Intelligent Decision-making for Selection of Communication Jamming Channel and Power
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摘要: 智能干扰是一种利用环境反馈自主学习干扰策略，对敌方通信链路进行有效干扰的技术。然而，现有的智能干扰研究大多假设干扰机能够直接获取通信质量反馈(如误码率或丢包率)，这在实际对抗环境中难以实现，限制了智能干扰的应用范围。为了解决这一问题，该文将通信干扰问题建模为马尔科夫决策过程(MDP)，综合考虑干扰基本原则和通信目标行为变化制定干扰效能衡量指标，提出了一种改进的策略爬山算法(IPHC)。该算法按照“观察(Observe)-调整(Orient)-决策(Decide)-行动(Act)”的OODA闭环，实时观察通信目标变化，灵活调整干扰策略，运用混合策略决策，实施通信干扰。仿真结果表明，在通信目标采用确定性规避策略时，所提算法能够较快收敛到最优干扰策略，并且其收敛耗时较Q-learning算法至少缩短2/3；当通信目标变换策略时，能够自适应学习，重新调整到最优干扰策略。在通信目标采用混合性规避策略时，所提算法也能够快速收敛，取得较优的干扰效果。Abstract: Intelligent jamming is a technique that utilizes environmental feedback information and autonomous learning of jamming strategies to effectively disrupt the communication links of the enemy. However, most existing research on intelligent jamming assumes that jammers can directly access the feedback of communication quality indicators, such as bit error rate or packet loss rate. This assumption is difficult to achieve in practical adversarial environments, thus limiting the applicability of intelligent jamming. To address this issue, the communication jamming problem is modeled as a Markov Decision Process (MDP), and by considering both the fundamental principles of jamming and the dynamic behavior of communication objectives, an Improved Policy Hill-Climbing (IPHC) algorithm is proposed. This algorithm follows an OODA loop of “Observe-Orient-Decide-Act”, continuously observes the changes of communication objectives in real time, flexibly adjusts jamming strategies, and applies a mixed strategy decision-making to execute communication jamming. Simulation results demonstrate that when the communication objectives adopt deterministic evasion strategies, the proposed algorithm can quickly converge to the optimal jamming strategy, and the convergence time is at least two-thirds shorter than that of the Q-learning algorithm. When the communication objectives switch evasion strategies, the algorithm can adaptively learn and readjust to the optimal jamming strategy. In the case of communication objectives using mixed evasion strategies, the proposed algorithm also achieves fast convergence and obtains superior jamming effects.
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1 基于IPHC的通信干扰信道和功率智能决策算法
参数设置：$ Q\left( {{\boldsymbol{s}},{\boldsymbol{a}}} \right) = 0 $，$ {\pi} \left( {{\boldsymbol{s}},{\boldsymbol{a}}} \right) = {1 \mathord{\left/ {\vphantom {1 {\left| A \right|}}} \right. } {\left| A \right|}} $，更新步长$\alpha $和学习率$\eta $。 学习过程：令$t = 0$，在状态${{\boldsymbol{s}}_t}$，依据$ {\pi} \left( {{{\boldsymbol{s}}_t},{\boldsymbol{a}}} \right) $得到动作${{\boldsymbol{a}}_t}$，并转移到下一状态${{\boldsymbol{s}}_{t + 1}}$。 while $t < T$ 由${{\boldsymbol{s}}_t}$和${{\boldsymbol{s}}_{t + 1}}$之间的关系，评估奖励：$ {r_t} = {w_1}{\varphi _1}\left( {{\text{JNSR}} - {T_{\text{h}}}} \right) + {w_2}\mu \left( {{f_{{\text{c}},t + 1}} - {f_{{\text{c}},t}}} \right) + {w_3}{\varphi _2}\left( {{p_{{\text{c}},t + 1}} - {p_{{\text{c}},t}}} \right) - {w_4}{{{p_{{\text{j}},t + 1}}} \mathord{\left/ {\vphantom {{{p_{{\text{j}},t + 1}}} {{P_{{\text{jMax}}}}}}} \right. } {{P_{{\text{jMax}}}}}} $； 依据奖励$ {r_t} $，调整Q值表：$ Q\left( {{{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t}} \right) = Q\left( {{{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t}} \right) + \alpha \left[ {{r_t} + \gamma \mathop {\max }\limits_{\boldsymbol{a}} Q\left( {{{\boldsymbol{s}}_{t + 1}},{\boldsymbol{a}}} \right) - Q\left( {{{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t}} \right)} \right] $； 依据Q值表调整策略，并进行归一化：$ {\pi} \left({\boldsymbol{s}},{\boldsymbol{a}}\right)={\pi} \left({\boldsymbol{s}},{\boldsymbol{a}}\right)+\eta ,\;\;{\boldsymbol{a}}=\mathrm{arg}\underset{{{\boldsymbol{a}}}^{\prime }}{\mathrm{max}}Q\left({\boldsymbol{s}},{\boldsymbol{{a}}}^{\prime }\right) $，$ {\pi} \left( {{\boldsymbol{s}},{{\boldsymbol{a}}_i}} \right) = {{{\pi} \left( {{\boldsymbol{s}},{{\boldsymbol{a}}_i}} \right)} \Bigr/ {\displaystyle\sum\limits_{i = 1}^{M \times K} {{\pi} \left( {{\boldsymbol{s}},{{\boldsymbol{a}}_i}} \right)} }} $； 转入下一时刻，$t = t + 1$，在状态${{\boldsymbol{s}}_t}$，依据$ {\pi} \left( {{{\boldsymbol{s}}_t},{\boldsymbol{a}}} \right) $得到动作${{\boldsymbol{a}}_t}$，并转移到下一状态${{\boldsymbol{s}}_{t + 1}}$。 
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表 1 仿真参数设置
参数 取值 $\gamma $ 0.5 $\alpha $ 0.1 $\eta $ 0.001 ${T_{\text{h}}}$ 0.3 ${w_1}$ 1 ${w_2}$ 0.5 ${w_3}$ 0.5 ${w_4}$ 1
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表 3 前2个最大Q值对应不同策略选择个数情况
序号 干扰
状态增大
功率切换
信道序号 干扰
状态增大
功率切换
信道1 $ \left( {{f_{{\text{j}},t}} = {F_1},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_1},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 1 1 11 $ \left( {{f_{{\text{j}},t}} = {F_3},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_3},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 1 1 2 $ \left( {{f_{{\text{j}},t}} = {F_1},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_1},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 1 12 $ \left( {{f_{{\text{j}},t}} = {F_3},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_3},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 2 0 3 $ \left( {{f_{{\text{j}},t}} = {F_1},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_1},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 1 1 13 $ \left( {{f_{{\text{j}},t}} = {F_4},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_4},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 1 1 4 $ \left( {{f_{{\text{j}},t}} = {F_1},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_1},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 2 0 14 $ \left( {{f_{{\text{j}},t}} = {F_4},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_4},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 1 5 $ \left( {{f_{{\text{j}},t}} = {F_2},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_2},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 0 2 15 $ \left( {{f_{{\text{j}},t}} = {F_4},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_4},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 1 1 6 $ \left( {{f_{{\text{j}},t}} = {F_2},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_2},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 0 2 16 $ \left( {{f_{{\text{j}},t}} = {F_4},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_4},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 2 0 7 $ \left( {{f_{{\text{j}},t}} = {F_2},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_2},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 1 1 17 $ \left( {{f_{{\text{j}},t}} = {F_5},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_5},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 1 1 8 $ \left( {{f_{{\text{j}},t}} = {F_2},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_2},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 2 0 18 $ \left( {{f_{{\text{j}},t}} = {F_5},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_5},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 1 9 $ \left( {{f_{{\text{j}},t}} = {F_3},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_3},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 0 2 19 $ \left( {{f_{{\text{j}},t}} = {F_5},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_5},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 1 1 10 $ \left( {{f_{{\text{j}},t}} = {F_3},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_3},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 1 20 $ \left( {{f_{{\text{j}},t}} = {F_5},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_5},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 1 1 总次数 21 19
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表 4 不同策略选择概率情况
序号 干扰
状态增大
功率切换
信道序号 干扰
状态增大
功率切换
信道1 $ \left( {{f_{{\text{j}},t}} = {F_1},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_1},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 1 0 11 $ \left( {{f_{{\text{j}},t}} = {F_3},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_3},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 0.76 0.24 2 $ \left( {{f_{{\text{j}},t}} = {F_1},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_1},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 0 12 $ \left( {{f_{{\text{j}},t}} = {F_3},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_3},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 1 0 3 $ \left( {{f_{{\text{j}},t}} = {F_1},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_1},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 0.89 0.11 13 $ \left( {{f_{{\text{j}},t}} = {F_4},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_4},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 1 0 4 $ \left( {{f_{{\text{j}},t}} = {F_1},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_1},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 0.77 0.23 14 $ \left( {{f_{{\text{j}},t}} = {F_4},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_4},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 0 5 $ \left( {{f_{{\text{j}},t}} = {F_2},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_2},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 1 0 15 $ \left( {{f_{{\text{j}},t}} = {F_4},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_4},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 0.93 0.07 6 $ \left( {{f_{{\text{j}},t}} = {F_2},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_2},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 0 16 $ \left( {{f_{{\text{j}},t}} = {F_4},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_4},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 1 0 7 $ \left( {{f_{{\text{j}},t}} = {F_2},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_2},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 0.98 0.02 17 $ \left( {{f_{{\text{j}},t}} = {F_5},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_5},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 1 0 8 $ \left( {{f_{{\text{j}},t}} = {F_2},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_2},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 0.80 0.20 18 $ \left( {{f_{{\text{j}},t}} = {F_5},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_5},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 0 9 $ \left( {{f_{{\text{j}},t}} = {F_3},{p_{{\text{j}},t}} = 2{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_3},{p_{{\text{c}},t}} = 7{\text{ }}{\rm{mW}}} \right) $ 1 0 19 $ \left( {{f_{{\text{j}},t}} = {F_5},{p_{{\text{j}},t}} = 6{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_5},{p_{{\text{c}},t}} = 21{\text{ }}{\rm{mW}}} \right) $ 0.87 0.13 10 $ \left( {{f_{{\text{j}},t}} = {F_3},{p_{{\text{j}},t}} = 4{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_3},{p_{{\text{c}},t}} = 14{\text{ }}{\rm{mW}}} \right) $ 1 0 20 $ \left( {{f_{{\text{j}},t}} = {F_5},{p_{{\text{j}},t}} = 8{\text{ }}{\rm{mW}},{f_{{\text{c}},t}} = {F_5},{p_{{\text{c}},t}} = 28{\text{ }}{\rm{mW}}} \right) $ 0.76 0.24 平均概率 0.94 0.06 注：表中有部分结果为0，实际上其值为小于${10^{ - 3}}$的值，对结果的影响极小。为了表述方便，本文将其忽略。
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