[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84687-en":3,"doc-seo-84687-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84687,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Deep Unfolded Wideband ISAC Beamforming for DMA Under Frequency-Selective Lorentzian Model","Integrated sensing and communications (ISAC) is strengthened by dynamic metasurface antennas (DMAs), yet common DMA designs assume frequency-flat element responses, limiting accuracy in wideband and ultra-wideband operation. This work analyzes a DMA-based wideband ISAC system using a frequency-selective Lorentzian response model that captures frequency-dependent DMA behavior. The goal jointly optimizes communication users’ SINR and the radar target’s SNR through projected-gradient ascent derivations and a deep-unfolded PGA architecture, achieving ~20% gain and up to 20× faster convergence.","Deep-Unfolded Wideband ISAC Beamforming for DMA Under Frequency-Selective Lorentzian Model  \nAbdolrasoul Sakhaei Gharagezlou, Graduate Student Member, IEEE, Pouya Mobaraki, Graduate Student Member, IEEE, Mehdi Monemi, Member, IEEE, Nhan Thanh Nguyen, Senior Member, IEEE, Mehdi Rasti, Senior Member, IEEE, Samad Ali, Member, IEEE, Matti Latva-aho, Fellow, IEEE  \narXiv :2607 .03389v 1 [ cs .IT] 3 Jul 2026  \nAbstract—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 frequencydependent 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.  \nIndex Terms—Integrated sensing and communications (ISAC), dynamic metasurface antennas (DMAs), frequencyselective Lorentzian model, projected gradient ascent (PGA), deep unfolding.  \nI. Introduction  \nNEXT-generation wireless systems must support a  \ngrowing number of stationary and mobile users, provide high data rates, and enable sensing and cognition capabilities [1] . Integrated sensing and communication (ISAC) integrates radar sensing and wireless communication capabilities within a unified system infrastructure, offering an efficient solution to meet these growing demands. To further improve performance, technologies such as millimeter-wave and terahertz bands, along with extremely large antenna arrays, are expected to be widely adopted. These technologies provide larger  \nAbdolrasoul Sakhaei Gharagezlou, Pouya Mobaraki, Mehdi Monemi, Nhan T. Nguyen, Mehdi Rasti, Samad Ali and Matti Latva-ahoare with Centre for Wireless Communications, University of Oulu, Finland (e-mail: {abdolrasoul.sakhaeigharagezlou; pouya.mobaraki; mehdi.monemi; nhan.nguyen; mehdi.rasti; samad.ali; matti.latva[aho}@oulu.fi](aho}@oulu.fi)).  \nbandwidths, reduce spectrum congestion, enable highly directional beams, and help overcome propagation losses [2] . Through the joint use of hardware resources and frequency bands, ISAC systems can improve spectrum efficiency while simultaneously lowering hardware complexity, energy consumption, and deployment costs [3] . The development of ISAC has motivated the adaptation of various beamforming architectures, but existing solutions face tradeoffs in beamforming flexibility, hardware cost, power consumption, and scalability [4], [5], [6], [7] . This highlights the need for antenna architectures that better balance performance, complexity, and energy efficiency.  \nDynamic metasurface antennas (DMAs) are a promising technology for realizing large-scale antenna arrays in a controllable, scalable, and hardware-efficient manner [8],[9] . They use metamaterial radiating elements emb","cbCais8mt3ABYgLp","https://ap.wps.com/l/cbCais8mt3ABYgLp","pdf",781333,1,14,"English","en",105,"# Introduction\n## Related Work","[{\"question\":\"What problem does the frequency-flat DMA assumption cause in wideband ISAC systems?\",\"answer\":\"It can lead to significant phase and magnitude mismatches because the model is accurate only for narrowband scenarios.\"},{\"question\":\"How does the paper model DMA elements for wideband ISAC design?\",\"answer\":\"It uses a frequency-selective Lorentzian response model to accurately represent frequency-dependent behavior of DMA elements.\"},{\"question\":\"What approach is proposed to optimize communication and sensing performance jointly?\",\"answer\":\"An alternating optimization framework based on projected gradient ascent derives gradients for digital beamforming vectors and Lorentzian parameters, and a deep-unfolded PGA architecture learns key hyperparameters to accelerate convergence.\"}]",1784197657,35,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"deep-unfolded-wideband-isac-beamforming-for-dma-under-frequency-selective-lorentzian-model","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/deep-unfolded-wideband-isac-beamforming-for-dma-under-frequency-selective-lorentzian-model/84687/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the frequency-flat DMA assumption cause in wideband ISAC systems?","Question",{"text":75,"@type":76},"It can lead to significant phase and magnitude mismatches because the model is accurate only for narrowband scenarios.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper model DMA elements for wideband ISAC design?",{"text":80,"@type":76},"It uses a frequency-selective Lorentzian response model to accurately represent frequency-dependent behavior of DMA elements.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach is proposed to optimize communication and sensing performance jointly?",{"text":84,"@type":76},"An alternating optimization framework based on projected gradient ascent derives gradients for digital beamforming vectors and Lorentzian parameters, and a deep-unfolded PGA architecture learns key hyperparameters to accelerate 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