Deep Reinforcement Learning for Autonomous Network Optimization

Abstract: The growing complexity of next-generation communication networks — encompassing heterogeneous 5G and beyond-5G (B5G) infrastructures, software-defined networking (SDN), network function virtualization (NFV), and ultra- dense small cell deployments — has rendered manual and rule-based network management approaches fundamentally inadequate. Autonomous network optimization, in which self-configuring, self-optimizing, and self-healing control loops adapt network parameters in real time without human intervention, has emerged as the essential operational paradigm for future intelligent networks. This paper proposes a Deep Reinforcement Learning framework for Autonomous Network Optimization (DRL-ANO) that employs a multi-agent Soft Actor-Critic (MASAC) architecture to simultaneously optimize radio resource management, traffic engineering, and network slice resource allocation across a heterogeneous multi-cell environment. The framework introduces a Graph Attention Network (GAT)-based state encoder that captures the dynamic topology and interference relationships of the network as a relational observation, enabling the MASAC agents to make topology-aware optimization decisions at millisecond timescales. A safe exploration mechanism enforcing hard Quality of Service (QoS) constraints via Lagrangian relaxation is incorporated to prevent the DRL agents from violating service level agreements during online learning and deployment. Security provisions include adversarial observation detection, encrypted policy distribution, and anomaly-triggered safe fallback modes. Experimental evaluation on a simulated heterogeneous 5G network with 19 base stations, 6 active network slices, and dynamic traffic generated from real-world LTE traces demonstrates a 39% improvement in network-wide spectral efficiency, a 44% reduction in end-to-end slice latency, a 31% improvement in resource utilization, and a 97.2% QoS constraint satisfaction rate, significantly outperforming rule-based, single-agent DRL, and model predictive control baselines.

Keywords: Deep Reinforcement Learning; Autonomous Network Optimization; Soft Actor-Critic; Multi-Agent Systems; Graph Attention Networks; Network Slicing; 5G; Radio Resource Management.