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 5G- enabled Vehicle -to-Everything (V2X) is a reliable communication technology that allows vehicles to communicate with other vehicles, networks, and infrastructure to enhance road safety and traffic proficiency. The challenges in 5G-V2X networks are high mobility, dynamic network topology, and strict quality-of-service (QoS) requirements. Especially in latency-sensitive applications such as collision avoidance and real-time traffic management, are degraded by frequent link failures, excessive routing overhead, and inefficient resource utilization. Recently, the machine learning-based routing algorithms integrated with fuzzy logic and metaheuristic optimization have achieved multi-objective performance with limited adaptability and slow convergence. To overcome these issues, a novel Multi-Objective Harris Hawks Optimization (MO-HHO) integrated with a Bayesian optimized Mobility-Aware Transformer Network (BMAT) is proposed to design an enhanced intelligent framework for 5G V2X Communication.MO-HHO optimizes cluster formation and routing paths with reduced latency and energy usage, and it re-clusters by improving throughput and stability using mobility-aware updates. The Bayesian optimized self-attention-based MAT transformer is used for typical long-range spatiotemporal dependencies for finding optimal cluster heads, routing stability, and probability of congestion with the Tree-structured Parzen Estimator (TPE) to produce better convergence. The proposed framework is assessed using realistic 5G V2X mobility scenarios under urban and highway conditions, and the obtained results achieve 20-30% reduced latency, a 15-35% reduction in energy consumption, and an 18-28% throughput enhancement compared to existing methods. Finally, the proposed framework 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

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

The dense deployment of Internet of Things (IoT) networks in smart cities poses severe challenges in spectral efficiency, energy consumption, and interference management. This paper addresses the joint optimization of three-dimensional (3D) beamforming, subcarrier assignment, and power allocation in a multi-carrier non-orthogonal multiple access (MC-NOMA) network supporting both device-to-infrastructure (D2I) and device-to-device (D2D) communications. A robust percentile-based channel model with spatial shadowing correlation is adopted to cope with urban propagation uncertainties, and an accurate elliptical footprint model derived from the 3-dB antenna pattern is used to evaluate coverage gaps and beam overlaps. The resulting mixed-integer nonlinear programming problem is solved by a three-layer memetic particle swarm optimization (Hybrid PSO) algorithm that combines a fixed-point Successive Interference Cancellation (SIC-aware) power solver, an iterative Hungarian method for subcarrier assignment, and an adaptive multi-phase local search. Simulation results demonstrate fast convergence, with the network power consumption stabilizing at 88 mW at a 600 MHz carrier frequency. 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. The findings confirm that the joint framework significantly enhances energy efficiency and robustness, making it a scalable solution for next-generation urban IoT networks.