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Radiomap characterizes geographical radio spectrum coverage and can facilitate resource allocation and management of wireless networks. One practical radiomap estimation (RME) task is to form a full radiomap from sparse samples collected by sensors or mobile devices. Often, traditional RME approaches focus on statistical data distributions without exploiting the underlying spatial correlations among sparse observations. Utilizing geometric/geographical path correlation, this letter proposes a novel dual-phase RME method based on graph neural networks. In this Dual-phase Graph-based Radiomap Estimation (Dual-GRE) framework, the first phase integrates graph attention (GAT) networks with radio propagation models to construct a coarse-resolution (CR) radiomap to embed the spatial information and physical principles. Phase 2 utilizes a deep convolution neural network that uses the CR radiomap and landscape information to derive fine-resolution radiomaps. Our experimental results demonstrate the power of physics-integrated GAT in capturing the spatial spectrum information, together with the efficiency of the proposed Dual-GRE in radiomap estimation. 

The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GOCOM, this work introduces a novel noise-restricted diffusionbased GO-COM (Diff-GOn) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing highquality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GO n well-suited for real-time communications and downstream applications. 

Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this work, we propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), by focusing on the extraction of the semantics vital to the downstream tasks. Specifically, we adopt a Vector Quantized Variational Autoencoder (VQ-VAE) to compress media data at the transmitter side. Instead of targeting the pixel-wise image data reconstruction, we measure the quality-of-service at the receiver end based on a pre-defined task-incentivized model. Moreover, to capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality in terms of goal-oriented semantics. Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE.