Intelligent Group Paging for 5 G IoT: A Unified DL Framework for Energy Optimization

Group Paging (GP) is essential for scalable device wake-up in Fifth-Generation (5 G) Internet of Things (IoT) networks. However, grouping devices with heterogeneous traffic characteristics can lead to energy inefficiency and degraded access reliability. This paper presents Deep Learning-based Group Paging (DL-GP), a unified Deep Learning (DL) framework that learns energy-optimal group assignments while satisfying Quality of Service (QoS) constraints. We formulate GP as an energy-aware optimization problem and solve it using a differentiable model that integrates: (i) a Sinkhorn layer to approximate discrete group labels, (ii) a Log-Sum-Exponential (LSE) surrogate to stably optimize Transmission Frequency (TF) and Discontinuous Reception (DRX) cycles, and (iii) an adaptive retransmission controller to enforce Access Success Rate (ASR) requirements. Trained on synthetic and LoRaWAN traces, DL-GP reduces average device energy consumption by 35% and 17% compared to Greedy and Genetic baselines in large-scale simulations, and by 66% and 64% in a real LoRaWAN deployment, respectively. It maintains low Paging Overhead (PO), satisfies ASR requirements, and achieves sub-millisecond processing latency. These results demonstrate that DL-GP is a practical real-time solution for energy-efficient and QoS-compliant paging in large-scale 5 G IoT systems.