Dynamic service-aware network selection framework for multi objective optimization in 5G-advanced heterogeneous wireless networks

Abstract

The increasing complexity of heterogeneous wireless networks (HWNs) and the diverse requirements of mobility patterns and service classes necessitate advanced solutions for network selection and resource optimization. Existing models often fall short in addressing dynamic mobility scenarios and service differentiation, leading to inefficiencies in resource allocation, suboptimal throughput, and increased latency. To overcome these limitations, this study proposes a dynamic service-aware network selector (DSANS) framework for 5G-advanced environments. The framework integrates an adaptive deep decision network (ADDN) for multi-objective optimization, addressing critical quality of service (QoS) metrics such as throughput, delay, and energy efficiency while enhancing quality of experience (QoE) for applications like enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC), and internet of things (IoT). The DSANS framework dynamically adapts to mobility patterns and varying network conditions, ensuring efficient resource estimation and optimal network selection. Simulation results highlight its superiority, achieving up to 25% improvement in throughput and a 15% reduction in latency compared to state-of-the-art algorithms. These findings validate DSANS as a robust solution for mitigating the limitations of existing models, optimizing network performance, and meeting the stringent demands of next-generation HWNs.