Current image fusion methods struggle to adapt to real-world environments encompassing diverse degradations with spatially varying characteristics. To address this challenge, we propose a robust fusion controller (RFC) capable of achieving degradation-aware image fusion through fine-grained language instructions, ensuring its reliable application in adverse environments. Specifically, RFC first parses language instructions to innovatively derive the functional condition and the spatial condition, where the former specifies the degradation type to remove, while the latter defines its spatial coverage. Then, a composite control priori is generated through a multi-condition coupling network, achieving a seamless transition from abstract language instructions to latent control variables. Subsequently, we design a hybrid attention-based fusion network to aggregate multi-modal information, in which the obtained composite control priori is deeply embedded to linearly modulate the intermediate fused features. To ensure the alignment between language instructions and control outcomes, we introduce a novel language-feature alignment loss, which constrains the consistency between feature-level gains and the composite control priori. Extensive experiments on publicly available datasets demonstrate that our RFC is robust against various composite degradations, particularly in highly challenging flare scenarios.

A radio environment map (REM) is a visualization method that display electromagnetic properties, such as received signal strength, channel gain, and power spectrum density in combination with geographic information. The map can effectively support modern Internet of Things (IoT) network planning and resource management. A novel REM construction scheme of an arbitrary height and frequency in 3-D space is studied in this article. First, a complex urban environment is considered, where the radiation sources transmit wireless signals in different frequency bands. Then, the construction is sliced into 2-D planes with various elevations to achieve precise and efficient sensing of 3-D space. For near-ground scenarios, preliminary global interpolation based on linear unbiased estimation is first performed to obtain a coarse REM, and then graph neural networks are utilized to further extract the relationships and features of the spatial nodes to improve the construction accuracy. For high-altitude scenarios, a small range of interpolation is carried out on the basis of linear unbiased estimation with the clustering center obtained by clustering the known sensing nodes as the center of the circle. Then the global construction is implemented via domain transformation processing to increase the construction speed. Finally, the 2-D REMs are stacked in sheets according to elevation to form a 3-D REM. The simulation results demonstrate the effectiveness and superiority of the proposed scheme.

Abstract Based on the successful application of generative adversarial network (GAN) models in the field of image generation, this article introduces GANs into the field of deep learning for communication systems and surveys its application in modulation classification. To solve the difficulties in feature extraction, to address the low recognition accuracy of existing radio signal modulation‐type recognition methods, and to adapt to complex electromagnetic environments with high noise interference intensity, this article presents a modulation recognition model for high‐order digital signals. This model uses the Morlet wavelet transform to analyse time‐frequency signals, uses the excellent image generation performance of a GAN model to extract and reconstruct the features of noise‐contaminated time‐frequency images, and designs an integrated classification network architecture to classify and predict reconstructed images. The experimental results show that the algorithm model proposed in this article can significantly improve the recognition accuracy of high‐order digital modulated signals under low signal‐to‐noise ratio conditions and can achieve 90% recognition accuracy at a signal‐to‐noise ratio of 1 dB.