Update 2023-10-30-edgeric.md

_posts/2023-10-30-edgeric.md

@@ -33,4 +33,4 @@ miscs:  # whatever you need to add Extra
 description: # all combinations are possible: (title+text+image, title+image, text+image etc), things will be populated in orders
     - text: "Radio Access Networks (RANs) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we introduce EdgeRIC, a real-time RIC co-located with the Distributed Unit (DU). It is decoupled from the RAN stack, and operates at the RAN timescale. EdgeRIC serves as the seat of real-time AI-in-the-loop for decision and control. It can access RAN and application-level information to execute AI-optimized and other policies in real-time (sub-millisecond). We demonstrate that EdgeRIC operates as if embedded within the RAN stack. We showcase RT applications called μApps over EdgeRIC that significantly outperforms a cloud-based near real-time RIC (> 15 ms latency) in terms of attained system throughput. Further, our over-the-air experiments with AI-based policies showcase their resilience to channel dynamics. Remarkably, these AI policies outperform model-based strategies by 5% to 25% in both system throughput and end user application-level benchmarks across diverse mobile scenarios. "      
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-Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we introduce EdgeRIC, a real-time RIC co-located with the Distributed Unit (DU). It is decoupled from the RAN stack, and operates at the RAN timescale. EdgeRIC serves as the seat of real-time AI-in-the-loop for decision and control. It can access RAN and application-level information to execute AI-optimized and other policies in real-time (sub-millisecond). We demonstrate that EdgeRIC operates as if embedded within the RAN stack. We showcase RT applications called μApps over EdgeRIC that significantly outperforms a cloud-based near real-time RIC (> 15 ms latency) in terms of attained system throughput. Further, our over-the-air experiments with AI-based policies showcase their resilience to channel dynamics. Remarkably, these AI policies outperform model-based strategies by 5% to 25% in both system throughput and end user application-level benchmarks across diverse mobile scenarios.
+Radio Access Networks (RANs) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we introduce EdgeRIC, a real-time RIC co-located with the Distributed Unit (DU). It is decoupled from the RAN stack, and operates at the RAN timescale. EdgeRIC serves as the seat of real-time AI-in-the-loop for decision and control. It can access RAN and application-level information to execute AI-optimized and other policies in real-time (sub-millisecond). We demonstrate that EdgeRIC operates as if embedded within the RAN stack. We showcase RT applications called μApps over EdgeRIC that significantly outperforms a cloud-based near real-time RIC (> 15 ms latency) in terms of attained system throughput. Further, our over-the-air experiments with AI-based policies showcase their resilience to channel dynamics. Remarkably, these AI policies outperform model-based strategies by 5% to 25% in both system throughput and end user application-level benchmarks across diverse mobile scenarios.