This paper compares standard Event A3, a resource- and mobility-aware heuristic, and a Double Deep Q-Network (DDQN) through a common xApp-oriented interface and paired system-level simulation. The heuristic combines radio quality, target-cell resources, UE mobility, and service class, with weights optimized offline using NSGA-II, while DDQN-Multi uses temporal radio and resource observations. All controllers are evaluated on identical UE trajectories, traffic assignments, and ray-traced radio inputs under Base, High-Mobility, and High-UE scenarios. Performance is assessed using service, handover, resource, energy, and fairness KPIs, complemented by CRITIC--TOPSIS ranking and complexity analysis. Results show that the heuristic achieves the lowest outage in all three scenarios and ranks first in the CRITIC--TOPSIS comparison, while retaining linear online complexity and interpretable decisions. DDQN-Multi improves target feasibility under congestion but requires offline training and produces more handovers and ping-pong events. The results indicate that lightweight resource-aware control can provide a favourable performance-complexity trade-off relative to DDQN under the considered scenarios.