This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno’s synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT’s periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm’s effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly $27.5\%$ when compared to conventional multi-level sleep operation and consumes less energy at $75.21\%$ of the energy consumed by the conventional method while incurring a throughput impairment of nearly $4.2\%$. Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices.

The proliferation of 5G networks has revolutionized wireless communication by delivering enhanced speeds, ultra-low latency, and widespread connectivity. However, in heterogeneous cloud radio access networks (H-CRAN), efficiently managing inter-cell interference while ensuring energy conservation remains a critical challenge. This paper presents a novel energy-efficient, dynamic enhanced inter-cell interference coordination (eICIC) scheme based on deep reinforcement learning (DRL). Unlike conventional approaches that focus primarily on optimizing parameters such as almost blank subframe (ABS) ratios and bias offsets (BOs), our work introduces the transmission power during ABS subframes (TPA) and the channel quality indicator (CQI) threshold of victim user equipments (CTV) into the optimization process. Additionally, this approach uniquely integrates energy consumption into the scheme, addressing both performance and sustainability concerns. By modeling key factors such as signal-to-interference-plus-noise ratio (SINR) and service rates, we introduce the concept of energy-utility efficiency to balance energy savings with quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves up to $70\%$ energy savings while enhancing QoS satisfaction, showcasing its potential to significantly improve the efficiency and sustainability of future 5G H-CRAN deployments.