Searcharxiv⌕ Search

arXiv · 2610.27445

Structured PARAFAC Channel Estimation for Flexible Intelligent Metasurface Receivers

Abstract

Flexible intelligent metasurfaces (FIMs) create geometric diversity through aperture morphing, but their configuration-dependent responses complicate uplink channel estimation. This letter considers multiple users sharing a common angular response and develops a structured PARAFAC model across calibrated FIM configurations. Its known identity factor and rank-one pilot-weighted channel factor enable deterministic, noniterative algebraic factor recovery through per-element rank-one approximations, reference-state normalization, global rank-one projection, and a least-squares morphing-factor update. The recovered spatial and morphing factors are then jointly matched to the calibrated manifold to estimate the common angles, user gains, and configuration-dependent channels. Simulations demonstrate accurate recovery and show that out-of-plane morphing resolves the azimuth-sign ambiguity of an undeformed planar aperture; an angular-spread experiment quantifies the mismatch when the common-response assumption is relaxed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xi Han, Junqiao Chen, Yuansheng Zhang, André L. F. de Almeida, George C. Alexandropoulos. 2026-10-20. Structured PARAFAC Channel Estimation for Flexible Intelligent Metasurface Receivers. https://arxiv.org/abs/2610.27445

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

ACHEM: A Real-Time Digital Twin Framework with Channel and Radio Emulation

Digital twins are becoming an important tool for designing, developing, testing, and optimizing next-generation wireless communication systems. Over the past decade, system softwarization has become a reality, and wireless communication systems are no exception. Software-Defined Radios (SDRs), in general, and Universal Software Radio Peripherals (USRPs), in particular, are often used for prototyping and testing advanced wireless systems. Unfortunately, there is currently no end-to-end, software-based, general-purpose testing environment for SDR-based systems: developers often rely on benchtop setups or even small testbeds, but those are costly and cumbersome to build. At the other end of the spectrum, simulations often rely on simplified channel/radio models and typically do not execute full-stack production code, which can increase development effort and reduce fidelity. In this paper, we propose ACHEM (A Channel Emulator), the first software-based, end-to-end wireless channel emulation environment and toolset for communication systems based on SDRs, specifically USRPs. With the proposed emulator and toolkit, any USRP-based system can be fully emulated at the I/Q level in a pure digital environment without requiring specialized hardware (e.g., vehicles, USRPs, FPGAs, or GPUs). The proposed emulator supports multiple transmitters and receivers, MIMO communications, multiple frequencies, heterogeneous sampling rates, real-time node mobility through vehicle emulation, antenna radiation patterns, and various channel models. ACHEM facilitates wireless digital twin development and deployment. ACHEM is validated with several popular open-source USRP-based wireless communication applications, including GNU Radio, srsRAN 4G/5G, and OpenAirInterface.

eess.SP↗

Unsupervised Learning for Scalable Downlink Power Control in Cell-Free Massive MIMO

In cell-free massive multiple-input multiple-output systems, downlink power control is essential to ensure uniformly high service quality across users. Existing solutions range from computationally demanding centralized optimization to scalable distributed schemes with limited fairness. This letter proposes an unsupervised, physics-informed framework that directly optimizes max-min fairness without optimal labels or user position information. The method scales with the number of UEs, requires no retraining when the number of UEs changes, and admits a fully scalable extension. Numerical results show that it nearly doubles the worst-user spectral efficiency over existing scalable schemes.

eess.SP↗

Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy

6G networks will introduce unprecedented complexity, which calls for a paradigm shift in network optimization and management. Artificial intelligence (AI)-based solutions, especially those enabled by the recently developed foundation models, have been recognized as promising candidates. Foundation models are large-scale AI models with general-purpose feature extraction capabilities, and once trained on massive amounts of data, they can be adapted to solve a wide range of downstream tasks, either in a zero-shot manner or with few-shot fine-tuning. This article provides a comprehensive overview of how foundation models are reshaping physical-layer processing and wireless resource management across three progressive paradigms. First, we examine the adaptation of off-the-shelf pre-trained foundation models to various wireless tasks. Second, we explore wireless-native foundation models, built from scratch on wireless data to bridge cross-domain modality gaps and capture universal wireless-domain physical characteristics. Third, we highlight agentic foundation models, which elevate static data processing into autonomous, reasoning-driven network orchestration. Furthermore, we discuss the impact of applying foundation models to emerging 6G frontiers, including integrated sensing and communications (ISAC), new multiple-input multiple-output (MIMO) architectures, semantic communications, and system-level network autonomy. Finally, we identify critical open challenges and opportunities, charting a promising path toward fully intelligent and adaptive wireless networks.

eess.SP↗