## 3 Multi-Node Collaborative Compound Jamming Recognition Based on Time-Frequency Graph and MAML
### Compound Jamming Recognition Based on Time-Frequency Image and MAML  
This article used the MAML algorithm to complete the recognition of compound jamming signals; the diagram is shown in Figure 7. First, we used single jamming to train the network parameters. Single jamming includes Single Tone Jamming (STI), Multi-Tone Jamming (MTJ), Partial Band Noise Jamming (PBNJ), Pulse Jamming (PJ), Comb Spectrum Noise Jamming (CSNJ), Linear Frequency Modulation Jamming (LFM)), Noise Frequency Modulation Jamming (NFMJ), and Sinusoidal Frequency Modulation Jamming (SFMJ). Four types of jamming were combined into the tasks each time. Use these tasks as the sample to train the network parameter \(\theta\). Predict the support set of the \(v\)th and \(y\)th tasks through the network, and obtain loss function \(\mathcal{L}^{y}(f_{\theta_{v}^{y}})\). Upgrade parameters as function (3), and apply the upgraded network parameter \(\hat{\theta}_{v}^{y}\) to the search set of the \(v\)th and \(y\)th tasks. Obtain loss function \(\mathcal{L}(f_{\theta_{v}})\) on task \(y\). Finally, according to function (5), obtain the \(v\)th jamming signal task loss function \(\mathcal{L}(f_{\theta_{v}})\), and complete the upgrade of the MAML parameter according to function (4).  
Figure 6: Two kinds of MAML parameter update processes.  
Figure 8 shows the initial network parameter update diagram of the compound recognition algorithm based on MAML. The network parameters obtained after meta-training are then used to initialize the network, and the support set and query set of the new task are composed of four kinds of compound signals to be classified, including STJ-PJ, STJ-LFMJ, MTJ-PJ, and MTJ-LFMJ. Finally, fine-tune the network parameters with the support set in the new task.  
Multi-Node Collaborative Compound Jamming Recognition Algorithm Based on MAML and Time-Frequency Analysis  
6G communication normally involves multiple nodes. These nodes can be land units, sea units, or air units. Through 6G communication, information is transferred and shared between these nodes. Similarly, the nodes in the 6G communication field can be used to collect the jamming signals as well. Based on this, we integrated the idea of multi-node collaboration [18; 19; 20] into the compound jamming recognition in the condition of few-shot learning, and proposed a multi-node collaborative compound jamming recognition algorithm based on the time-frequency image and MAML. As shown in Figure 9, the trained and fine-tuned MAML model will be distributed to each communication node. Each cognitive node has the capability of collecting jamming signals and recognizing compound jamming signals through the MAML method independently. After this, the compound jamming recognition results will be sent to the fusion center. The fusion center fuses the jamming recognition results from each node, and produces the final jamming recognition result. The specific flow of Algorithm 1 is shown in the following table.  
Figure 8: Initial network parameter update diagram of the compound jamming recognition algorithm based on time–frequency image and MAML.  
Figure 7: Diagram of MAML compound jamming recognition.