Dynamic Adaptation Technology of Frequency Hopping Communication in Spectrum Coexistence Environment

Abstract

In spectrum coexistence environments, multiple technologies operate in the shared frequency bands, which leads to dynamic spectrum occupancy, interference, and inefficient spectrum utilization. This study investigates frequency hopping (FH) communication as an effective solution to improve spectrum efficiency and communication reliability in spectrum coexistence environments. The fundamental principle of FH is introduced and its capability of mitigating interference through dynamic carrier frequency switching is emphasized. Recent study progress is reviewed from two perspectives. Spectrum sensing-based FH approaches, especially those with deep learning techniques, are shown to improve the accuracy and robustness of spectrum occupancy detection under dynamic conditions. Intelligent algorithm based-FH methods such as reinforcement learning (RL) and deep reinforcement learning (DRL) are discussed because of their effectiveness in optimizing FH schemes and adapting to time-varying spectrum environments. Despite these advancements, existing approaches still have several limitations, including reliance on large scale training datasets, computational complexity and insufficient adaptability. Future study direction could be advanced artificial intelligence techniques, lightweight algorithm design, cross-layer optimization and integration with emerging technologies. This paper offers valuable insights for the development of efficient, robust, and adaptive wireless systems in next-generation spectrum coexistence environments.