Anritsu, SK Telecom, POSTECH, and Bluetest Jointly Verify AI-Driven Antenna Optimization
Anritsu, in collaboration with SKT and others, has jointly verified AI-driven antenna optimization technology.
📋 Article Processing Timeline
- 📰 Published: March 27, 2026 at 23:56
- 🔍 Collected: March 28, 2026 at 21:59 (22h 2m after Published)
- 🤖 AI Analyzed: April 15, 2026 at 01:08 (411h 8m after Collected)
Anritsu Corporation (President Hirokazu Hamada) has conducted a joint verification in collaboration with Sweden's Bluetest for the AI-driven antenna optimization technology being developed by SK Telecom (hereafter SKT), South Korea's largest telecommunications operator, and Pohang University of Science and Technology (hereafter POSTECH), which conducts cutting-edge technology research. In this verification, MIMO measurement data was acquired in a real user environment using Anritsu's Radio Communication Test Station MT8000A and Universal Wireless Test Set MT8870A to confirm the effectiveness of antenna performance optimization based on AI analysis and optimization technology.
Furthermore, the details of this verification were introduced at "MWC Barcelona 2026 (MWC 2026)," the world's largest mobile communications-related exhibition.
The purpose of this verification is to present an approach to quantitatively evaluate and improve antenna performance based on measurement data reflecting actual user environments. In addition to throughput, ECC (Error Correlation Coefficient), which indicates the degree of correlation between antennas, was used as an indicator, and evaluations were conducted under usage conditions assuming actual use, such as free space, hand-held operation, and proximity to the head. Furthermore, by considering differences in the user's grip state, performance differences in time-varying RF environments were clarified.
In the AI analysis, RF performance differences for each antenna tuner state were modeled, and the optimal antenna switching configuration was automatically identified based on the measurement data. This enabled dynamic optimization of antenna performance in line with actual user environments while maintaining communication quality. As a result of the verification, significant throughput improvements were confirmed under multiple user usage conditions for an 8-receive antenna (8Rx) configuration, and throughput improvements of up to more than double were confirmed for a 4-transmit antenna (4Tx) configuration.
Verification Overview
This verification presented an AI antenna optimization workflow based on actual measurement data acquired in an OTA (Over-the-Air) test environment. The optimization verification was visualized through the following processes:
- Analysis of RF performance fluctuations according to user usage conditions and tuner states
- Comparative evaluation of power and performance characteristics for each antenna path
- Derivation of the optimal switching state based on throughput and ECC data
- Verification of performance improvement through AI analysis based on measurement data
This approach is characterized by enabling verification that reflects actual environments, as opposed to conventional static antenna design-centric evaluations.
_________________________________________________________________________
Radio Communication Test Station MT8000A (5G NR supported)
The MT8000A is an integrated RF and protocol test station for 5G NR device verification. In this verification, it was used for MIMO performance analysis and throughput evaluation in an OTA environment.
- 4x4 / 8x8 MIMO signal generation and OTA data acquisition
- Multi-port synchronized measurement via digital IQ capture
- Throughput evaluation under controlled 5G NR link conditions
- Providing a highly reproducible and stable RF test environment
The MT8000A enabled the acquisition of highly reliable and reproducible measurement data necessary for AI analysis.
__________________________________________________________________________
Universal Wireless Test Set MT8870A
The MT8870A is a non-signaling general-purpose RF measurement platform. In this verification, it was used for evaluating RF characteristics for each antenna path and comparative measurement of switching states.
- RF power measurement for each antenna path by tuner state
- TX/RX path control and RF characteristic evaluation
- Measurement data acquisition for each antenna switching state
- Supports RF measurement in multi-port configurations
The path-specific RF characteristic data acquired by the MT8870A is utilized as fundamental data in the AI analysis.
Anritsu's products, solutions, and other information can be found on our solutions introduction page and Facebook.
Furthermore, the details of this verification were introduced at "MWC Barcelona 2026 (MWC 2026)," the world's largest mobile communications-related exhibition.
The purpose of this verification is to present an approach to quantitatively evaluate and improve antenna performance based on measurement data reflecting actual user environments. In addition to throughput, ECC (Error Correlation Coefficient), which indicates the degree of correlation between antennas, was used as an indicator, and evaluations were conducted under usage conditions assuming actual use, such as free space, hand-held operation, and proximity to the head. Furthermore, by considering differences in the user's grip state, performance differences in time-varying RF environments were clarified.
In the AI analysis, RF performance differences for each antenna tuner state were modeled, and the optimal antenna switching configuration was automatically identified based on the measurement data. This enabled dynamic optimization of antenna performance in line with actual user environments while maintaining communication quality. As a result of the verification, significant throughput improvements were confirmed under multiple user usage conditions for an 8-receive antenna (8Rx) configuration, and throughput improvements of up to more than double were confirmed for a 4-transmit antenna (4Tx) configuration.
Verification Overview
This verification presented an AI antenna optimization workflow based on actual measurement data acquired in an OTA (Over-the-Air) test environment. The optimization verification was visualized through the following processes:
- Analysis of RF performance fluctuations according to user usage conditions and tuner states
- Comparative evaluation of power and performance characteristics for each antenna path
- Derivation of the optimal switching state based on throughput and ECC data
- Verification of performance improvement through AI analysis based on measurement data
This approach is characterized by enabling verification that reflects actual environments, as opposed to conventional static antenna design-centric evaluations.
_________________________________________________________________________
Radio Communication Test Station MT8000A (5G NR supported)
The MT8000A is an integrated RF and protocol test station for 5G NR device verification. In this verification, it was used for MIMO performance analysis and throughput evaluation in an OTA environment.
- 4x4 / 8x8 MIMO signal generation and OTA data acquisition
- Multi-port synchronized measurement via digital IQ capture
- Throughput evaluation under controlled 5G NR link conditions
- Providing a highly reproducible and stable RF test environment
The MT8000A enabled the acquisition of highly reliable and reproducible measurement data necessary for AI analysis.
__________________________________________________________________________
Universal Wireless Test Set MT8870A
The MT8870A is a non-signaling general-purpose RF measurement platform. In this verification, it was used for evaluating RF characteristics for each antenna path and comparative measurement of switching states.
- RF power measurement for each antenna path by tuner state
- TX/RX path control and RF characteristic evaluation
- Measurement data acquisition for each antenna switching state
- Supports RF measurement in multi-port configurations
The path-specific RF characteristic data acquired by the MT8870A is utilized as fundamental data in the AI analysis.
Anritsu's products, solutions, and other information can be found on our solutions introduction page and Facebook.