ns3-oran Simulator for O-RAN Energy Modeling

• ns3-oran Simulator is an extension to ns-3 that models O-RAN components with accurate, hardware-level RU energy consumption assessment.
• It leverages a detailed power consumption model capturing active and sleep modes, including nonlinear scaling and loss factors.
• The simulator integrates xApp-driven control with ns-3’s energy tracking to enable dynamic power management and network optimization.

The ns3-oran Simulator is an extension of the ns-3 network simulation platform designed for rigorous Open Radio Access Network (O-RAN) research and prototyping. It provides mechanisms for modeling, simulating, and managing the behavior and efficiency of O-RAN components, particularly with fine-grained, hardware-level Radio Unit (RU) energy consumption assessment. The simulator leverages xApp-driven control loops to enable dynamic adaptation and experimentation with disaggregated, programmable network functions.

1. Architecture and Scope

ns3-oran is explicitly developed as a tailored extension to the ns-3 discrete-event network simulator, with emphasis on the needs of O-RAN systems (Wadud et al., 13 Sep 2025). The simulator integrates:

• A detailed RU-centric energy model that encompasses both active and sleep modes.
• Interfaces for xApp control to dynamically adjust RU power states and operating parameters.
• Direct linkage to ns-3’s native energy tracking infrastructure, facilitating per-node energy accounting within full-stack wireless simulation scenarios.

This approach distinguishes ns3-oran from legacy frameworks, such as EARTH or VBS-DRX, by modeling O-RAN modularity and the real-time interplay between hardware, protocol stack, and control logic.

2. Radio Unit Power Consumption Model

The simulator’s core innovation is its granular RU power modeling, which is parameterized by hardware-level features (Wadud et al., 13 Sep 2025). The RU’s total power consumption $P_{\mathrm{RU,total}}$ is characterized by:

$$P_{\mathrm{RU,total}} = \begin{cases} P_{\mathrm{active}}, & \text{if RU is active} \ P_{\mathrm{standby}}, & \text{if RU is in sleep mode} \ \end{cases}$$

In active mode, the model captures nonlinear scaling effects, inefficiencies, and per-transceiver contributions:

$$P_{\mathrm{active}} = n_{\mathrm{trx}} \frac{P_{\mathrm{PA}} + P_0}{(1-\delta_{\mathrm{DC}})(1-\delta_{\mathrm{MS}})(1-\delta_{\mathrm{cool}})}$$

where:

• $n_{\mathrm{trx}}$ = number of transceivers
• $P_{\mathrm{PA}}$ = power amplifier consumption
• $P_0$ = summation of fixed components (RF, baseband, mmWave overheads)
• $$\delta_{\mathrm{DC}}, \delta_{\mathrm{MS}}, \delta_{\mathrm{cool}}$$ = loss factors (DC inefficiency, miscellaneous, cooling loss)

Power amplifier inefficiency and feeder losses are modeled as:

$$P_{\mathrm{PA}} = \frac{P_{\mathrm{tx}}}{\eta_{\mathrm{PA}}} (1 - \delta_{\mathrm{af}})$$

During sleep:

$$P_{\mathrm{standby}} = n_{\mathrm{trx}} \cdot P_{\mathrm{sleep}}$$

Current is then computed:

$$I_{\mathrm{RU,total}} = P_{\mathrm{RU,total}} / V_{\mathrm{DC}}$$

This parameterization allows for simulation of realistic nonlinear behavior as a function of transmit power and hardware settings.

3. Comparison to Alternative Energy Modeling Frameworks

Distinct from EARTH (which concentrates on base station/baseband efficiency) and VBS-DRX approaches, the ns3-oran model is organized around the RU entity. Explicit inclusion of transceiver count, power amplifier inefficiency, mmWave-specific overhead, cooling, feeder losses, and standby modes enables simulation of highly modular, disaggregated network architectures which are emblematic of O-RAN (Wadud et al., 13 Sep 2025). A plausible implication is that this finer granularity affords better accuracy for energy management strategizing and hardware optimization than prior approaches.

4. Simulation Workflow and Numerical Validation

The modeling framework is validated through simulation of a two-cell LTE scenario with user equipment (UE) handovers and varied transmit power settings. This demonstrates that RU energy consumption $E$ increases nonlinearly with $P_{\mathrm{tx}}$, exhibiting gradual growth at low power and steeper increases above 30 dBm. Energy efficiency $\eta$ (in kilobits per joule) reaches a peak near 20 dBm and then decreases inversely with transmit power—identifying an optimal operating point for RUs in typical O-RAN deployments.

Gradient analyses of $E$ and $\eta$ against $P_{\mathrm{tx}}$ underscore the dominance of nonlinearities imparted by PA inefficiency and static overheads. This suggests the model’s suitability for optimizing RU design and operational policies in energy-constrained environments.

5. Integration and Control via ns-3 and xApps

The power consumption model is embedded within ns-3’s energy tracking system using modules such as BasicEnergySourceHelper and SimpleDeviceEnergyModel (Wadud et al., 13 Sep 2025). The function CalculateRUCurrent() interfaces directly with the PHY layer (e.g., LteEnbPhy) to compute and update dynamic RU current draw in real time.

This architecture opens the RU model to closed-loop xApp control via the O-RAN Near-RT RIC, enabling research into energy-aware scheduling, dynamic power adaptation, and sleep mode activation driven by live network measurements.

6. Applications and Future Directions

The ns3-oran simulator’s modular energy modeling is directly relevant for O-RAN operators and researchers concerned with energy efficiency, cost reduction, and sustainability. Key application areas include:

• Identification of hardware and operational inefficiencies for cost-effective deployment.
• Dynamic RU power management and sleep mode strategies via xApp logic.
• Adaptation to 5G-Advanced and anticipated 6G scenarios (e.g., massive MIMO, mmWave small cells, energy harvesting).
• Investigation of energy consumption patterns and their impact on network reliability, coverage, and user QoS in disaggregated architectures.

A plausible implication is that the extensibility of the ns3-oran energy model will support further expansion into renewables integration and multi-layer network optimization across future generations of wireless systems.

7. Significance and Conclusions

By explicitly modeling RU-centric energy consumption—including hardware-level nonlinearities, detailed loss factors, and sleep mode dynamics—and embedding it into a full-stack, programmable simulation environment, ns3-oran enables simulation-driven research into O-RAN energy management. Its validation, parameterization, and API integration are designed for advanced experimentation and facilitate the development and prototyping of energy-optimal xApps, distinguishing it in the landscape of academic and industrial wireless network research (Wadud et al., 13 Sep 2025).