BeamLLM: Vision-Empowered mmWave Beam Prediction with Large Language Models

In this paper, we propose BeamLLM, a vision-aided millimeter-wave (mmWave) beam prediction framework leveraging large language models (LLMs) to address the challenges of high training overhead and latency in mmWave communication systems. By combining computer vision (CV) with LLMs' cross-modal reasoning capabilities, the framework extracts user equipment (UE) positional features from RGB images and aligns visual-temporal features with LLMs' semantic space through reprogramming techniques. Evaluated on a realistic vehicle-to-infrastructure (V2I) scenario, the proposed method achieves 61.01% top-1 accuracy and 97.39% top-3 accuracy in standard prediction tasks, significantly outperforming traditional deep learning models. In few-shot prediction scenarios, the performance degradation is limited to 12.56% (top-1) and 5.55% (top-3) from time sample 1 to 10, demonstrating superior prediction capability.

Deep Learning-based Data-aided Activity Detection with Extraction Network in Grant-free Sparse Code Multiple Access Systems

This letter proposes a deep learning-based data-aided active user detection network (D-AUDN) for grant-free sparse code multiple access (SCMA) systems that leverages both SCMA codebook and Zadoff-Chu preamble for activity detection. Due to disparate data and preamble distribution as well as codebook collision, existing D-AUDNs experience performance degradation when multiple preambles are associated with each codebook. To address this, a user activity extraction network (UAEN) is integrated within the D-AUDN to extract a-priori activity information from the codebook, improving activity detection of the associated preambles. Additionally, efficient SCMA codebook design and Zadoff-Chu preamble association are considered to further enhance performance.

Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems

In grant-free sparse code multiple access system, joint optimization of contention resources for users and active user detection (AUD) at the receiver is a complex combinatorial problem. To this end, we propose a deep learning-based data-aided AUD scheme which extracts a priori user activity information via a novel user activity extraction network (UAEN). This is enabled by an end-to-end training of an autoencoder (AE), which simultaneously optimizes the contention resources, i.e., preamble sequences, each associated with one of the codebooks, and extraction of user activity information from both preamble and data transmission. Furthermore, we propose self-supervised pre-training scheme for the UAEN, which ensures the convergence of offline end-to-end training. Simulation results demonstrated that the proposed AUD scheme achieved 3 to 5dB gain at a target activity detection error rate of ${{10}^{-3}}$ compared to the state-of-the-art DL-based AUD schemes.