A Novel Approach to Received Signal Power Optimization in 5G Networks using Pelican Optimization Algorithm

A Novel Power Optimization Technique for Sliced 5G Network

—To reduce power consumption and extend network lifespan, academic and industrial groups have focused on energy-efficiency approaches for Next Generation Networks (NGNs). Fifth-generation (5G) networks offer a large number of services at high data rates, low latency, and massive connectivity. Increasing volumes of heterogeneous traffic from billions of devices, ranging from smartphones to intelligent transport systems, significantly challenge network resource utilization, particularly power consumption. This study targets energy-efficient resource allocation in sliced 5G systems, ensuring service-level guarantees for heterogeneous applications through intelligent optimization. This work proposes a novel hybrid optimization framework for energy-aware resource provisioning in 5G sliced networks using Hybrid Grey Wolf–Tasmanian Devil Optimization (HGWTDO) with a Linear Pattern Search (LPS) refinement technique. While HGWTDO combines the global search ability of Grey Wolf Optimization (GWO) and the exploitation abilities of the Tasmanian Devil Optimizer, the addition of LPS provides accurate local convergence. LPS has been integrated into the proposed solution to enhance optimization results. The solution is augmented with a Classification Tree-based classification that assigns users to their corresponding slices for Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and massive Machine-Type Communication (mMTC) based on quality of service (QoS) requirements. The suggested system provides improved power efficiency under QoS constraints and is an intelligent, scalable solution for energy-aware 5G network slicing compared with existing techniques.

Network Selection for 6G D2D Communication Using Genetic AI-Based Decision Framework

Intelligent resource allocation technique for 5G NR self-organizing networks via self-improved lyrebird optimization algorithm

An enhanced intelligent framework for 5G V2X communication using multi-objective optimization and mobility-aware transformer networks.

The proposed framework achieves 20-30% reduced latency, a 15-35% reduction in energy consumption, and an 18-28% throughput enhancement compared to existing methods, and ensures a wide improvement in reliability and adaptability in 5G V2X communication networks.

Unified Optimization of Solar-Powered Cell Switching in HAPS-Assisted 6G Networks

Simulation results demonstrate that the proposed integrated green energy strategy, leveraging GA and k-means clustering, achieves considerable energy savings compared to baseline approaches and clustering-based problem simplification is shown to deliver performance comparable to the full-scale solution that explores the entire search space.

Optimal resource allocation in urban Internet of Things using 3D beamforming.

The proposed MC-NOMA with 3D beamforming consistently outperforms baseline schemes that employ OFDMA with shared spectrum or uniform linear arrays, especially under high channel estimation errors, strong external interference, stringent coverage constraints, and increasing user densities.