Multi-Objective BS Energy and QoS Optimization with Prediction-based Safe DRL

This paper proposes a multi-level sleep mode control technique based on safe deep reinforcement learning (SDRL), which integrates long short-term memory (LSTM)-based traffic prediction to reduce the energy consumption of small cell base stations, as they account for a large proportion of heterogeneous networks. To mitigate quality of service (QoS) degradation caused by prolonged sleep states, the proposed framework incorporates safety constraints that proactively avoid excessive sleep continuity by predicting future traffic, while simultaneously restricting unacceptable mode transitions. The framework learns to balance these objectives through a reward structure that explicitly captures the trade-off between energy saving and QoS, and is implemented using a deep Q-network (DQN). Simulation results show that the proposed method ensures efficient and stable operation in dynamic traffic environments, achieving reduced energy consumption while stably satisfying QoS requirements compared to benchmark methods.