Page 380 - AI for Good Innovate for Impact
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AI for Good Innovate for Impact



                      The solution also contributes to environmental sustainability by reducing energy consumption
                      at base stations. AI-driven radio parameter tuning minimizes power usage and lowers the
                      carbon footprint of network operations. ArcGIS integration further supports environmental
                      monitoring for issues such as deforestation and urban heat islands.

                      Finally, the platform fosters cross-sector collaboration between telecom providers, city planners,
                      AI researchers, and public agencies. By uniting expertise and data from multiple domains, the
                      use case promotes innovation and scalable solutions for smarter, greener, and more connected
                      cities.


                      Partners

                      Amrita University, ESRI ArcGIS, Indian Institute of Science, Indian Open Source Mobile Congress

                      2�3     Future Work

                      •    Integrating ArcGIS into 5G core and OpenRAN
                      •    Incorporating Digital Twin for Proof of Concept


                      3      Use Case Requirements

                      •    REQ-01: It is expected that the 5G Core system supports real-time geospatial data
                           ingestion from ArcGIS using REST APIs for both feature and geometry services.
                      •    REQ-02: It is critical that a continuously updated Digital Twin of each 5G/6G OpenRAN
                           base station is maintained within the 5G Core to simulate network behavior under varying
                           environmental and traffic conditions.
                      •    REQ-03: It is expected that the system utilizes Genetic Algorithms (GA) to optimize radio
                           configurations and power allocation in order to minimize energy consumption while
                           maintaining service coverage and signal quality.
                      •    REQ-04: It is critical that real-time radio parameter tuning is achieved through Proximal
                           Policy Optimization (PPO), based on live Key Performance Indicator (KPI) feedback from
                           physical base stations.
                      •    REQ-05: It is expected that the system operates autonomously with minimal manual
                           intervention, enabling self-optimization and self-healing capabilities across the network.
                      •    REQ-06: It is critical that the solution remains fully compatible with Open Radio Access
                           Network (OpenRAN) RAN Intelligent Controller (RIC) standards to ensure vendor
                           interoperability and future-readiness for 6G deployment.




























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