Deep-Unfolded Wideband ISAC Beamforming for DMA Under Frequency-Selective Lorentzian Model
2026-07-03 • Information Theory
Information Theory
AI summaryⓘ
The authors studied a new kind of antenna called dynamic metasurface antennas (DMAs) used for both communication and sensing in wireless networks. They found that previous models that assumed the antenna behaves the same across all frequencies were not accurate for wideband signals, so they used a more realistic frequency-selective model based on Lorentzian responses. They developed a method to optimize the system’s performance balancing communication quality and radar detection, using a mathematical technique that learns faster and works better. Their results showed that this new model and method improve performance and speed compared to older approaches.
Integrated Sensing and Communications (ISAC)Dynamic Metasurface Antennas (DMA)Frequency-selective Lorentzian modelWideband systemsSignal-to-Interference-plus-Noise Ratio (SINR)Signal-to-Noise Ratio (SNR)Projected Gradient Ascent (PGA)Deep unfoldingBeamformingOptimization
Authors
Abdolrasoul Sakhaei Gharagezlou, Pouya Mobaraki, Mehdi Monemi, Nhan T. Nguyen, Mehdi Rasti, Samad Ali, Matti Latva-aho
Abstract
Integrated sensing and communications (ISAC), empowered by dynamic metasurface antennas (DMAs), has emerged as a promising paradigm for next-generation wireless networks. However, existing DMA-based designs commonly rely on the frequency-flat response model for DMA elements, which is accurate only in narrowband scenarios and can cause significant phase and magnitude mismatches in wideband and ultra-wideband systems. This paper investigates a DMA-based wideband ISAC system under a frequency-selective Lorentzian response model, which accurately captures the frequency-dependent behavior of DMA elements. We aim to jointly balance the aggregate signal-to-interference-plus-noise ratio (SINR) of communication users and the signal-to-noise ratio (SNR) of the radar target. To this end, we first develop an alternating optimization framework based on projected gradient ascent (PGA), deriving closed-form gradients of the objective function with respect to the digital beamforming vectors, resonance frequencies, and damping factors under the frequency-selective Lorentzian DMA model. We then propose an unfolded PGA architecture that preserves the interpretability of model-based optimization while learning key hyperparameters to accelerate convergence. Simulation results show that the frequency-selective Lorentzian model improves performance by approximately 20\% over its frequency-flat approximation. Moreover, deep-unfolded PGA achieves up to 20-fold faster convergence and improves the objective value by up to 7\% compared with PGA-based benchmarks.