基于频率选择性洛伦兹模型的深度展开宽带ISAC波束形成在DMA中的应用
Deep-Unfolded Wideband ISAC Beamforming for DMA Under Frequency-Selective Lorentzian Model
AI总结：
研究基于频率选择性洛伦兹响应模型的宽带ISAC系统，通过交替优化框架平衡通信用户和雷达目标性能，提出深度展开PGA架构，提升性能并加速收敛。
摘要：
集成感知与通信（ISAC）借助动态超表面天线（DMA）成为下一代无线网络的有前景范式。现有基于DMA的设计常依赖频率平坦响应模型，本文研究基于频率选择性洛伦兹响应模型的宽带ISAC系统，旨在平衡通信用户的总信干噪比和雷达目标的信噪比，提出相关框架和架构并验证优势。
英文摘要：
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.
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