Cellular Radio Access Networks (RANs) are rapidly evolving towards 6G, driven by the need to reduce costs and introduce new revenue streams for operators and enterprises. In this context, AI emerges as a key enabler in solving complex RAN problems spanning both the management and application domains. Unfortunately, and despite the undeniable promise of AI, several practical challenges still remain, hindering the widespread adoption of AI applications in the RAN space. This article attempts to shed light to these challenges and argues that existing approaches in addressing them are inadequate for realizing the vision of a truly AI-native 6G network. Motivated by this lack of solutions, it proposes a generic distributed AI platform architecture, tailored to the needs of an AI-native RAN and discusses its alignment with ongoing standardization efforts.<\/p>\n","protected":false},"excerpt":{"rendered":"
Cellular Radio Access Networks (RANs) are rapidly evolving towards 6G, driven by the need to reduce costs and introduce new revenue streams for operators and enterprises. In this context, AI emerges as a key enabler in solving complex RAN problems spanning both the management and application domains. Unfortunately, and despite the undeniable promise of AI, […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193726],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[247546,248533],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-1090110","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us","msr-field-of-study-edge-computing","msr-field-of-study-ml"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2024-10-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/10\/Distributed-AI-for-RAN-TR.pdf","id":"1090116","title":"distributed-ai-for-ran-tr","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":1090116,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2024\/10\/Distributed-AI-for-RAN-TR.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Ganesh Ananthanarayanan","user_id":31834,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Ganesh Ananthanarayanan"},{"type":"user_nicename","value":"Xenofon Foukas","user_id":39276,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Xenofon Foukas"},{"type":"user_nicename","value":"Bozidar Radunovic","user_id":31286,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Bozidar Radunovic"},{"type":"user_nicename","value":"Yongguang Zhang","user_id":34995,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Yongguang Zhang"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[715138,1089753],"msr_project":[922440],"publication":[],"video":[],"download":[],"msr_publication_type":"unpublished","related_content":{"projects":[{"ID":922440,"post_title":"Project Janus: Programmable RAN and Platform","post_name":"programmable-ran-platform","post_type":"msr-project","post_date":"2023-02-24 04:30:21","post_modified":"2024-10-08 06:33:47","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/programmable-ran-platform\/","post_excerpt":"Programmable RAN and Platform Check our latest MWC 2024 announcement and the demo videos: Virtualization of telco networks is beginning to happen in Radio Access Networks (RAN), especially regarding Open Radio Access Networks (O-RAN). However, there are some obstacles to this trend. The current O-RAN architecture limits programmability to predefined telemetry and control that an xApp can access through existing, standardized service models which may limit the pace of innovation. 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