[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-northeastern-open-source-mmimo-ai-ran-prototype-en":3,"article-related-northeastern-open-source-mmimo-ai-ran-prototype-en":31,"series-model-release-72c84bef-e7d0-4aea-8f4f-8b5d9ded3426":85},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":22,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"72c84bef-e7d0-4aea-8f4f-8b5d9ded3426","northeastern-open-source-mmimo-ai-ran-prototype-en","Northeastern shows first open-source mMIMO AI-RAN","\u003Cp data-speakable=\"summary\">Northeastern University built the first open-source massive \u003Ca href=\"\u002Ftag\u002Fmimo\">MIMO\u003C\u002Fa> AI-RAN prototype.\u003C\u002Fp>\u003Cp>Researchers at \u003Ca href=\"https:\u002F\u002Fwww.northeastern.edu\u002F\" target=\"_blank\" rel=\"noopener\">Northeastern University\u003C\u002Fa> said they have demonstrated the first open-source prototype of a massive MIMO AI-RAN system at the university’s Institute for Intelligent Networked Systems Open6G OTIC. The testbed, shown on May 20, 2026, combines hardware from \u003Ca href=\"https:\u002F\u002Fwww.amplitechgroup.com\u002F\" target=\"_blank\" rel=\"noopener\">AmpliTech Group\u003C\u002Fa>, software from \u003Ca href=\"https:\u002F\u002Fopenairinterface.org\u002F\" target=\"_blank\" rel=\"noopener\">OpenAirInterface\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">NVIDIA\u003C\u002Fa> \u003Ca href=\"\u002Ftag\u002Fgpu\">GPU\u003C\u002Fa>-accelerated RAN processing.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>數值\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Announcement date\u003C\u002Ftd>\u003Ctd>May 20, 2026\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Radio unit\u003C\u002Ftd>\u003Ctd>64T64R O-RAN Category B\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Precoding layers\u003C\u002Ftd>\u003Ctd>4-layer CSI-feedback-based MIMO precoder\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Antenna elements\u003C\u002Ftd>\u003Ctd>64\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>System stack\u003C\u002Ftd>\u003Ctd>OAI L2+ + NVIDIA AI Aerial L1\u002FL2\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>What changed\u003C\u002Fh2>\u003Cp>The prototype ties together an AmpliTech mMIMO O-RAN Category B radio unit, \u003Ca href=\"\u002Ftag\u002Fnvidia\">NVIDIA\u003C\u002Fa> AI Aerial for layer 1 and \u003Ca href=\"\u002Ftag\u002Flayer-2\">layer 2\u003C\u002Fa> processing, and OAI’s L2+ stack into a single standards-compliant system. Northeastern said the setup uses exclusively open-source software at the higher layers and was built as a reproducible reference implementation.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779329635870-5ivk.png\" alt=\"Northeastern shows first open-source mMIMO AI-RAN\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The team also used a two-stage precoding design: a 4-layer CSI-feedback-based MIMO precoder accelerated on NVIDIA AI Aerial, plus a 64-antenna codebook-based beamformer inside the CAT-B O-RU. In testing, the system sustained throughput across multiple user equipment devices under mobility, with layer-2 beam management helping maintain performance.\u003C\u002Fp>\u003Cul>\u003Cli>Open6G OTIC had already certified the AmpliTech CAT-B RU for O-RAN conformance.\u003C\u002Fli>\u003Cli>The demo covers the full stack from physical layer to RAN control plane.\u003C\u002Fli>\u003Cli>Northeastern says the setup can be reproduced without proprietary closed-stack components.\u003C\u002Fli>\u003Cli>AmpliTech’s radio is a 64T64R massive MIMO unit.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Why it matters\u003C\u002Fh2>\u003Cp>For developers, the main signal is that high-performance massive MIMO no longer has to depend on vendor-locked integrations. The demo shows that AI-RAN functions can run on GPU-accelerated infrastructure while keeping the software stack open and interoperable.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779329642441-8nj9.png\" alt=\"Northeastern shows first open-source mMIMO AI-RAN\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>For the market, the proof point strengthens the case for Open RAN and AI-native wireless systems in 5G and 6G work. It also gives researchers and operators a reference platform for testing multi-vendor interoperability, beam management, and compute-heavy RAN workloads in realistic conditions.\u003C\u002Fp>\u003Cp>AmpliTech said the integration validates its radio technology in a disaggregated environment, while OAI framed the project as evidence that open standards can support full mMIMO AI-RAN builds. The practical question now is whether more labs and vendors can reproduce the same mix of openness, throughput, and mobility handling outside Northeastern’s testbed.\u003C\u002Fp>","Northeastern built the first open-source massive MIMO AI-RAN prototype, combining AmpliTech hardware, OAI software, and NVIDIA AI Aerial.","finance.yahoo.com","https:\u002F\u002Ffinance.yahoo.com\u002Fsectors\u002Ftechnology\u002Farticles\u002Fnortheastern-university-demonstrates-first-open-144500594.html",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779329635870-5ivk.png","model-release","en","02d9658b-c5d1-457c-8c15-be3033bfad5c",[17,18,19,20,21],"Open RAN","massive MIMO","AI-RAN","NVIDIA","OpenAirInterface",[23,24,25,26],"Northeastern demonstrated the first open-source massive MIMO AI-RAN prototype.","The system combines AmpliTech hardware, OAI software, and NVIDIA AI Aerial.","The testbed sustained throughput across multiple UEs under mobility.","The demo strengthens the case for open, reproducible 5G and 6G RAN stacks.",5,"2026-05-21T02:13:24.711464+00:00","2026-05-21T02:13:24.691+00:00","38ed61dd-a038-4945-af71-fd4a57f67a28",{"tags":32,"relatedLang":44,"relatedPosts":48},[33,35,37,39,42],{"name":19,"slug":34},"ai-ran",{"name":17,"slug":36},"open-ran",{"name":18,"slug":38},"massive-mimo",{"name":40,"slug":41},"Nvidia","nvidia",{"name":21,"slug":43},"openairinterface",{"id":15,"slug":45,"title":46,"language":47},"northeastern-open-source-mmimo-ai-ran-prototype-zh","Northeastern 首發開源 mMIMO AI-RAN","zh",[49,55,61,67,73,79],{"id":50,"slug":51,"title":52,"cover_image":53,"image_url":53,"created_at":54,"category":13},"58aa41ca-2c5f-44c6-ab07-2002473e95b1","gemini-1-5-pro-002-flash-002-2-0-flash-update-en","Gemini 1.5 Pro-002, Flash-002 and 2.0 Flash update Google 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Benchmarks","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780840981235-e7hm.png","2026-06-07T14:02:30.280485+00:00",{"id":68,"slug":69,"title":70,"cover_image":71,"image_url":71,"created_at":72,"category":13},"0e767e9d-5d17-4cd0-b6ee-0328f89eb49b","gemma-4-12b-specs-benchmarks-run-locally-en","Gemma 4 12B: Specs, Benchmarks & How to Run It Locally","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780777984661-5ymr.png","2026-06-06T20:32:25.294996+00:00",{"id":74,"slug":75,"title":76,"cover_image":77,"image_url":77,"created_at":78,"category":13},"9d15f962-739d-44f8-a7f9-11bca64d38e0","best-kimi-models-2026-k2-5-vs-k2-thinking-en","Best Kimi Models in 2026: K2.5 vs K2 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