Adaptive Network Slicing and Resource Allocation in 5G-Advanced Systems - Elias Hartmann (Computational Intelligence Systems, 2026)
5G-Advanced networks are expected to support heterogeneous service classes with sharply different reliability, latency, and bandwidth requirements while operating under time-varying radio and transport constraints. This paper presents an adaptive network slicing and re- source allocation framework for 5G-Advanced systems that combines short-horizon demand prediction, slice-aware policy optimization, and hierarchical radio–compute scheduling. The proposed method, termed Hierarchical Adaptive Network Slicing Allocator (HANSA), mod- els the network slicing problem as a constrained Markov decision process in which a slice or- chestrator allocates spectrum, physical resource blocks, and edge-compute quotas to enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) slices. A realistic experimental design is constructed using synthetic-yet-plausible traces derived from urban mobility, industrial sensing, and mixed enterprise traffic patterns over 24 weeks. The evaluation compares HANSA against static par- titioning, proportional-fair slicing, deep Q-learning, and proximal policy optimization baselines. Across all scenarios, HANSA increases aggregate throughput by 14.8% relative to the strongest baseline, reduces the 95th-percentile latency of URLLC flows by 28.6%, and improves slice satis- faction ratio from 91.7% to 96.4%. An ablation study shows that hierarchical control, predictive demand estimation, and SLA-aware reward shaping each contribute materially to the final gain. The results indicate that adaptive cross-layer slicing is a practical mechanism for translating 5G-Advanced flexibility into measurable quality-of-service improvements without excessive com- putational overhead.