An AI-Driven Multi-Source Data Fusion Framework for Intelligent Network Optimization in 5G-A Systems

Abstract

Fifth-generation advanced (5G-A) networks are expected to support ultra-dense deploy- ments, cross-domain service orchestration, and stringent quality-of-service guarantees under highly dynamic traffic and channel conditions. Conventional optimization pipelines rely on single-domain measurements and reactive heuristics, which limits their ability to capture com- plex interactions among radio access, transport load, user mobility, and application-layer de- mand. This paper presents a practical AI-driven multi-source data fusion framework for intelli- gent network optimization in 5G-A systems. The framework integrates heterogeneous telemetry from gNodeB counters, user equipment traces, edge-cloud logs, and external context signals through a temporally aligned graph-feature fusion architecture. We formulate network opti- mization as a constrained sequential decision problem and design a hybrid model that combines a spatio-temporal encoder with a policy optimization layer to jointly improve throughput, la- tency, energy efficiency, and fairness.
To evaluate realism and robustness, we construct a 5G-A-oriented benchmark by combin- ing OpenRAN-style KPI streams, synthetic but statistically calibrated mobility traces, and service-level traffic profiles for enhanced mobile broadband, ultra-reliable low-latency commu- nication, and massive machine-type communication slices. Experiments are conducted on a digital twin testbed with configurable load shocks and interference bursts. Compared with rep- resentative baselines including rule-based scheduling, single-source deep reinforcement learning, and transformer-only predictors, the proposed method improves weighted network utility by 12.8%, reduces 95th percentile latency by 18.6%, and increases cell-edge user throughput by 15.2%. Ablation studies confirm that temporal synchronization, cross-source attention, and constraint-aware action projection all contribute materially to final performance.
The study demonstrates that multi-source fusion is not merely a modeling preference but an operational requirement for next-generation autonomous network management. We further analyze computational complexity, deployment trade-offs, and failure modes, showing that the design can meet near-real-time control loops in edge-assisted 5G-A management stacks while maintaining stable behavior under non-stationary traffic conditions.