基于深度迁移学习的动态频谱快速适配抗干扰方法
Rapid adaption to dynamic spectrum anti-jamming approach based on deep transfer learning
李思达 1徐逸凡 1刘杰 1林凡迪 1韩昊 1易剑波 2徐煜华1
作者信息
- 1. 陆军工程大学通信工程学院,江苏南京,210000
- 2. 海南宝通实业公司,海南海口,570100
- 折叠
摘要
机器学习逐渐发展成为一种成熟强大的技术工具,并被广泛应用于无线通信抗干扰领域.其中,较为典型的有基于深度强化学习的抗干扰方法,通过与动态、不确定通信环境的不断交互来学习最优用频策略,有效解决动态频谱接入抗干扰的问题.然而,由于外界电磁频谱空间复杂、干扰模式样式动态多变,从头开始学习复杂的抗干扰通信任务往往时效性差,导致学习效率和通信性能显著下降.针对上述问题,提出基于深度迁移学习的动态频谱快速适配抗干扰方法.首先,通过构建预训练模型对已知干扰模式进行学习;其次,使用卷积神经网络提取现实场景下的感知频谱数据,重用过往经验优先启动加速适配;最后,运用微调策略辅助强化学习实施在线抗干扰信道接入.仿真结果表明,相较于传统强化学习算法,所提方法能够有效加快算法收敛速度,提升通信设备抗干扰性能.
Abstract
Machine learning has become a mature and powerful technique and has been widely used in the fields of wireless anti-jamming communication.Deep reinforcement learning(DRL),one of the typical anti-jamming approaches,that enables an agent to learn an optimal frequency-using policy by constantly interacting with dynamic and uncertain communications environments,has been proposed as effective tools to solve the problem of dynamic spectrum accessing.However,learning a complex task from scratch often results in poor timeliness due to the complexity of the state space of the external electromagnetic spectrum and the vola-tile variation for the jamming patterns,which may cause a significant decline of the learning efficiency as well as communication performance instead.For these problems mentioned a-bove,this paper proposes a rapid adaption to dynamic spectrum anti-jamming(DSAL)meth-od based on deep transfer learning(DTL).Firstly,an adequately pre-trained model is estab-lished learned from known jamming patterns.Further,convolution neural network(CNN)is used to extract jamming features from sensed spectrum data in real-world scenario and reu-sing knowledge that comes from previous experience contributes to scale up priority-startup and fast-adaption.In addition,fine-tune strategy is adopted to assist reinforcement learning(RL)algorithm to implement the task of on-line channel accessing for anti-jamming tasks.The simulation results show that,compared with traditional RL algorithm,our improved method can increase the convergence speed and reach better anti-jamming performance.
关键词
动态频谱抗干扰/深度迁移学习/强化学习/快速适配Key words
DSAL/DTL/RL/rapid adaption引用本文复制引用
基金项目
国家自然科学基金资助项目(62071488)
国家自然科学基金资助项目(U22B2022)
江苏省自然科学基金资助项目(BK20231027)
出版年
2024
NETL
NSTL
维普
万方数据