Dynamic clustering-based adaptive frequency allocation for interference mitigation and device-to-device communication in wireless cellular 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.