Tri Nhu Do

Nghia Thinh Nguyen, Tri Nhu Do

In this paper, we propose a new Temporal MDS-Vision Transformer (T-MDS-ViT) for multiclass target classification using millimeter-wave FMCW radar micro-Doppler spectrograms. Specifically, we design a transformer-based architecture that processes stacked range-velocity-angle (RVA) spatiotemporal tensors via patch embeddings and cross-axis attention mechanisms to explicitly model the sequential nature of MDS data across multiple frames. The T-MDS-ViT exploits mobility-aware constraints in its attention layer correspondences to maintain separability under target overlaps and partial occlusions. Next, we apply an explainable mechanism to examine how the attention layers focus on characteristic high-energy regions of the MDS representations and their effect on class-specific kinematic features. We also demonstrate that our proposed framework is superior to existing CNN-based methods in terms of classification accuracy while achieving better data efficiency and real-time deployability.

Tri Nhu Do

In this paper, we propose the concept of AIO-RAN-NTN, a unified all-in-one Radio Access Network (RAN) for Non-Terrestrial Networks (NTNs), built on an open architecture that leverages open interfaces and artificial intelligence (AI)-based functionalities. This approach advances interoperability, flexibility, and intelligence in next-generation telecommunications. First, we provide a concise overview of the state-of-the-art architectures for Open-RAN and AI-RAN, highlighting key network functions and infrastructure elements. Next, we introduce our integrated AIO-RAN-NTN blueprint, emphasizing how internal and air interfaces from AIO-RAN and the 3rd Generation Partnership Project (3GPP) can be applied to emerging environments such as NTNs. To examine the impact of mobility on AIO-RAN, we implement a testbed transmission using the OpenAirInterface platform for a standalone (SA) New Radio (NR) 5G system. We then train an AI model on realistic data to forecast key performance indicators (KPIs). Our experiments demonstrate that the AIO-based SA architecture is sensitive to mobility, even at low speeds, but this limitation can be mitigated through AI-driven KPI forecasting.

Ngoc Tuyen Do, Tri Nhu Do

In the surveillance and defense domain, multi-target detection and classification (MTD) is considered essential yet challenging due to heterogeneous inputs from diverse data sources and the computational complexity of algorithms designed for resource-constrained embedded devices, particularly for Al-based solutions. To address these challenges, we propose a feature fusion and knowledge-distilled framework for multi-modal MTD that leverages data fusion to enhance accuracy and employs knowledge distillation for improved domain adaptation. Specifically, our approach utilizes both RGB and thermal image inputs within a novel fusion-based multi-modal model, coupled with a distillation training pipeline. We formulate the problem as a posterior probability optimization task, which is solved through a multi-stage training pipeline supported by a composite loss function. This loss function effectively transfers knowledge from a teacher model to a student model. Experimental results demonstrate that our student model achieves approximately 95% of the teacher model's mean Average Precision while reducing inference time by approximately 50%, underscoring its suitability for practical MTD deployment scenarios.

Ngoc Long Pham, Tri Nhu Do

Neural network (NN)-based end-to-end (E2E) communication systems, in which each system component may consist of a portion of a neural network, have been investigated as potential tools for developing artificial intelligence (Al)-native E2E systems. In this paper, we propose an NN-based bitwise receiver that improves computational efficiency while maintaining performance comparable to baseline demappers. Building on this foundation, we introduce a novel symbol-wise autoencoder (AE)-based E2E system that jointly optimizes the transmitter and receiver at the physical layer. We evaluate the proposed NN-based receiver using bit-error rate (BER) analysis to confirm that the numerical BER achieved by NN-based receivers or transceivers is accurate. Results demonstrate that the AE-based system outperforms baseline architectures, particularly for higher-order modulation schemes. We further show that the training signal-to-noise ratio (SNR) significantly affects the performance of the systems when inference is conducted at different SNR levels.