Open RAN to close performance gap with AI and ML  - The EE

Open RAN to close performance gap with AI and ML 

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Emerging artificial intelligence (AI) and machine learning (ML) innovations in Open RAN massive MIMO (mMIMO) solutions will play an important role in improving performance to match that of traditional RAN mMIMO, according to ABI Research. While traditional RAN vendors currently dominate the mMIMO market, momentum for Open RAN is building as the technology matures, with pioneering deployments from operators like Rakuten and DISH.

“Advanced AI and ML techniques are poised to help close the performance gap by enhancing key capabilities such as beamforming and channel estimation,” said Larbi Belkhit, a 5G, 6G and open RAN research analyst. “Integration of these models, likely in the distributed unit (DU), will be instrumental for Open RAN vendors to maximise spectrum efficiency.”

Companies such as DeepSig are already demonstrating that AI-powered software can improve Open RAN mMIMO efficiency. Its OmniPHY formula uses ML for improved channel estimation, beam optimisation and interference mitigation in 5G networks. As AI/ML matures, such answers applied to Open RAN mMIMO will boost performance and energy consumption closer to traditional RAN levels.

“The integration of AI and ML techniques, along with other innovations in energy efficiency and GPU acceleration, will accelerate performance improvements closer to traditional RAN networks. This will remove certain barriers to Open RAN adoption and pave the way for flexible, interoperable 5G deployments for network operators rather than reliance on radio network equipment from traditional vendors currently dominating the market, such as EricssonHuawei, and Nokia,” Belkhit concludes.

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