automotive communication engineers

Abstract: Connected vehicles use millimeter-wave (mmWave) sidelinks for the data rates cooperative driving demands, and emerging designs select the carrier band from sensed rainfall. We show that this weather awareness is an attack surface: an adversary who spoofs only the rainfall input dictates the victim's carrier frequency, and through it its communication range, without transmitting on the channel. We evaluate the attack in MilliCar, an ns-3 module that runs the selected band as the real 3GPP NR V2X carrier with per-band propagation, beamforming, and blockage. Forcing the band up to 73 GHz holds an eight-vehicle platoon's reliable range at 38 m while the honest baseline doubles it to 82 m; forcing it down to 5 GHz sustains 97% long-range reception but collapses the transport block to a third and quadruples long-range latency to 12.5 ms. We then implement the defense the mechanism implies. Rain loss grows linearly with distance while path loss grows logarithmically, so a receiver that tests measured SINR against the attenuation its reported weather predicts flags force-up with 98% probability within 1.5 s at a 1% false-alarm rate, and re-selection then restores long-range reception from 60% to 75%. The same test is structurally blind to force-down, because the 5 GHz fallback is nearly rain-immune. An advecting rain cell that swings the local rate from 15 to 81 mm/h leaves every result unchanged. Weather-aware band selection therefore requires an authenticated meteorological input; physical cross-checking covers one half of the threat.

Abstract: Integrated sensing and communication (ISAC) provides a promising foundation for beam prediction in future vehicle-to-everything (V2X) networks. However, existing sensing-assisted beamforming methods still rely heavily on radio-frequency sensing, which may become unreliable in complex vehicular environments. Meanwhile, the growing availability of heterogeneous sensors, such as cameras and LiDAR, offers new opportunities to improve beam prediction through richer environmental perception. Motivated by this, this paper proposes a multi-modal beam prediction framework for V2X networks. Specifically, we develop BeamTransFuser, a hierarchical Transformer-based architecture that progressively fuses camera, LiDAR, radar, and GPS observations for robust beam prediction. In addition, to handle possible missing modalities in practical deployment, we introduce a generative module that reconstructs missing modality features from the available observations. Experimental results on a real-world multi-modal V2X dataset show that the proposed framework consistently outperforms representative baselines, while the generative module further improves robustness under incomplete sensing conditions.