English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Open Radio Access Network (O-RAN) architecture empowers intelligent andautomated optimization of the RAN through applications deployed on the RANIntelligent Controller (RIC) platform, enabling capabilities beyond what isachievable with traditional RAN solutions. Within this paradigm, TrafficSteering (TS) emerges as a pivotal RIC application that focuses on optimizingcell-level mobility settings in near-real-time, aiming to significantly improvenetwork spectral efficiency. In this paper, we design a novel TS algorithmbased on a Cascade Reinforcement Learning (CaRL) framework. We propose statespace factorization and policy decomposition to reduce the need for largemodels and well-labeled datasets. For each sub-state space, an RL sub-policywill be trained to learn an optimized mapping onto the action space. To applyCaRL on new network regions, we propose a knowledge transfer approach toinitialize a new sub-policy based on knowledge learned by the trained policies.To evaluate CaRL, we build a data-driven and scalable RIC digital twin (DT)that is modeled using important real-world data, including networkconfiguration, user geo-distribution, and traffic demand, among others, from atier-1 mobile operator in the US. We evaluate CaRL on two DT scenariosrepresenting two network clusters in two different cities and compare itsperformance with the business-as-usual (BAU) policy and other competingoptimization approaches using heuristic and Q-table algorithms. Benchmarkingresults show that CaRL performs the best and improves the averagecluster-aggregated downlink throughput over the BAU policy by 24% and 18% inthese two scenarios, respectively.

CaRL: Cascade Reinforcement Learning with State Space Splitting for O-RAN based Traffic Steering