The rapid growth of mobile traffic and heterogeneous services in 5G and beyond Radio Access Networks (RANs) has created significant challenges in reducing network energy consumption while maintaining adequate service performance. This paper proposes a comprehensive traffic- and user-density-aware energy consumption model for 5G and beyond RANs under dynamic traffic conditions. The proposed framework is implemented in MATLAB and considers a 24-hour operating period with 15-minute time intervals. Dynamic traffic is first generated and used to estimate active-user density and Physical Resource Block (PRB) utilization. Based on the instantaneous traffic and resource utilization, base stations (BSs) are dynamically assigned to sleep, low-load, medium-load, or high-load states, enabling unnecessary BSs to be deactivated during low-demand periods. The model incorporates static, dynamic, sleep-state, and auxiliary power components to estimate hourly and daily RAN energy consumption. In addition, throughput, energy efficiency, energy consumption per bit, and energy-saving potential are evaluated to establish the relationship between traffic demand, resource utilization, BS activity, and power consumption. Simulation results show that the proposed dynamic BS operation consumes 64.84 kWh/day, compared with 91.72 kWh/day for continuous BS operation, achieving an energy reduction of approximately 26.88 kWh/day or 29.28%. The network achieves 3,547,502.97 bit/J energy efficiency with an energy consumption of 2.8189 × 10?7 J/bit. The results demonstrate that traffic-aware dynamic BS activation provides an effective and practical approach for improving energy efficiency in 5G and beyond RANs.
5G RAN; Beyond-5G Networks; Energy Consumption; Energy Efficiency; Dynamic Traffic; User Density; PRB Utilization; Base-Station Sleep; Dynamic BS Activation.
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