System-Level Comparison of Multimodal and In-Band mmWave Sensing for Beam Prediction in 6G ISAC

Integrated sensing and communication (ISAC) can reduce beam-training overhead in mmWave vehicle-to-infrastructure (V2I) links by enabling in-band sensing-based beam prediction, while exteroceptive sensors can further enhance the prediction accuracy. This work develop a system-level framework that evaluates camera, LiDAR, radar, GPS, and in-band mmWave power, both individually and in multimodal fusion using the DeepSense-6G Scenario-33 dataset. A latency-aware neural network composed of lightweight convolutional (CNN) and multilayer-perceptron (MLP) encoders predict a 64-beam index. We assess performance using Top-k accuracy alongside spectral-efficiency (SE) gap, signal-to-noise-ratio (SNR) gap, rate loss, and end-to-end latency. Results show that the mmWave power vector is a strong standalone predictor, and fusing exteroceptive sensors with it preserves high performance: mmWave alone and mmWave+LiDAR/GPS/Radar achieve 98% Top-5 accuracy, while mmWave+camera achieves 94% Top-5 accuracy. The proposed framework establishes calibrated baselines for 6G ISAC-assisted beam prediction in V2I systems.

Mobility-Aware Localization in mmWave Channel: Adaptive Hybrid Filtering Approach

Precise user localization and tracking enhances energy-efficient and ultra-reliable low latency applications in the next generation wireless networks. In addition to computational complexity and data association challenges with Kalman-filter localization techniques, estimation errors tend to grow as the user's trajectory speed increases. By exploiting mmWave signals for joint sensing and communication, our approach dispenses with additional sensors adopted in most techniques while retaining high resolution spatial cues. We present a hybrid mobility-aware adaptive framework that selects the Extended Kalman filter at pedestrian speed and the Unscented Kalman filter at vehicular speed. The scheme mitigates data-association problem and estimation errors through adaptive noise scaling, chi-square gating, Rauch-Tung-Striebel smoothing. Evaluations using Absolute Trajectory Error, Relative Pose Error, Normalized Estimated Error Squared and Root Mean Square Error metrics demonstrate roughly 30-60% improvement in their respective regimes indicating a clear advantage over existing approaches tailored to either indoor or static settings.

Autonomous Self-Trained Channel State Prediction Method for mmWave Vehicular Communications

Establishing and maintaining 5G mmWave vehicular connectivity poses a significant challenge due to high user mobility that necessitates frequent triggering of beam switching procedures. Departing from reactive beam switching based on the user device channel state feedback, proactive beam switching prepares in advance for upcoming beam switching decisions by exploiting accurate channel state information (CSI) prediction. In this paper, we develop a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station (gNB) collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model. The proposed framework exploits the CSI feedback from vehicular users combined with overhearing the C-V2X cooperative awareness messages (CAMs) they broadcast. We implement and evaluate the proposed framework using deepMIMO dataset generation environment and demonstrate its capability to provide accurate CSI prediction for 5G mmWave vehicular users. CSI prediction model is trained and its capability to provide accurate CSI predictions from various input features are investigated.