With growing pressure for profitability and complexities introduced by emerging standards like 6G, it is crucial to rethink our approach to testing methodologies and technologies.
This keynote will emphasize the vital role of test and measurement in shaping innovative RF and wireless designs, particularly in the context of energy efficient wireless systems.
Since embedding AI in RF/ Wireless is a key trend to improve the efficiency of mobile networks for power, spectrum and performance, we will explore the prototyping and benchmarking of an exemplary Base Station Neural Receiver using Open-Air-Interface with NI USRP. In particular, we will present how to synchronously monitor the power consumption of wireless systems using the NI cRIO platform. The presentation will cover power saving use cases such as the proposed 3GPP increased cell sleep opportunities by applying an optimized MAC resource allocation as well as measuring the required processing power and energy consumption of a base station neural receiver to run the embedded AI-model. Such a measurement platform enables researchers and engineers to holistically measure, study, and optimize the power and energy consumption of existing and future wireless communication systems.
With growing pressure for profitability and complexities introduced by emerging standards like 6G, it is crucial to rethink our approach to testing methodologies and technologies.
This keynote will emphasize the vital role of test and measurement in shaping innovative RF and wireless designs, particularly in the context of energy efficient wireless systems.
Since embedding AI in RF/ Wireless is a key trend to improve the efficiency of mobile networks for power, spectrum and performance, we will explore the prototyping and benchmarking of an exemplary Base Station Neural Receiver using Open-Air-Interface with NI USRP. In particular, we will present how to synchronously monitor the power consumption of wireless systems using the NI cRIO platform. The presentation will cover power saving use cases such as the proposed 3GPP increased cell sleep opportunities by applying an optimized MAC resource allocation as well as measuring the required processing power and energy consumption of a base station neural receiver to run the embedded AI-model. Such a measurement platform enables researchers and engineers to holistically measure, study, and optimize the power and energy consumption of existing and future wireless communication systems.