
Cognitive Learning: A New Paradigm for Machine Learning in Electromagnetic Spectrum Environment
RUAN Tian-chen, WU Qi-hui, ZHAO Shi-jin, ZHOU Fu-hui, HUANG Yang
ACTA ELECTRONICA SINICA ›› 2023, Vol. 51 ›› Issue (6) : 1430-1442.
Cognitive Learning: A New Paradigm for Machine Learning in Electromagnetic Spectrum Environment
With the exponential growth in the number of wireless devices and wireless applications, and the emergence of various wireless technologies, the electromagnetic spectrum environment has presented complex characteristics such as multi-domain environments and situations, diverse applications, changeable behaviors, and dense signals. Spectrum resource control through immediate and effective analysis and processing of massive data is vital for efficient spectrum utilization and electromagnetic spectrum dominance. Intelligent technology represented by machine learning methods provides new opportunities to analyze electromagnetic spectrum data. Machine learning methods have been applied to wireless networks, spectrum management, resource management, and other scenarios. There are many challenges when applying machine learning to the electromagnetic spectrum environment. Because of the dynamically changing environment and tasks, the scarcity of high-quality labeled samples, real-time spectrum decision-making, and the difficulty of knowledge utilization and transfer in the electromagnetic spectrum environment, existing machine learning algorithms are not ideal for intelligent cognitive decision-making. Taking the research on interference recognition as an example, the traditional methods include two stages: signal feature extraction and pattern recognition. With the growth in computer computing power, deep learning techniques gradually replace traditional methods because of their powerful feature characterization capability. However, the electromagnetic spectrum environment and data often have uncertainties caused by unknown, dynamic changes. Adapting deep learning to unobserved tasks quickly is challenging, indicating poor generalization capability. Furthermore, it is limited by its reliance on a large number of high-quality labeled samples and manual adjustment of hyperparameters for offline training. Thus, although deep learning has far outperformed traditional methods in most research scenarios, traditional machine learning-based image recognition models still have advantages in some scenarios. In interference recognition, if traditional machine learning algorithms and deep learning algorithms and their hyperparameters can be dynamically scheduled according to the environment and task characteristics, the shortcomings of using a single algorithm can be avoided, resulting in improved overall performance. It is necessary to propose a learning paradigm that continuously adapts to dynamic environments and tasks, accumulates new knowledge, is robust to erroneous sample data, and is partially interpretable. The leap from perceptual intelligence to cognitive intelligence toward general artificial intelligence has research significance and practical value.
Given the characteristics of the electromagnetic spectrum, this paper proposes a new paradigm of machine learning method in the electromagnetic spectrum environment—cognitive learning, attempting to use machine learning techniques at a “cognitive” level. It includes offline training, online execution, online feedback and offline self-learning processes, corresponding to the executive control and introspective processes of the brain's cognitive mechanisms. Offline training explores the matching relationship between the algorithm type and hyperparameters with the environment and the task. During the online process, it can perform decision-making based on the actual environment, select appropriate algorithms and hyperparameters, and output learning results. The feedback of learning results can be used for self-optimization in offline self-learning, forming an offline-online-offline structure.The cognitive feature extraction module establishes matching relationships between features of environments and tasks and algorithm types and hyperparameters to obtain the appropriate algorithm types and hyperparameters. The memory module calls specific algorithms and hyperparameter values for algorithm reconstruction. The learning network module executes the selected algorithm based on the input data and outputs online learning results.
The cognitive evaluation module introspects the online learning results and adjusts the data in the memory database and cognitive case base, and updates the most appropriate algorithm and hyperparameters. In the offline stage, the current learning results are judged whether to be stored as a new cognitive case in the memory. The cognitive control module retrains the selection of algorithms and hyperparameters to continuously optimize the performance of selecting algorithms and hyperparameters.
Moreover, we reveal and confirm the dynamic matching theorem and the optimal matching theorem in cognitive learning according to the No Free Lunch Theorem. The dynamic matching theorem states the existence and dynamics of optimal algorithms and hyperparameters. For any environment or task, there is always a set of algorithms and hyperparameters with optimal performance, which differs for different environments and tasks. The proposed cognitive learning paradigm can select appropriate algorithms and hyperparameters. The optimal matching theorem states that as the knowledge of algorithm and hyperparameter selection increases, the search space of candidate algorithm types and hyperparameters decreases, and the probability of selecting the most suitable algorithm type and hyperparameters increases.
A cognitive learning scheme for radio frequency interference identification is designed. Different algorithms and hyperparameters are adopted for different problems to generate test results and a cognitive case base based on the differential task requirements and signal modalities acquired by cognitive feature extraction. A large number of cognitive cases are used as training samples to train the neural network for algorithm and hyperparameter selection. A radial basis function neural network is used for the neural network structure, which has the advantage that the number of hidden units does not need to be set manually. The size of hidden layers can be automatically expanded according to the approximate linear correlation criterion when the number of training samples for various problem features increases, alleviating the problem of catastrophic forgetting in neural networks.
We performed interference recognition simulations with different datasets and different recognition requirements. The simulation results demonstrate that the proposed paradigm can adapt to dynamically changing environments and tasks, improve performance by self-learning, and alleviate the impact of mislabeled samples. The cognitive learning paradigm has a pioneering significance for developing machine learning methods applied to the electromagnetic spectrum environment. There remains room for improvement in cognitive learning. We must seek future research breakthroughs in multimodal computing, knowledge interpretability, large-scale tasks, and learning architecture scalability.
electromagnetic spectrum environment / machine learning / cognitive learning / algorithm selection / interference identification {{custom_keyword}} /
1 |
吴启晖, 任敬. 电磁频谱空间认知新范式: 频谱态势[J]. 南京航空航天大学学报, 2016, 48(5): 625-632.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
2 |
李京华, 丁国如, 徐以涛, 等. 面向电磁频谱战的群体智能初探[J]. 航空兵器, 2020, 27(4): 56-63.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
3 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
4 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
5 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
6 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
7 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
8 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
9 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
10 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
11 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
12 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
13 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
14 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
15 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
16 |
方芳, 李永贵, 牛英滔, 等. 基于决策树算法的干扰信号识别[J]. 通信技术, 2019, 52(11): 2617-2623.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
17 |
徐国进. 典型通信干扰信号识别技术研究[D]. 成都: 电子科技大学, 2018.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
18 |
朱鹏程. GNSS干扰检测与识别技术研究[D]. 重庆: 重庆大学, 2018.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
19 |
郝万兵, 马若飞, 洪伟. 基于时频特征提取的雷达有源干扰识别[J]. 火控雷达技术, 2017, 46(4): 11-15.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
20 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
21 |
练东海. 基于深度学习的无线通信干扰信号识别与处理[J]. 通信电源技术, 2021, 38(2): 104-107.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
22 |
党泽. 基于深度学习的无线通信干扰信号识别与处理技术研究[D]. 成都: 电子科技大学, 2020.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
23 |
杨岚, 肖海涛, 张渭乐. 基于频带相关性Deep Learning的无线通信干扰智能识别[J]. 航空科学技术, 2022, 33(4): 108-118.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
24 |
刘华祠. 基于传统机器学习与深度学习的图像分类算法对比分析[J]. 电脑与信息技术, 2019, 27(5): 12-15.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
25 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
26 |
崔建双, 刘晓婵, 杨美华, 等. 基于元学习推荐的优化算法自动选择框架与实证分析[J]. 计算机应用, 2017, 37(4): 1105-1110.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
27 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
28 |
任义, 迟翠容, 单菁, 等. 基于元学习的推荐算法选择优化框架实证[J]. 计算机工程与设计, 2020, 41(6): 1610-1616.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
29 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
30 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
31 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
32 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_ref.label}} |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
/
〈 |
|
〉 |