From d15430c3311e4a5ec533fe1203d95481f3393657 Mon Sep 17 00:00:00 2001
From: Ushasi Ghosh <39842627+ushasigh@users.noreply.github.com>
Date: Mon, 26 Aug 2024 15:43:15 -0700
Subject: [PATCH] Update 2023-10-30-edgeric.md

---
 _posts/2023-10-30-edgeric.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_posts/2023-10-30-edgeric.md b/_posts/2023-10-30-edgeric.md
index 9e2d2c811b8..6975660acae 100644
--- a/_posts/2023-10-30-edgeric.md
+++ b/_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. "      
 ---
-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.