Optimized Call Admission Control for LTE Networks Based on Multilevel Bandwidth Allocation Framework

Efficient resource allocation is vital for ensuring quality of service (QoS) in Long‐Term Evolution (LTE) networks, especially for call admission control (CAC). This study introduces a multilevel bandwidth (BW) allocation model that dynamically assigns BW based on call requirements and computes the minimum necessary BW when availability is limited. Calls are classified as new calls (NC) or handoff calls (HC) based on network conditions. To enhance CAC decision‐making, a cascade feedforward network (Cascade FFN), integration of a cascade neuro‐fuzzy network and a deep feedforward neural network (DFNN), is employed. The proposed model achieves high BW utilization of 0.911, power efficiency of 65.098 dBm, and mean throughput of 516,830.584 bps. Also, it minimizes call blocking probabilities for NC, which is 0.591, and for HC, which is 0.605; the call dropping probabilities for NC, which is 0.606, and for HC, which is 0.592; the mean delay of 0.066 s; and the number of dropped users of 820.620. These results demonstrate significant improvements in LTE performance through intelligent BW allocation and CAC.