Anti-Jamming Hybrid Beamforming for Millimeter-Wave Multi-Subarray Systems

Abstract

In millimeter-wave (mmWave) multi-subarray systems, the beamforming space is limited by hardware constraints, and the anti-jamming performance degrades due to direction-of-arrival (DoA) estimation errors. To tackle these challenges, this letter proposes two robust hybrid beamforming (HBF) approaches, namely interior-point (IP)-based HBF and deep unfolding network (DUN)-based HBF. The IP-based HBF leverages Riemannian manifold optimization to design the analog beamforming matrix under hardware constraints, and employs the interior-point algorithm to maximize the signal-to-interference-plus-noise ratio (SINR) under strict jamming suppression constraints. The DUN-based HBF unfolds the gradient ascent algorithm into a deep network, significantly reducing computational complexity with slight performance degradation. Simulation results demonstrate that the IP-based HBF achieves superior anti-jamming performance, while the DUN-based HBF achieves a favorable trade-off between anti-jamming performance and computational complexity. Both proposed methods outperform the baseline schemes, providing flexible and effective anti-jamming solutions for practical mmWave multi-subarray systems.