Findings validate the efficacy of incorporating swarm intelligence into the 5G architectures as a viable and self-optimizing solution for the promotion of connectivity and signal power performance in the next-generation high-density wireless networks.
For 6G wireless networks, efficient resource allocation is a significant problem, especially with the growing need for ultra-low latency, high-speed communication, and efficient energy consumption. The traditional approach is found to be inadequate to meet the dynamic changes and service-allocation requirements. The application of AI and DL is seen as an efficient approach to making intelligent, timely decisions in complex scenarios. This paper proposes an integrated AI- and DL-based approach for efficient, intelligent resource allocation in 6G wireless communication. The difficulties encountered in dynamic spectrum allocation, energy depletion, and attenuation are addressed through an integrated approach that combines optimal path selection with efficient allocation mechanisms. The input parameters considered are residual battery indicator (RBI), channel matrix (H), normalized spectrum availability (v), SINR values, node pairs (s, d), service levels, and historical statistics. To ensure data quality, a Recursive Hampel Filter-Based Estimation Model (ReHF-EM) has been employed. Furthermore, for fundamental decision-making, a Dual-Stage Multi-Time-Scale Temporal Attention-Based LSTM network (D-MTSTA-LSTM) has been architected, which effectively learns short- and long-term relationships in network trends, thereby precisely predicting optimal communication routes and associated power and spectrum allocation. Additionally, the parameters of the proposed model have been fine-tuned using the Pied Kingfisher Optimizer (PKfO) for better efficiency, thus reducing complexities associated with the model. The proposed model has been implemented using Python, and various performance parameters such as Spectrum Efficiency (SE), Energy Efficiency (EE), SINR margin, Bit Error Rate (BER), Computational Time (CT), and Accuracy have been considered to evaluate the proposed model. The results show a 23.6% increase in Energy Efficiency and a 19.2% reduction in Bit Error Rate.
The densification of wireless networks and growing real-time service demands have intensified the need for intelligent, energy-efficient resource allocation. Traditional static and centralized methods fall short in adapting to the dynamic and interference-prone nature of 5G and emerging 6G environments. This study proposes a decentralized reinforcement learning (RL)-based framework for joint power and spectrum allocation in ultra-dense wireless systems. Each base station acts as an autonomous agent, making real-time decisions based on local traffic and interference conditions. Simulated using a custom Python-based environment with 50 base stations and 500 users, the RL approach is benchmarked against static and optimization-based methods. Results show the RL model achieves up to 91% energy efficiency, 94% spectrum utilization, and only 5% QoS degradation, outperforming baseline models. This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
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.
Fifth-generation (5G), Beyond 5G (B5G), and sixth-generation (6G) wireless networks, along with the Internet of Things (IoT), are core communication infrastructure in smart cities. Their increased deployments create high-dimensional optimization and resource management challenges. Consequently, researchers have increasingly explored the use of Artificial Intelligence (AI) models for optimizing networks. The Proximal Policy Optimization (PPO) is one such algorithm that optimizes networks. This Systematic Literature Review (SLR) follows the PRISMA 2020 protocol to review 76 studies published between 2023 and 2026 to synthesize recent PPO-based approaches to optimize communication systems. This study examines key PPO variants in major communication domains. It outlines the primary obstacles to real-world deployment and provides a cross-domain classification. According to this study, PPO provides continuous action spaces with good training stability for AI models. Its stable policy-learning capabilities make it suitable for next-generation communication systems. However, sim-to-real transfer, reward design, and multi-agent scalability are a few key challenges encountered. Future directions emphasize robust, deployable PPO frameworks for 6G, IoT, and internet architecture.