面向非完全电磁信息的智能频谱分配技术研究

赵浩钦; 段国栋; 司江勃; 黄睿; 石嘉

doi:10.11999/JEIT231005

面向非完全电磁信息的智能频谱分配技术研究

doi: 10.11999/JEIT231005

1.
西安电子科技大学通信工程学院西安 710071
2.
中国电子科技集团公司第二十九研究所成都 610036

基金项目: 电磁空间作战与应用重点实验室基金(JJ2021-001)，国家自然科学基金(62425103)

详细信息

作者简介:
赵浩钦：男，博士生，研究方向为智能频谱共享

段国栋：男，高级工程师，研究方向为智能频谱共享

司江勃：男，教授，研究方向为认知抗干扰通信、智能频谱共享

黄睿：男，博士生，研究方向为智能频谱共享

石嘉：男，副教授，研究方向为无线资源管理

通讯作者:
司江勃　jbsi@xidian.edu.cn

中图分类号: TN97
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- 被引次数: 0
出版历程
- 收稿日期: 2023-09-15
- 修回日期: 2024-04-24
- 网络出版日期: 2024-05-15
- 刊出日期: 2024-07-29

Research on Intelligent Spectrum Allocation Techniques for Incomplete Electromagnetic Information

1.
Institute of Communications Engineering, Xidian University, Xi’an 710071, China
2.
Southwest China Research Institute of Electronic Equipment, Chengdu 610036, China

Funds: The Electromagnetic Space Warfare and Applications Key Laboratory Foundation (JJ2021-001), The National Natural Science Foundation of China (62425103)

摘要

摘要: 针对电磁对抗过程中环境动态变化，多节点自主用频决策频谱利用率低的问题，该文开展面向非完全电磁信息的智能协同频谱分配技术研究，通过多节点智能协同提升频谱利用率。首先将复杂电磁环境频谱分配问题建模为最大化用频设备的优化问题，其次提出一种基于多节点协同分流经验回放机制的资源决策算法(Co-DDQN)，算法基于协同分流函数对历史经验数据进行评估，并通过分级经验池进行训练，使各智能体在仅观测自身状态信息条件下形成轻量级协同决策能力，解决低视度条件下多节点决策优化方向与整体优化目标不一致的问题，提升频谱利用率；设计了一种基于置信分配的混合奖励函数，各节点决策兼顾个体的奖励，能够减少惰性节点的出现，探索更优的整体动作策略，进一步提升系统效益。仿真结果表明：当节点数为20时，所提算法的可接入设备数优于全局贪婪算法与遗传算法，并与信息完全共享的集中式频谱分配算法的差距在5%内，更适用于低视度节点的协同频谱分配。
- 频谱资源分配 /
- 深度强化学习 /
- 非完全电磁信息 /
- 协同分流机制
Abstract: To solve the problem of low spectrum utilization of multi-node autonomous frequency decision-making in the dynamic electromagnetic countermeasure environment, the research on intelligent cooperative spectrum allocation technology for in complete electromagnetic information is carried out, which improves spectrum utilization through multi-node intelligent collaboration. Firstly, the spectrum allocation problem is modelled as an optimization problem to maximize the frequency-using equipment, and secondly, a resource decision-making algorithm based on the multi-node cooperative diversion experience repetition mechanism (Cooperation- Deep double Q-network, Co-DDQN) is proposed. This algorithm evaluates the historical experience data based on the cooperative diversion function and is trained by a hierarchical experience pool, so that each agent can form a lightweight cooperative decision-making ability under self-observation, and solve the problem of inconsistency between the optimization direction of multi-node decision-making and the overall optimization goal under low-visibility conditions. Besides, a hybrid reward function based on confidence allocation is designed, and each node considers itself when the decision is made, which can reduce the emergence of lazy nodes, explore a better overall action strategy, and further improve the system efficiency. Simulation results show that when the number of nodes is 20, the number of accessible devices of the proposed algorithm outperforms the global greedy algorithm and the genetic algorithm, and the difference with the centralized spectrum allocation algorithm with complete information sharing is within 5%, which is more suitable for cooperative spectrum allocation of low-visibility nodes.
- Spectrum resource allocation /
- Deep reinforcement learning /
- Incomplete electromagnetic information /
- Cooperative triage mechanism

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图 1 设备空间分布模型

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图 2 Dec-POMDP模型

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图 3 算法流程图

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图 4 不同学习率权重下的算法性能

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图 5 不同奖励机制下的算法性能

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图 6 智能算法训练过程

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图 7 不同节点数下算法性能对比图

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图 8 不同干扰功率下算法性能对比图

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图 9 不同算法下时间开销对比图

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表 1 系统参数

参数	含义
M	作战空间范围
T	整个战时时隙数
f	电磁环境中正交子信道
B	信道带宽
$ {P^{{\mathrm{r}}}} $	雷达设备发射功率
$ {P^{{\mathrm{c}}}} $	通信设备发射功率
$ {P^{{\mathrm{e}}}} $	敌方设备发射功率
$ {N_t} $	t时隙环境中的噪声功率
$ g_{n{,{\mathrm{c}}}}^{{{\mathrm{com}}}} $	节点n中的通信设备至控制中心c的信道增益
$ g_{n,n'}^{{{\mathrm{com}}}} $	节点n中的通信设备至其余节点$ n' $的信道增益
$ g_{n{,{\mathrm{c}}}}^{{{\mathrm{rad}}}} $	节点n中的雷达设备至控制中心c的信道增益
$ g_{n,n'}^{{{\mathrm{rad}}}} $	节点n中的雷达设备至其余节点$ n' $的信道增益
$ {g_{{\mathrm{e}}{,}n}} $	敌方设备e至节点n的信道增益
$ {g_{{\mathrm{e}}{\text{,}}{\mathrm{c}}}} $	敌方设备e至控制中心c的信道增益
$ P_{\min }^{\text{e}} $	雷达最小可检测功率

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1 Co-DDQN算法

1: 环境初始化；构建DDQN神经网络，设置目标Q网络中参数的　更新频率为$ {T}_{\mathrm{f}\mathrm{r}\mathrm{e}} $
2：for each episode do：
3: 　$ t=0 $ /初始化时间系数/
4: 　for each mini-episode do：
5: 　　$ {{\boldsymbol{s}}}_{t}^{n} $，$ {{\boldsymbol{a}}}_{t}^{n} $ /各节点n感知自身状态信息并根据　　　　$ \varepsilon {\text{-}}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{y} $策略输出各节点策略/
6: 　　${\boldsymbol{A}}_t^n \leftarrow \{ {\boldsymbol{a}}_t^1,{\boldsymbol{a}}_t^2,\cdots,{\boldsymbol{a}}_t^n\| n\in N\} $ /联合所有节点策略进行　　　　资源分配，与环境交互获取奖励/
7: 　　$ {{\boldsymbol{S}}}_{t+1} $; $ {\mathrm{R}\mathrm{W}}_{t} $ /更新环境状态；获取系统总收益/
8: 　　$ {{\boldsymbol{s}}}_{t+1}^{n} $; $ {\mathrm{r}\mathrm{w}}_{t}^{n} $ /各节点感知自身状态信息；获取自身奖励/
9: 　　将经验样本存入各节点的经验池
10: 　 $ {D}^{1\mathrm{s}\mathrm{t}},{D}^{2\mathrm{n}\mathrm{d}},{D}^{3\mathrm{r}\mathrm{d}} $ /各节点建立3种经验池/
11: 　根据3.2.1节协同分流函数对经验进行评估分流
12: 　 $ {\mathrm{loss}} $ /基于 $ {D}^{1\mathrm{s}\mathrm{t}},{D}^{2\mathrm{n}\mathrm{d}},{D}^{3\mathrm{r}\mathrm{d}} $计算损失函数/
13: 　 $ \stackrel{～}{\theta }\leftarrow \theta $ /使用不同学习率$ {{\mathrm{lr}}}_{L}={\alpha {\mathrm{lr}}}_{M}={\alpha }^{2}{{\mathrm{lr}}}_{S} $更新网　　　　络参数/
14: 　 $ t\leftarrow t+1 $ /进入下一时隙/
15: 　 end
16: 　 if $ \mathrm{e}\mathrm{p}\mathrm{i}\mathrm{s}\mathrm{o}\mathrm{d}\mathrm{e}\%{T}_{\mathrm{f}\mathrm{r}\mathrm{e}}==0 $:
17: 　　复制当前网络参数至目标Q网络
18：end

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