Difference between revisions of "5G OAI Neural Receiver Testbed with USRP X410"
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Bharat Agarwal and Neel Pandeya | Bharat Agarwal and Neel Pandeya | ||
| − | == | + | ==Executive Summary== |
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| + | ===Overview=== | ||
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| + | This Application Note presents a practical, system-level benchmarking platform leveraging NI USRP software-defined radios (SDRs) and the OpenAirInterface (OAI) 5G/NR stack for evaluating AI-enhanced wireless receivers in real-time. It addresses one of the key challenges in deploying AI/ML at the physical layer-ensuring reliable system performance under real-time constraints. | ||
| + | |||
| + | ===Motivation and Context=== | ||
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| + | AI and ML techniques hold promise for improving both wireless and non-wireless KPIs across the stack, from core-level optimization (e.g., load balancing, power savings), to tightly-timed PHY/MAC innovations such as: | ||
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| + | * ML-based digital predistortion to improve power efficiency | ||
| + | * Neural receivers for channel estimation and symbol detection with improved SNR tolerance | ||
| + | * Intelligent beam and positioning prediction, even under fast channel dynamics | ||
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| + | Consortia such as 3GPP (Release-18/19) and O-RAN are actively defining how AI/ML can be incorporated into future cellular network standards. | ||
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[[Category:Application Notes]] | [[Category:Application Notes]] | ||
Revision as of 18:08, 30 October 2025
Contents
Application Note Number and Authors
AN-829
Authors
Bharat Agarwal and Neel Pandeya
Executive Summary
Overview
This Application Note presents a practical, system-level benchmarking platform leveraging NI USRP software-defined radios (SDRs) and the OpenAirInterface (OAI) 5G/NR stack for evaluating AI-enhanced wireless receivers in real-time. It addresses one of the key challenges in deploying AI/ML at the physical layer-ensuring reliable system performance under real-time constraints.
Motivation and Context
AI and ML techniques hold promise for improving both wireless and non-wireless KPIs across the stack, from core-level optimization (e.g., load balancing, power savings), to tightly-timed PHY/MAC innovations such as:
- ML-based digital predistortion to improve power efficiency
- Neural receivers for channel estimation and symbol detection with improved SNR tolerance
- Intelligent beam and positioning prediction, even under fast channel dynamics
Consortia such as 3GPP (Release-18/19) and O-RAN are actively defining how AI/ML can be incorporated into future cellular network standards.
