Dynamic Graph Convolutional Neural Networks (DGCNNs) are effective for processing graph-structured data, particularly point clouds used in immersive media, augmented reality (AR), and virtual reality (VR) applications. However, their inference can be computationally intensive, especially with high-resolution inputs, posing challenges for latency-sensitive and resource-constrained deployments. To address these challenges, we propose confidence-based early exit architectures tailored for fast inference in multimedia-oriented DGCNN systems. The proposed method accelerates inference by allowing samples to exit at intermediate layers once a confidence threshold is met, while preserving or even enhancing classification accuracy. Our best-performing configuration achieves a 3× speed-up alongside a 0.59% accuracy gain, while the most efficient design provides a 4.3× speed-up with only a minor accuracy trade-off. These results demonstrate the potential of early exiting as a lightweight and scalable solution for point-cloud inference in future multimedia communication systems.

The Internet of Things (IoT) integrates deep learning (DL) to enhance real-time data processing across diverse applications. However, deploying DL models on resourceconstrained IoT devices remains challenging, especially for tasks such as Angle-of-Arrival (AoA) estimation in dynamic environments. Variations in deployment conditions, such as changing modulation schemes, lead to domain shifts that degrade traditional models' performance, underscoring the need for adaptive, low-complexity DL frameworks. This paper introduces a novel compact phase and amplitude representation within a Prototypical Network-based approach, optimized for domain-adaptive AoA prediction in IoT and validated using real data from a softwaredefined radio (SDR) testbed. Compared to covariance and raw IQ data, our proposed representation reduces Mean Absolute Error (MAE) by approximately 32 % and 55 %, respectively, in unseen modulation scenarios. Further, evaluations on an SDR dataset collected using a$2 \times 2$Uniform Rectangular Array (URA) configuration with seven modulation schemes demonstrate that Prototypical Networks with few-shot learning enable accurate and robust adaptation with minimal data, maintaining high accuracy across both seen and unseen modulations

Deep learning (DL) techniques have recently emerged to efficiently manage mmWave beam transmissions without requiring time consuming beam sweeping strategies. A fundamental challenge in these methods is their dependency on hardware-specific training data and their limited ability to generalize. Large drops in performance are reported in literature when DL models trained in one antenna environment are applied in another. This paper proposes the application of Prototypical Networks to address this challenge – and utilizes the DeepBeam real-world dataset [1] to validate the developed solutions. Prototypical Networks (PN) excel in extracting features to establish class-specific prototypes during the training, resulting in precise embeddings that encapsulate the defining features of the data. We demonstrate the effectiveness of PN to enable generalization of deep beam predictors across unseen antennas. Our approach, which integrates data normalization and prototype normalization with the PN, achieves an average beam classification accuracy of 74.11% when trained and tested on different antenna datasets. This is an improvement of 398% compared to baseline performances reported in literature that do not account for such domain shifts. To the best of our knowledge, this work represents the first demonstration of the value of Prototypical Networks for domain adaptation in wireless networks, providing a foundation for future research in this area.

Automatic modulation classification (AMC) plays a critical role in wireless communications by autonomously classifying signals transmitted over the radio spectrum. Deep learning (DL) techniques are increasingly being used for AMC due to their ability to extract complex wireless signal features. However, DL models are computationally intensive and incur high inference latencies. This paper proposes the application of early exiting (EE) techniques for DL models used for AMC to accelerate inference. We present and analyze four early exiting architectures and a customized multi-branch training algorithm for this problem. Through extensive experimentation, we show that signals with moderate to high signal-to-noise ratios (SNRs) are easier to classify, do not require deep architectures, and can therefore leverage the proposed EE architectures. Our experimental results demonstrate that EE techniques can significantly reduce the inference speed of deep neural networks without sacrificing classification accuracy. We also thoroughly study the trade-off between classification accuracy and inference time when using these architectures. To the best of our knowledge, this work represents the first attempt to apply early exiting methods to AMC, providing a foundation for future research in this area.