Jul 2026· Journal of High Speed Networks· Vol 32, pp. 217 - 230· 0 citations· 16 references
TL;DR
Experimental evaluation demonstrates that the proposed DCB-AFA scheme significantly enhances spectrum utilization, reduces interference, and lowers power consumption compared to conventional approaches, making it a robust solution for next-generation wireless networks.
Abstract
Efficient frequency allocation in device-to-device (D2D) communication remains a critical challenge due to the need to mitigate interference while maintaining high quality of service (QoS). Traditional static allocation methods fail to adapt to dynamic network conditions, leading to inefficient spectrum utilization and degraded performance. This paper proposes a dynamic clustering-based adaptive frequency allocation (DCB-AFA) framework to address these limitations in wireless networks operating under continuously changing environments. The proposed approach leverages user location information and communication patterns to form adaptive clusters that minimize intra-network interference while enabling efficient D2D connectivity. A machine learning-based prediction mechanism is incorporated to anticipate user behavior and dynamically adjust cluster boundaries and resource allocation strategies. Furthermore, a QoS-aware feedback system continuously monitors network conditions and refines allocation decisions to improve performance metrics such as throughput, latency, and energy efficiency. Experimental evaluation demonstrates that the proposed DCB-AFA scheme significantly enhances spectrum utilization, reduces interference, and lowers power consumption compared to conventional approaches, making it a robust solution for next-generation wireless networks.
The next generation of wireless systems extends ultra-reliable low-latency communications (URLLC) to the realm of massive connections, termed mURLLC. To address the inherent conflict between stringent quality of service (QoS) requirements in URLLC and the problem of severe and highly fluctuating interference behind demands of massive connectivity, effective fast fading (FF) mitigation and resource allocation strategies are crucial. Through in-depth analysis of FF characteristics, this paper derives optimized configurations for two FF mitigation approaches: protection margin reservation and $K$ -repetition. Furthermore, we integrate these FF mitigation strategies into a hierarchical-clustering (HC)-based resource allocation algorithm for configured-grant in mURLLC. This results in a highly practical and efficient algorithm for managing radio resources and interference in mURLLC scenarios. Simulation results demonstrate that our proposed algorithm achieves over 65% reduction in resource consumption without compromising reliability, significantly enhancing network capacity to support demanding mURLLC applications.
Yi-Chen Guo, Lili Xu, Yihang Cheng et al.· IEEE Transactions on Wireles...· 0 citations
This paper proposes a simulation-based dynamic message packing framework that enables runtime MPS selection based on queue occupancy and inter-participant distance while preserving the standard TDMA structure and waveform and provides a practical, standards-compliant runtime optimization for Link 16 systems.
F. Abut, Mehmet Kızıldağ· IEEE Access· 0 citations
This paper introduces a multi-stage resource management system that is adaptive in 5G networks to address QoS and mobility efficiency issues and combines the set of network parameters with Adaptive Multivariate Kernel Resource Estimation in case of assessing and normalizing available resources.
G. Ramasamy, C. Chandrasekar· International journal of com...· 0 citations
Dynamic wireless resource allocation in multi-cell networks is challenging due to non-stationary traffic, intercell interference coupling, and heterogeneous quality-of-service (QoS) constraints. Conventional schedulers and standalone metaheuristics lack adaptability across operating regimes, while deep reinforcement learning (DRL) methods often incur high training complexity and stability limitations. This paper proposes a context-aware reinforcement hyper-heuristic framework for dynamic wireless resource allocation. A contextual bandit controller hierarchically selects among multiple low-level optimization heuristics based on real-time network state features. A multi-objective reward design jointly optimizes throughput, fairness, power efficiency, and allocation stability. We establish sublinear regret guarantees under the contextual bandit model and prove convergence under standard stochastic approximation conditions. Extensive simulations over 5,000 large-scale multi-cell instances demonstrate consistent improvements over proportional fair scheduling, evolutionary methods, and DRL-based allocators in throughput, Jain's fairness index, convergence speed, and robustness to traffic perturbations. Statistical tests confirm the significance of the gains. The results indicate that reinforcementdriven hyper-heuristic orchestration provides a scalable and theoretically grounded solution for dynamic wireless resource management.
K. Danach, Samir Haddad, J. Sayah et al.· 2026 6th International Confe...· 0 citations
The dense deployment of heterogeneous communication networks significantly improves spectrum utilization and network capacity but simultaneously introduces complex co-channel and cross-tier interference. To address the challenges of multi-source interference, dynamic network environments, and large-scale coordination, this study develops a collaborative framework integrating intelligent interference suppression and dynamic network optimization. A deep reinforcement learning-based interference coordination algorithm is first designed to adaptively adjust transmission power and spectrum resource allocation according to channel conditions and traffic load, thereby improving spectrum efficiency and reducing inter-layer interference. Subsequently, a federated learning-based crossdomain optimization strategy is proposed to achieve collaborative resource scheduling and load balancing without sharing raw user data. To validate the effectiveness of the proposed framework, simulation experiments are conducted under urban hotspot, high-speed railway, and industrial deployment scenarios. Results demonstrate significant improvements in interference mitigation capability, network robustness, and resource utilization efficiency. The proposed method provides technical support for future wireless communication systems and contributes to the development of electromagnetic wave propagation management, intelligent spectrum allocation, and next-generation heterogeneous networks.
Due to the rapid growth of smartphones, tablets, and IoT devices, wireless LAN traffic continues to increase, causing severe congestion in high-density environments. Although IEEE 802.11be introduces Multi-Link Operation (MLO) to improve performance by utilizing multiple links, conventional fixed channel selection and traffic allocation schemes cannot adapt to dynamically changing wireless environments. To address this problem, this paper proposes an adaptive channel selection and traffic allocation method based on a distributed spectrum management database that shares real-time channel utilization information among access points. The proposed method dynamically selects channels and allocates traffic for each MLO link according to both channel occupancy and link capacity, enabling efficient utilization of frequency resources. Simulation results using the network simulator ns-3 demonstrate that the proposed method improves total network throughput compared to conventional approaches in high-density environments.
