# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 1 Introduction
Traditional WSS techniques typically transform time-domain wireless signals into the frequency domain to extract statistical metrics from frequency subbands for decision-making. Although these methods are conceptually straightforward, they often fail to deliver optimal performance in complex channel environments. In contrast, deep learning-based approaches, which exploit the inherent structural information of wireless signals [20], achieve superior sensing performance. [PERSON] et al. proposed DeepSense, a real-time WSS framework that integrates deep learning with real-time closed-loop processing to directly handle I/Q data [21]. This framework incorporates spectrum sensing capabilities into the baseband processing of software-defined radios (SDR). To reduce the runtime of DeepSense, [PERSON] et al. introduced the ParallelCNN architecture, which is particularly suited for resource-constrained environments [22]. [PERSON] et al. developed a method that employs the short-time Fourier transform (STFT) to convert received signals into two-dimensional time-frequency matrices, which are subsequently normalized into grayscale images and used as inputs for deep neural networks [23]. This approach enhances the representation of signal features. Additionally, a spectrum sensing method based on an enhanced deep learning classification approach was proposed, utilizing a hybrid architecture of convolutional neural networks(CNNs) and recurrent neural networks (RNNs) [24]. This method enhances the accuracy and efficiency of spectrum sensing by facilitating feature extraction and time-series information capture, excelling in dynamic spectrum sensing. Furthermore, [PERSON] et al. introduced a WSS architecture based on the attention mechanism, referred to as \"Spectrum Transformer\" [25]. This model leverages multi-head self-attention mechanisms to capture both inter-band and intra-band correlations, thus achieving more accurate spectrum detection. Finally, [PERSON] et al. introduced CNNWSS-Net, a novel network that utilizes the frequency-domain representation of wireless signals as input, improving detection accuracy without increasing computational complexity [18]. This paper primarily investigates the CSS technology in cognitive radio and decomposes it into a compressed reconstruction problem and a WSS problem. The key contributions are as follows: (1) Considering the BSF of wideband spectrum signals, we propose a novel neural network architecture called block sparsity feature enhancement ISTA networks (BEISTA-Net). This architecture replaces the proximal mapping in ISTA with multiple convolutional operations and incorporates a BSF extractor composed of one-dimensional convolutional kernels of varying sizes to extract the BSF of wideband spectrum signals. Additionally, the method employs coordinate attention (CA) [26] to enhance the BSF and suppress noise. (2) We propose a network named block sparse wideband spectrum sensing network (BSWSS-Net) for WSS. BSWSS-Net utilizes one-dimensional convolutional kernels of multiple sizes to extract low-level structural information from wideband spectrum signals and fuses these features through fully connected layers to effectively capture channel state information. (3) Extensive experiments are conducted on simulated spectrum data across different sparsity levels and SNRs. Numerical results demonstrate that the BEISTA-Net outperforms existing reconstruction algorithms in terms of reconstruction accuracy. Furthermore, compared to current deep learning-based WSS algorithms, the proposed BSWSS-Net reduces the number of floating point operations by at least 50% while maintaining detection accuracy. The remainder of this paper is organized as follows: Section 2 introduces the system model. Section 3 presents the architecture and training methodology of BEISTA-Net and BSWSS-Net. Section 4 describes the datasets and performance metrics. Experiment results are provided in Section 5. Finally, the work of this paper is summarized in Section 6.