Feature Comparison and Throughput Accuracy of OMNeT++ and Riverbed Network Simulators Using a Testbed Environment

Abstract

Accurate network simulation is essential for evaluating wireless communication systems; however, the fidelity of simulation results strongly depends on the underlying modeling assumptions, particularly at the physical (PHY) layer. In this paper, we present a comparative analysis of OMNeT++ and Riverbed Modeler with respect to feature support and throughput prediction accuracy, validated against measurements obtained from a controlled wireless testbed. In this paper, we investigate simulator performance across representative scenarios, including line-of-sight (LOS), non-line-of-sight (NLOS), interference, and congestion conditions. To identify the sources of performance deviation, the analysis further examines the relationship among the signal-to-interference-plus-noise ratio (SINR), packet error rate (PER), and throughput. The results obtained show that both simulators achieve high accuracy under ideal conditions, particularly for LOS and congestion scenarios, where higher layer effects dominate or channel impairments are minimal. However, significant discrepancies arise under NLOS and interference conditions, where accurate modeling of channel dynamics and error behavior is critical. The results show that Riverbed consistently demonstrates closer agreement with testbed, owing to its continuous SINR tracking and probabilistic PER modeling. This enables smooth adaptation of throughput to varying channel conditions. In contrast, OMNeT++ exhibits step-like SINR–throughput and SINR–PER relationships caused by threshold-based abstractions, leading to noticeable inaccuracies in transitional SINR regimes (5–15 dB). Overall, this study highlights that PHY-layer abstraction fidelity is the dominant factor influencing throughput accuracy in network simulation. While OMNeT++ offers flexibility and extensibility suitable for research and prototyping, Riverbed Modeler provides more reliable performance prediction in scenarios requiring high modeling precision. Finally, we provide guidelines for best practice checklists in network simulation and model validation.