# CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks

arXiv: https://arxiv.org/abs/2509.10508 · HTML: https://mlsanity.com/paper/2509.10508/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 57 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://mlsanity.com/paper/2106.05370/) (2021)
- [Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration](https://mlsanity.com/paper/2111.09676/) (2021)
- [Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment](https://mlsanity.com/paper/2001.09251/) (2020)
- [AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective](https://mlsanity.com/paper/2309.10575/) (2023)
- [Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks](https://mlsanity.com/paper/2006.12653/) (2020)
- [Learning to Beamform in Heterogeneous Massive MIMO Networks](https://mlsanity.com/paper/2011.03971/) (2020)
- [Sub-6GHz Assisted mmWave Hybrid Beamforming with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2404.17138/) (2024)
- [Hierarchical Beam Alignment for Millimeter-Wave Communication Systems: A Deep Learning Approach](https://mlsanity.com/paper/2308.12198/) (2023)
- [Frame Structure Design and Analysis for Millimeter Wave Cellular Systems](https://mlsanity.com/paper/1512.05691/) (2015)

## Abstract (arXiv)

Heterogeneous Vehicular Networks (HetVNets) play a key role by stacking different communication technologies such as sub-6GHz, mm-wave and DSRC to meet diverse connectivity needs of 5G/B5G vehicular networks. HetVNet helps address the humongous user demands-but maintaining a steady connection in a highly mobile, real-world conditions remain a challenge. Though there has been ample of studies on beam prediction models a dedicated solution for HetVNets is sparsely explored. Hence, it is the need of the hour to develop a reliable beam prediction solution, specifically for HetVNets. This paper introduces a lightweight deep learning-based solution termed-"CAR-BRAINet" which consists of convolutional neural networks with a powerful multi-head attention (MHA) mechanism. Existing literature on beam prediction is largely studied under a limited, idealised vehicular scenario, often overlooking the real-time complexities and intricacies of vehicular networks. Therefore, this study aims to mimic the complexities of a real-time driving scenario by incorporating key factors such as prominent MAC protocols-3GPP-C-V2X and IEEE 802.11BD, the effect of Doppler shifts under high velocity and varying distance and SNR levels into three high-quality dynamic datasets pertaining to urban, rural and highway vehicular networks. CAR-BRAINet performs effectively across all the vehicular scenarios, demonstrating precise beam prediction with minimal beam overhead and a steady improvement of 17.9422% on the spectral efficiency over the existing methods. Thus, this study justifies the effectiveness of CAR-BRAINet in complex HetVNets, offering promising performance without relying on the location angle and antenna dimensions of the mobile users, and thereby reducing the redundant sensor-latency.