DeepHopping-LTE: A Deep Reinforcement Learning Framework for Adaptive Frequency Hopping Spread Spectrum (FHSS) in Dense 4G Cellular Networks

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@article{1723917,
      author = {Eyidia Nkechinyere, Smiles Chiweikpe Obiajunwa},
      title = {DeepHopping-LTE: A Deep Reinforcement Learning Framework for Adaptive Frequency Hopping Spread Spectrum (FHSS) in Dense 4G Cellular Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {2372-2388},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723917.pdf},
      abstract = {The rapid growth of mobile devices and dense 4G deployments has intensified interference and congestion, challenging network reliability, spectrum efficiency, and quality of service. This study proposes DeepHopping-LTE, a deep reinforcement learning-based adaptive frequency hopping framework designed to dynamically optimize frequency selection in dense cellular networks. By leveraging a deep Q-network (DQN), the framework balances exploration and exploitation using an epsilon-greedy policy, converging to optimal hopping sequences to minimize interference while maximizing throughput. Simulations and field data from two urban locations demonstrated that DeepHopping-LTE significantly outperforms traditional static FHSS and heuristic frequency hopping methods. The framework achieved a stable signal-to-interference-plus-noise ratio (SINR) averaging 15–17 dB, compared to 5 dB for static methods, more than doubling spectrum efficiency to 5.5 bits/s/Hz under high load conditions. Packet delivery ratio also improved dramatically, ranging from 94% to 98%, while traditional FHSS averaged 75%, reflecting superior reliability and quality of service. Throughput improvement ratios reached 1.6 under peak user density, and interference reduction ranged from 40% to 70%, confirming effective mitigation of environmental noise. Additionally, the adaptability index reached 0.95, demonstrating near-instantaneous response to dynamic network changes, whereas heuristic and static methods lagged at 0.65 and 0.15, respectively. The results validate that reinforcement learning can transform frequency hopping from a static approach into an intelligent, adaptive solution capable of optimizing network performance in real-time. By continuously observing network states and selecting optimal channels, DeepHopping-LTE not only mitigates interference but also enhances spectrum utilization, reliability, and user experience. These findings provide a practical foundation for policy implementation in dense urban networks, supporting guidelines for adaptive frequency management, interference monitoring, and reinforcement learning deployment in 4G and future 5G environments. In conclusion, DeepHopping-LTE addresses the critical challenges of dense cellular networks, offering a scalable, intelligent, and resilient framework that surpasses traditional methods, enabling efficient and reliable communication in high-density mobile ecosystems.},
      keywords = {LTE; SINR; DeepHopping; DQN; interference},
      month = {October},
      doi = {https://doi.org/10.64388/IREV10I4-1723917}
  }