Power Monitoring for Energy Efficient 5G/6G with OAI and USRP

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Application Note Number and Authors

AN-844

Authors

Bharat Agarwal and Neel Pandeya

Executive Summary

Energy efficiency is becoming a critical KPI for 5G-Advanced and 6G systems, especially in Open RAN, AI-driven PHY, and Integrated Sensing and Communication (ISAC) testbeds.

Real-time power monitoring enables:

  • Evaluation of baseband processing efficiency
  • Measurement of RF front-end power consumption
  • AI accelerator energy profiling
  • Optimization of system-level energy-per-bit metrics

This application note demonstrates how to implement accurate power monitoring in a 5G/6G testbed using NI measurement hardware and software tools.


    1. Demonstrator Scope and Overview

This demonstrator implements a measurement framework to **synchronously monitor and measure power and energy consumption** across multiple heterogeneous components of a wireless system.

      1. Device Under Test (DUT)
    • 5G/6G Base Station Prototype**, consisting of:

- Linux server (baseband processing platform) - OpenAirInterface (OAI) base station stack - NI USRP (RF front-end) - External switchable power amplifier (PA)

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    1. Demo Use Cases
      1. 1. 3GPP-Aligned Demo Use Case
    • Energy Savings via Enhanced Cell Sleep Mechanisms**

- Demonstrates energy reduction at the base station power amplifier. - Evaluates increased cell sleep opportunities. - Quantifies real-time power savings during inactive traffic periods.

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      1. 2. AI-RAN Demo Use Case
    • Energy Profiling of AI-Native vs. Traditional Receiver**

- Measures energy consumption of: 

 - AI-native base station receiver
 - Traditional (e.g., LMMSE-based) receiver

- Compares energy-per-bit and total system power. - Evaluates trade-offs between performance gains and energy cost.

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    1. Key Feature of the Measurement Framework

All power and energy measurements are:

- Fully synchronized to a common time grid - Aligned with the **500 µs slot grid of the 5G NR system**

This synchronization enables:

- Slot-level energy analysis - Accurate correlation between radio activity and power consumption - Fine-grained energy profiling of PHY processing, RF transmission, and AI inference workloads

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