Low-Complexity Wireless Technique Classification With Multi-Feature Fusion Broad Learning Network

With the development of wireless technology and the Internet of Things (IoT), managing limited spectrum resources has become crucial. As the IoT landscape grows, more effective wireless technique classification (WTC) is imperative. Traditional Deep Learning (DL) methods for WTC, while robust, suffer from high computational complexity, making them less practical for real-time applications. Addressing this, our paper introduces a novel solution, the Multi-Feature Fusion Broad Learning Network (MFBLN) for WTC, which employs broad learning. Here, several features of the wireless technique are inputted into a multi-branch module to obtain classification information from different perspectives. Then, those features are integrated, which performs better than the typical Broad Learning structure. Our simulation results show that our proposed MFBLN method performs well on classic WTC datasets in the Intelligent Transportation System (ITS) band. The performance of MFBLN at a 25 Msps sampling rate shows an improvement of approximately 0.67%, coupled with a significant reduction in FLOPs by 81.93%, and a 72.53% decrease in training time. Additionally, ablation studies further affirm the necessity of each module within the MFBLN framework, underscoring their collective contribution to its enhanced efficiency and effectiveness.