arXiv subjects

Jiancheng An

Publications and source records attributed to Jiancheng An.

At least 19 recordsLinked to original sources

arXiv2026

A Fully Wave-Domain Wideband MU MIMO OFDM Transmitter via Stacked Intelligent Metasurfaces

This paper proposes an advanced realization principle for wideband multiuser multiple-input multiple-output orthogonal frequency-division multiplexing (MU-MIMO OFDM) transmitters, where the conventional transmitter-side baseband chain is physically synthesized in the wave domain. For design and optimization purposes, this fully wave-domain wideband MU-MIMO OFDM transmitter implemented by a cascaded SIM structure is functionally partitioned into two cascaded SIM blocks. The first block, denoted as SIM_1, integrates symbol loading and channel-adaptive MU-MIMO precoding updated at the channel-coherence timescale, mapping the user streams to a virtual port-subcarrier representation. The second block, denoted as SIM_2, acts as an offline-configured sampling-rate modulator that materializes the inverse discrete Fourier transform (IDFT) and cyclic prefix (CP) insertion directly in the wave domain. This baseband-free architecture establishes a virtual-to-physical transition from information bits to radiated CP-extended OFDM waveforms. To account for practical nonidealities, SIM_2 is optimized to fit the ideal multi-port CP-OFDM operator, and its residual response is mapped into an effective coupling matrix. Then, SIM_1 is optimized in a communication-oriented manner by jointly adapting discrete phase shifts and stream-subcarrier power loading to maximize the sum spectral efficiency. Results demonstrate the convergence, architecture trade-off, wave-domain OFDM materialization accuracy, and competitive performance of the proposed baseband-free transmitter.

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arXiv2026

Joint Power Allocation and Phase-Shift Design for Beyond-Diagonal Stacked Intelligent Metasurfaces-Aided ISAC Systems

Stacked intelligent metasurfaces (SIM) provide an efficient architecture for integrated sensing and communication (ISAC) with few radio-frequency (RF) chains. However, diagonal SIM provide only element-wise phase control, so balancing multiuser communication and sensing performance may require additional layers. In this letter, we propose a beyond-diagonal SIM (BD-SIM) architecture for ISAC, enabling controllable intra-layer coupling through reconfigurable impedance networks, thereby enhancing wave-domain processing flexibility. We develop a unified alternating optimization framework applicable to fully-connected, group-connected, and diagonal SIM architectures. Within this framework, we derive a closed-form power allocation rule and propose an effective variable separation algorithm for multi-layer phase-shift design. Simulation results show that the proposed BD-SIM achieve a better communication-sensing trade-off and require fewer layers to attain performance comparable to conventional SIM.

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arXiv2026

Bistatic Integrated Sensing and Communications with Flexible Intelligent Metasurfaces

We propose a novel doubly-dispersive (DD) multiple-input multiple-output (MIMO) channel model incorporating flexible intelligent metasurfaces (FIMs), suitable for integrated sensing and communications (ISAC) in high-mobility scenarios. We show how the proposed FIM-parameterized DD (FPDD) channel model extends to multicarrier waveforms known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Leveraging this model, we formulate an achievable rate maxi-mization problem with a sensing constraint for all waveforms and solve it via gradient ascent with closed-form gradients. Numerical results indicate that FIM technology significantly impacts the achievable rate, with careful parametrization essential for strong ISAC performance across all waveforms.

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arXiv2026

Electromagnetic Neural Network for Direction-of-Arrival Estimation

Accurate and real-time direction of arrival (DOA) estimation is crucial for beamforming in unmanned aerial vehicle (UAV) communication systems. However, the existing high-precision DOA estimation algorithms encounter high computational complexity when implemented on a UAV with on-board signal processing constraints. To tackle this issue, an electromagnetic neural network (EMNN) is developed for DOA estimation, which is capable of generating the angular spectrum of the incident signal based solely on amplitude observation. Specifically, the proposed EMNN consists of two components: a stacked intelligent metasurfaces (SIM) is mounted on the UAV, and each meta-atom is an artificial neuron that can process signals in the electromagnetic domain with low energy consumption and ultra-fast computing speed. Furthermore, a fully connected layer is cascaded to process the received amplitude signal, enhancing the non-linear extraction and representational ability of EMNN. Moreover, to reduce the computational complexity and observation snapshots required for high-resolution DOA estimation, we develop a hierarchical DOA estimation framework, which involves two stages for conducting coarse and fine DOA estimation, respectively. For each stage, EMNN is trained on randomly generated training samples and their corresponding spectra to achieve the desired estimation goal. Finally, the simulation results validate that the proposed EMNN achieves approximately 13 dB gain in classification error reduction over the conventional beamforming (CBF) method in dual-signal scenarios, albeit its lower cost and radio frequency (RF)-related power consumption.

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arXiv2026

Baseband-Free Wideband MU-MIMO OFDM via Stacked Intelligent Metasurfaces

This paper proposes a novel baseband-free wideband MU-MIMO OFDM transmitter architecture enabled by two cascaded stacked intelligent metasurfaces (SIMs). Unlike conventional wireless transmitters, the proposed design shifts symbol loading, MU-MIMO precoding, and OFDM modulation from digital baseband to the wave domain. Specifically, in the first SIM (SIM$_1$), a programmable symbol-loading interface first loads one stacked virtual-subcarrier coefficient vector onto a common monochromatic carrier, while the remaining layers perform channel-adaptive MU-MIMO precoding from the data streams to the port-subcarrier domain. Then, the second SIM (SIM$_2$) realizes offline-configured wave-domain OFDM modulation by implementing the inverse discrete Fourier transform (IDFT) and cyclic-prefix (CP) insertion operator directly in the wave domain. Based on this architecture, we develop a wave-domain system model that explicitly characterizes the virtual-to-physical transition from block-level coefficients to radiated OFDM subcarriers. The designs of SIM$_1$ and SIM$_2$ are formulated as two operator-fitting problems, respectively targeting a block-diagonal wideband precoder and the ideal OFDM modulation operator. Under practical discrete phase constraints, we develop a quantization-aware training-based gradient descent (QAT-GD) framework for both SIM stages. Numerical results verify the effectiveness of the proposed optimization, the correctness of the synthesized wave-domain functionalities, and the strong end-to-end MU-MIMO OFDM performance of the resulting architecture.

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arXiv2026

Task-Oriented Wave Processing with Stacked Intelligent Metasurfaces: Framework, Fusion, and Challenges

The deep integration of diverse services in sixth-generation (6G) networks poses significant challenges to conventional task-agnostic channels, often resulting in performance conflicts. To resolve these bottlenecks, this article introduces a physical-layer computing paradigm enabled by stacked intelligent metasurfaces (SIMs), transforming the wireless environment from a passive medium into a programmable signal processor. Specifically, we establish a unified framework to map high-level service requirements directly to wave-domain synthesis. We then investigate the fusion of diverse services, demonstrating how the deep computational architecture of SIMs resolves resource conflicts in integrated sensing and communication (ISAC) and integrated communication and computation (ICC) scenarios. Furthermore, we critically analyze fundamental challenges, including diffractive channel modeling and inverse task-to-phase mapping, while validating through numerical results that this approach elevates the system from simple coexistence to true service symbiosis. Finally, we discuss key research directions to pave the way for service-native 6G architectures.

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arXiv2026

DRL-Based Joint Beamforming and Surface Shape Optimization for Flexible Intelligent Metasurface-Aided ISAC Systems

Integrated sensing and communication (ISAC) unifies high-precision sensing and wireless data transmission. In this paper, we investigate the design of ISAC systems enabled by flexible intelligent metasurface (FIM) and aim to minimize the Cram\'er-Rao bound (CRB) with quality of service (QoS) constraints using deep reinforcement learning (DRL). Specifically, we formulate the joint design of beamforming matrix and FIMs surface shape to reduce the CRB subject to transmit power, QoS and the FIMs surface shape constraints. However, the non-convex formulation makes optimization problem difficult to solve. To tackle this issue, we develop a deep deterministic policy gradient (DDPG) actor critic DRL scheme for the joint design, guided by a constraint aware reward to progressively improve sensing performance. Numerical results demonstrate that jointly optimizing the beamforming matrix and the FIMs surface shape substantially decreases CRB while ensuring communication quality compared with existing rigid arrays.

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arXiv2026

Sparse Channel Estimation for SIM-based mmWave Near-Field Communications

In this paper, we address the channel estimation (CE) problem in SIM-based multi-user (MU) millimeter-wave (mmWave) near-field communication systems. To address the severe path loss and blockage in mmWave communication systems, many meta-atoms are typically integrated into each layer of the SIM. Then, the number of radio frequency (RF) chains at the base station (BS) is fewer than that of meta-atoms per layer, resulting in an underdetermined problem. Additionally, the increase in the number of meta-atoms in each layer expands the SIM's near-field region, leading to the user equipment (UEs) being mostly situated in this region, necessitating precise modeling of the channel under the spherical wavefront assumption. To address these issues, we introduce a compressed sensing (CS)-based CE protocol to tackle the underdetermined problem. In contrast to the traditional CS-based estimation framework, we investigate a polar-domain channel representation to tackle the severe energy spread effect of the classical angular-domain channel representation in near-field communication systems. Specifically, we design a novel polar-domain transform matrix for uniform planar arrays (UPAs), thereby transforming the CE problem into a sparse recovery task of the paths' support set and complex gains. To overcome the limitations of the sparse Bayesian learning (SBL) framework in tackling high-dimensional dictionaries, we propose a low-complexity polar-domain SBL (LCPD-SBL) algorithm, which significantly reduces computational complexity without compromising estimation accuracy.

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arXiv2026

Weighted Sum-Rate Enhancement for Flexible Intelligent Metasurface-Assisted Multicell Systems

Flexible intelligent metasurface (FIM) technology has emerged as a promising technology for enhancing wireless communication performance by dynamically reshaping the propagation environment. Compared with conventional rigid reconfigurable intelligent surfaces (RIS), an FIM is composed of multiple electromagnetic (EM) scattering units, each of which can flexibly modify its displacement in the direction normal to the surface, thereby cooperatively morphing the overall surface shape. This additional degree of freedom (DoF) enables improved beamforming and interference mitigation, particularly in complex multicell scenarios. In this paper, an optimization problem for maximizing the weighted sum-rate (WSR) in a multicell multi-user multiple-input single-output (MU-MISO) system assisted by an FIM deployed at the cell boundary is investigated. We jointly optimize the transmit beamforming at the base station (BS), the phase shift matrix, and the FIM surface shape, subject to constraints on the transmit power budget, unit-modulus reflection coefficients, and surface shape morphing range. Due to the non-convex objective function with highly coupled variables, solving the formulated optimization problem is challenging. To tackle this challenge, we propose an efficient alternating optimization framework that leverages the weighted minimum mean square error (WMMSE) method to reformulate the problem and the block coordinate descent (BCD) algorithm to iteratively update the variables. Specifically, the Riemannian conjugate gradient (RCG) algorithm is leveraged to optimize the phase shift matrix, while the projected gradient descent (PGD) method is adopted to optimize the surface shape of the FIM. Additionally, the optimal beamforming vectors are obtained in closed form.

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arXiv2026

Distributed Electromagnetic Neural Networks for Task-Oriented Semantic Communications

Semantic communications (SemCom) is a promising paradigm that prioritizes the transmission of task-relevant information, thereby enabling superior communication efficiency over traditional bit-centric systems. However, most existing SemCom systems face critical limitations in computational efficiency and spatial flexibility. To overcome these limitations, we propose a novel unmanned aerial vehicles (UAV)-enabled distributed electromagnetic neural network (EMNN) for a task-oriented SemCom system. Specifically, the proposed distributed EMNN is composed of multiple UAV-mounted stacked intelligent metasurfaces (SIM) and a ground receiving station (GRS), where multiple SIMs collaboratively encode image semantics in the wave domain, and the GRS performs decoding based on the received power distribution. Moreover, we employ a temperature-adaptive gradient optimization algorithm to train the distributed EMNN, which mitigates gradient vanishing and enhances learning stability. Finally, the numerical simulation results demonstrate the effectiveness of distributed EMNN in image recognition task-oriented SemCom, achieving an average $8\%$ accuracy improvement over the single-SIM baseline across multiple datasets.

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arXiv2026

Enhanced Channel Estimation for Flexible Intelligent Metasurface-Aided Communication Systems

Flexible intelligent metasurface (FIM) has recently received considerable interest due to its advantage in realizing a better channel condition by dynamically morphing its surface shape. An FIM consists of multiple elements deposited on a flexible substrate. These elements can not only transmit signals, but also adapt their displacements in a direction perpendicular to the FIM surface via an attached controller. In this paper, we consider the channel estimation problem for the uplink of an FIM-enhanced communication system via customizing the orthogonal matching pursuit (OMP) method. Specifically, we formulate an optimization problem of minimizing the column coherence of the measurement matrix by optimizing the FIM's surface shape, subject to the morphing range constraint. Based on the estimated direction of arrival (DOA) and channel gain, we further investigate the signal-to-noise ratio (SNR) improvement in the FIM-enhanced downlink multiple-input single-output (MISO) system. Numerical results demonstrate that an FIM significantly outperforms a conventional rigid uniform planar array (UPA), thereby showing that FIM can substantially improve channel estimation accuracy and achieve SNR improvement, even when using estimated channel parameters.

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arXiv2026

SIM-assisted Secure Mobile Communications via Enhanced Proximal Policy Optimization Algorithm

With the development of sixth-generation (6G) wireless communication networks, the security challenges are becoming increasingly prominent, especially for mobile users (MUs). As a promising solution, physical layer security (PLS) technology leverages the inherent characteristics of wireless channels to provide security assurance. Particularly, stacked intelligent metasurface (SIM) directly manipulates electromagnetic waves through their multilayer structures, offering significant potential for enhancing PLS performance in an energy efficient manner. Thus, in this work, we investigate an SIM-assisted secure communication system for MUs under the threat of an eavesdropper, addressing practical challenges such as channel uncertainty in mobile environments, multiple MU interference, and residual hardware impairments. Consequently, we formulate a joint power and phase shift optimization problem (JPPSOP), aiming at maximizing the achievable secrecy rate (ASR) of all MUs. Given the non-convexity and dynamic nature of this optimization problem, we propose an enhanced proximal policy optimization algorithm with a bidirectional long short-term memory mechanism, an off-policy data utilization mechanism, and a policy feedback mechanism (PPO-BOP). Through these mechanisms, the proposed algorithm can effectively capture short-term channel fading and long-term MU mobility, improve sample utilization efficiency, and enhance exploration capabilities. Extensive simulation results demonstrate that PPO-BOP significantly outperforms benchmark strategies and other deep reinforcement learning algorithms in terms of ASR.10.1109/TWC.2026.3658332

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arXiv2026

Low-Complexity Multi-Agent Continual Learning for Stacked Intelligent Metasurface-Assisted Secure Communications

Stacked intelligent metasurfaces (SIMs), composed of multiple layers of reconfigurable transmissive metasurfaces, are gaining prominence as a transformative technology for future wireless communication security. This paper investigates the integration of SIM into multi-user multiple-input multiple-output (MIMO) systems to enhance physical layer security. A novel system architecture is proposed, wherein each base station (BS) antenna transmits a dedicated single-user stream, while a multi-layer SIM executes wave-based beamforming in the electromagnetic domain, thereby avoiding the need for complex baseband digital precoding and significantly reducing hardware overhead. To maximize the weighted sum secrecy rate (WSSR), we formulate a joint precoding optimization problem over BS power allocation and SIM phase shifts, which is high-dimensional and non-convex due to the complexity of the objective function and the coupling among optimization variables. To address this, we propose a manifold-enhanced heterogeneous multi-agent continual learning (MHACL) framework that incorporates gradient representation and dual-scale policy optimization to achieve robust performance in dynamic environments with high demands for secure communication. Furthermore, we develop SIM-MHACL (SIMHACL), a low-complexity learning template that embeds phase coordination into a product manifold structure, reducing the exponential search space to linear complexity while maintaining physical feasibility. Simulation results validate that the proposed framework achieves millisecond-level per-iteratio ntraining in SIM-assisted systems, significantly outperforming various baseline schemes, with SIMHACL achieving comparable WSSR to MHACL while reducing computation time by 30\%.

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arXiv2026

Stacked Intelligent Metasurfaces-Based Electromagnetic Wave Domain Interference-Free Precoding

This paper introduces an interference-free multi-stream transmission architecture leveraging stacked intelligent metasurfaces (SIMs), from a new perspective of interference exploitation. Unlike traditional interference exploitation precoding (IEP) which relies on computational hardware circuitry, we perform the precoding operations within the analog wave domain provided by SIMs. However, the benefits of SIM-enabled IEP are limited by the nonlinear distortion (NLD) caused by power amplifiers. A hardware-efficient interference-free transmitter architecture is developed to exploit SIM's high and flexible degree of freedom (DoF), where the NLD on modulated symbols can be directly compensated in the wave domain. Moreover, we design a frame-level SIM configuration scheme and formulate a maxmin problem on the safety margin function. With respect to the optimization of SIM phase shifts, we propose a recursive oblique manifold (ROM) algorithm to tackle the complex coupling among phase shifts across multiple layers. A flexible DoF-driven antenna selection (AS) scheme is explored in the SIM-enabled IEP system. Using an ROM-based alternating optimization (ROM-AO) framework, our approach jointly optimizes transmit AS, SIM phase shift design, and power allocation (PA), and develops a greedy safety margin-based AS algorithm. Simulations show that the proposed SIM-enabled frame-level IEP scheme significantly outperforms benchmarks. Specifically, the strategy with AS and PA can achieve a 20 dB performance gain compared to the case without any strategy under the 12 dB signal-to-noise ratio, which confirms the superiority of the NLD-aware IEP scheme and the effectiveness of the proposed algorithm.

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arXiv2026

Cram\'er-Rao Bound Minimization for Flexible Intelligent Metasurface-Enabled ISAC Systems

Integrated sensing and communication (ISAC) have been widely recognized as a key enabler for future wireless networks, where the Cram\'er-Rao bound (CRB) plays a central role in quantifying sensing accuracy.In this paper, we present the first study on CRB minimization in flexible intelligent metasurface (FIM)-enabled ISAC systems.Specifically, we first derive an average CRB expression that explicitly depends on FIM surface shape and demonstrate that array reconfigurability can substantially reduce the CRB, thereby significantly enhancing sensing performance.Moreover, to tackle the challenging CRB minimization problem, we adopt average Fisher information maximization as a surrogate objective and use the Gauss-Hermite quadrature method to obtain an explicit approximation of the objective function.The resulting problem is then decoupled into three subproblem, i.e., beamforming optimization and transmit/receive FIM surface shape optimization.For beamforming optimization, we employ the Schur complement and penalty-based semi-definite relaxation (SDR) technique to solve it.Furthermore, we propose a fixed-point equation method and a projected gradient algorithm to optimize the surface shapes of the receive and transmit FIMs, respectively.Simulation results demonstrate that, compared to rigid arrays, surface shaping of both transmit and receive FIMs can significantly reduce the average sensing CRB while maintaining communication quality, and remains effective even in multi-target scenarios.

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arXiv2026

Stacked Intelligent Metasurface-Aided Wave-Domain Signal Processing: From Communications to Sensing and Computing

Artificial neural networks possess remarkable capabilities for abstract feature extraction, while electromagnetic computing leverages wave propagation to execute complex mathematical operations. Concurrently, metasurfaces engineered from subwavelength meta-atoms offer unprecedented control over electromagnetic wavefronts. Synthesizing these three cutting-edge fields has sparked significant interest in developing electromagnetic neural networks via stacked intelligent metasurface (SIM) technology, which aims to execute diverse signal processing tasks directly within the wave domain. By enabling direct processing of information-carrying electromagnetic waves, SIMs offer a promising paradigm for high-speed, massively parallel, and low-power signal processing. This article provides a comprehensive overview of SIM technology, beginning with its evolutionary trajectory. We then delve into its theoretical foundations and examine state-of-the-art SIM hardware prototypes. Furthermore, we analyze the optimization and training strategies devised to configure SIM functionalities from two distinct perspectives. Additionally, the diverse applications of SIM technology across the communication, sensing, and computing domains are explored, supported by experimental evidence that highlights its ability to sustain multiple functions within a single device. Finally, we outline critical technical challenges to deploying SIMs in next-generation wireless networks and chart promising research directions to fully unlock their transformative potential.

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arXiv2025

Flexible Intelligent Layered Metasurfaces for Downlink Multi-user MISO Communications

Stacked intelligent metasurfaces (SIMs) have recently gained attention as a paradigm for wave-domain signal processing with reduced reliance on costly radio-frequency (RF) chains. However, conventional SIMs rely on uniform inter-layer spacing and require deep stacking to ensure processing capability, resulting in severe power attenuation in practice. To address this issue, we propose a flexible intelligent layered metasurface (FILM) architecture consisting of two shape-controllable flexible metasurface layers. By replacing rigid metasurfaces with flexible ones in both layers, the transmission coefficient matrix can be dynamically adjusted, significantly decreasing the number of required layers while maintaining signal processing performance. Firstly, we develop a two-layer FILM-assisted multi-user multiple-input single-output (MU-MISO) system, wherein we formulate a channel fitting problem aimed at reducing the difference between the FILM-induced and target channels. Then, we solve this non-convex problem by employing an alternating optimization (AO) method, featuring closed-form phase shift updates and a gradient descent-based shape optimization. Furthermore, we analyze the upper bound on sum-rate and the complexity of computation to provide insights into design trade-offs. Finally, simulation results demonstrated that the proposed transmissive FILM architecture achieves over 200\% improvement in sum-rate and more than 7 dB bit-error rate (BER) gain compared to the conventional seven-layer SIMs.

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arXiv2025

Flexible Intelligent Metasurface for Reconfiguring Radio Environments

Flexible intelligent metasurface (FIM) technology holds immense potential for increasing the spectral efficiency and energy efficiency of wireless networks. In contrast to traditional rigid reconfigurable intelligent surfaces (RIS), an FIM consists of an array of elements, each capable of independently tuning electromagnetic signals, while flexibly adjusting its position along the direction perpendicular to the surface. In contrast to traditional rigid metasurfaces, FIM is capable of morphing its surface shape to attain better channel conditions. In this paper, we investigate the single-input single-output (SISO) and multiple-input single-output (MISO) communication systems aided by a transmissive FIM. In the SISO scenario, we jointly optimize the FIM phase shift matrix and surface shape to maximize the end-to-end channel gain. First, we derive the optimal phase-shift matrix for each tentative FIM surface shape to decompose the high-dimensional non-convex optimization problem into multiple one-dimensional subproblems. Then, we utilize the particle swarm optimization (PSO) algorithm and the multi-interval gradient descent (MIGD) method for updating the FIM's surface shape to maximize the channel gain. In the MISO scenario, we jointly optimize the transmit beamforming, the FIM surface shape, and the phase shift matrix to maximize the channel gain. To tackle this complex problem with multiple highly coupled variables, an efficient alternating optimization algorithm is proposed. Simulation results demonstrate that FIM significantly improves channel gain compared to traditional RIS and exhibits good adaptability to multipath channels.

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