Dual-Side Beamforming and Fairness Optimization in Multi-User Millimeter Wave Massive MIMO for V2X-IoT Networks

Connected vehicles are mobile Internet-of-Things (IoT) nodes requiring reliable, high-capacity, low-latency vehicle-to-everything (V2X) communication for cooperative perception, safety messaging, traffic coordination, and data-intensive services. This article presents dual-side beamforming for multi-user millimeter-wave massive MIMO in V2X-IoT, targeting end-to-end beam alignment, capacity, spectral efficiency, and user fairness in challenging urban environments. A 64-element base-station array serves multiple vehicles: transmit precoding directs energy toward users, while receive combining strengthens desired signals and suppresses interference and noise. Unlike transmitter-only studies, this framework analyzes transmit–receive array interactions, evaluating beam alignment, array-response matching, and the effect of receiver processing on effective signal-to-interference-plus-noise ratio (SINR). We characterize the antenna in Ansys Electronics Desktop 2026 R1 using full-wave electromagnetic simulation, then integrate the results into Divergent Physics, an AI-driven Python RF-design environment. We evaluate multi-user beamforming, power allocation, and capacity across user locations and channel conditions, comparing Maximum Ratio Transmission (MRT), Zero-Forcing (ZF), Linear Minimum Mean Square Error (LMMSE), and fairness-oriented optimization. Dual-sided beamforming—joint BS transmit precoding and vehicle receive combining—delivers the best SINR, capacity, and spectral efficiency. Fairness-aware configurations reduce spectral efficiency across all methods by prioritizing balanced user performance. These configurations provide a scalable framework for analyzing interactions among antenna design, beamforming, vehicle deployment, and communication performance in mmWave V2X-IoT networks.