arXiv subjects

Kangda Zhi

Publications and source records attributed to Kangda Zhi.

At least 19 recordsLinked to original sources

arXiv2026

Electromagnetic Twin: Completing the Wireless World from Sparse Channel Evidence

Acquiring dense channel information over many locations and beams incurs considerable pilot and processing overhead. Radio maps and channel knowledge maps (CKMs) reduce this overhead by reusing site-specific channel information, but their contents must be refreshed when new measurements or environmental observations become available. This paper introduces an \emph{electromagnetic twin} as an updatable digital representation that uses sparse channel evidence to reconstruct the wireless state requested by communication queries. Rather than replacing radio maps or CKMs, the twin uses a CKM as channel memory, combines it with registered scene information, and regenerates its outputs after each evidence update. We instantiate this idea by completing a two-dimensional channel-gain field from sparse samples and an incomplete floor plan. A learned RF completion backbone recovers the main propagation structure, and a lightweight residual adapter tests whether frozen CLIP features provide useful side information. With $4\%$ measured locations and $55\%$ missing semantic objects, the RF backbone attains $4.44$ dB RMSE, compared with $8.29$ dB for CKM interpolation and $8.38$ dB for an incomplete physics prior. Residual adaptation reduces RMSE by a paired mean of $0.135$ dB (95\% confidence interval: $0.100$--$0.169$ dB), but a same-capacity random-feature control is statistically indistinguishable from the CLIP-conditioned adapter. The results therefore support the measurement--update--query loop and lightweight residual correction, while avoiding an unsupported attribution of the correction to visual semantics.

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arXiv2026

From Sparse Probes to Sum-Rate Maximization: Electromagnetic Twin Beamforming

Probing every candidate location at each wireless-map update is costly. This letter develops electromagnetic-twin (ET) beamforming that converts sparse spatial probes into a sum-rate decision. The ET stores a location-dependent angular power spectrum, updates its innovation through graph-regularized estimation, and queries user covariances for projected beam optimization. A rate-sensitivity bound selects subsequent probes by query-weighted posterior-variance reduction. With 1\% random probes, ET beamforming achieves $2.69$ bit/s/Hz versus $1.12$ for a static channel knowledge map; with 7\% query-aware probes, it reaches $3.03$ bit/s/Hz, within 3.4\% of perfect-covariance beamforming.

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arXiv2026

Near-Field Communications with Different Array Geometries: Rayleigh Distance, Channel Estimation, and Transmission Design

This work establishes a framework of near-field communication under different array geometries of extremely large-scale multiple-input multiple-output (XL-MIMO). We first formulate the near-field spatial non-stationary channel model which is characterized by the distance between the user and each antenna on uniform and modular curved arrays. By fixing the total number of antennas while varying the degree of curvature, we investigate a fair case where the horizontal arc length of the curved array is the same as the planar array. We explicitly unveil the non-trivial impact of array curvature on extending the near-field region for cell edges. Then, for arbitrary array geometries and arbitrary-field channels, we estimate the spatial-domain channel by tackling a compressed sensing problem with a learned regularizer. Without relying on specific codebooks, we propose a denoising autoencoder (AE)-aided approximated message passing (AMP) algorithm and provide the corresponding theoretical replica bound. Finally, based on the estimated channel, we propose an optimization algorithm to maximize the sum user rate for sub-connected XL-MIMO systems by jointly designing the array geometry and hybrid precoding in the downlink. Numerical results demonstrate that the proposed AE-AMP algorithm can effectively estimate the spatial non-stationary near-field channels with robustness and generalities compared to several conventional and deep-learning-based benchmarks. The improvement of data rate by using modular curved arrays with the estimated channel is also validated.

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arXiv2025

Near-Field 3D Localization and MIMO Channel Estimation with Sub-Connected Planar Arrays

This paper investigates the design of channel estimation and 3D localization algorithms in a challenging scenario, where a sub-connected planar extremely large-scale multiple-input multiple-output (XL-MIMO) communicates with multi-antenna users. In the near field, the uplink MIMO channel is of full column rank and therefore can not be estimated effectively by applying existing codebooks that are designed for the far-field case or for the near-field case but limited to single antenna users. To solve this problem, we propose a three-stage algorithm aided by orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL). Specifically, we firstly partition the XL-MIMO into subarrays and use OMP to solve the compressed sensing (CS) problem about subarray channel estimation with the Discrete Fourier Transform (DFT)-based dictionary matrix. Secondly, exploiting the estimated subarray channels and employing one-dimensional multiple signal classification (MUSIC), we estimate the central location of the user array under the Least Squares (LS) criterion. Finally, we utilize the estimated central location to construct a refined location-aided dictionary matrix and obtain the MIMO channel estimation using SBL. Results exhibit the significant superiority of the proposed algorithm compared with several benchmarks, in terms of both the pilot overhead and estimation accuracy.

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arXiv2025

Near-Field Imaging by Exploiting Frequency Correlation in Wireless Communication Networks

In this work, we address the near-field imaging under a wideband wireless communication network by exploiting both the near-field channel of a uniform linear array (ULA) and the image correlation in the frequency domain. We first formulate the image recovery as a special multiple measurement vector (MMV) compressed sensing (CS) problem, where at various frequencies the sensing matrices can be different, and the image coefficients are correlated. To solve such an MMV problem with various sensing matrices and correlated coefficients, we propose a sparse Bayesian learning (SBL)-based solution to simultaneously estimate all image coefficients and their correlation on multiple frequencies. Moreover, to enhance estimation performance, we design two illumination patterns following two different criteria. From the CS perspective, the first design minimizes the total coherence of the sensing matrix to increase the mutual orthogonality of the basis vectors. Alternatively, to improve SNR, the second design maximizes the illumination power of the imaging area. Numerical results demonstrate the effectiveness of the proposed SBL-based method and the superiority of the illumination designs.

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arXiv2025

Near-Field Integrated Imaging and Communication in Distributed MIMO Networks

In this work, we propose a general framework for wireless imaging in distributed MIMO wideband communication systems, considering multi-view non-isotropic targets and near-field propagation effects. For indoor scenarios where the objective is to image small-scale objects with high resolution, we propose a range migration algorithm (RMA)-based scheme using three kinds of array architectures: the full array, boundary array, and distributed boundary array. With non-isotropic near-field channels, we establish the Fourier transformation (FT)-based relationship between the imaging reflectivity and the distributed spatial-domain signals and discuss the corresponding theoretical properties. Next, for outdoor scenarios where the objective is to reconstruct the large-scale three-dimensional (3D) environment with coarse resolution, we propose a sparse Bayesian learning (SBL)-based algorithm to solve the multiple measurement vector (MMV) problem, which further addresses the non-isotropic reflectivity across different subcarriers. Numerical results demonstrate the effectiveness of the proposed algorithms in acquiring high-resolution small objects and accurately reconstructing large-scale environments.

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arXiv2025

Fluid Antenna Enabled Direction-of-Arrival Estimation Under Time-Constrained Mobility

Fluid antenna (FA) technology has emerged as a promising approach in wireless communications due to its capability of providing increased degrees of freedom (DoFs) and exceptional design flexibility. This paper addresses the challenge of direction-of-arrival (DOA) estimation for aligned received signals (ARS) and non-aligned received signals (NARS) by designing two specialized uniform FA structures under time-constrained mobility. For ARS scenarios, we propose a fully movable antenna configuration that maximizes the virtual array aperture, whereas for NARS scenarios, we design a structure incorporating a fixed reference antenna to reliably extract phase information from the signal covariance. To overcome the limitations of large virtual arrays and limited sample data inherent in time-varying channels (TVC), we introduce two novel DOA estimation methods: TMRLS-MUSIC for ARS, combining Toeplitz matrix reconstruction (TMR) with linear shrinkage (LS) estimation, and TMR-MUSIC for NARS, utilizing sub-covariance matrices to construct virtual array responses. Both methods employ Nystrom approximation to significantly reduce computational complexity while maintaining estimation accuracy. Theoretical analyses and extensive simulation results demonstrate that the proposed methods achieve underdetermined DOA estimation using minimal FA elements, outperform conventional methods in estimation accuracy, and substantially reduce computational complexity.

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arXiv2025

Scalable Fluid Antenna Systems: A New Paradigm for Array Signal Processing

Most existing antenna array-based source localization methods rely on fixed-position arrays (FPAs) and strict assumptions about source field conditions (near-field or far-field), which limits their effectiveness in complex, dynamic real-world scenarios where high-precision localization is required. In contrast, this paper introduces a novel scalable fluid antenna system (SFAS) that can dynamically adjust its aperture configuration to optimize performance for different localization tasks. Within this framework, we develop a two-stage source localization strategy based on the exact spatial geometry (ESG) model: the first stage uses a compact aperture configuration for initial direction-of-arrival (DOA) estimation, while the second stage employs an expanded aperture for enhanced DOA and range estimation. The proposed approach eliminates the traditional need for signal separation or isolation to classify source types and enables a single SFAS array to achieve high localization accuracy without field-specific assumptions, model simplifications, or approximations, representing a new paradigm in array-based source localization. Extensive simulations demonstrate the superiority of the proposed method in terms of localization accuracy, computational efficiency, and robustness to different source types.

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arXiv2025

Sum Capacity Characterization of Pinching Antennas-assisted Multiple Access Channels

Pinching antenna system (PASS) has recently shown its promising ability to flexibly reconfigure wireless channels via dynamically adjusting the positions of pinching antennas over a dielectric waveguide, termed as pinching beamforming. This paper studies the fundamental limit of the sum rate for a PASS-assisted multiple access channel, where multiple users transmit individual messages to a base station under the average power constraint. To this end, a dynamic pinching beamforming setup is conceived, where multiple pinching beamforming vectors are employed in a transmission period and the capacity-achieving non-orthogonal multiple access (NOMA) based scheme is considered. For the ideal case with an asymptotically large number of pinching beamforming vectors, the optimal transmission scheme is unveiled to carry out alternating transmission among each user whose channel power gain is maximized with the tailored pinching beamforming. This implies that NOMA is not needed for achieving the sum capacity and the required optimal number of pinching beamforming vectors is equal to the number of users. With this insight, the corresponding sum rate is derived in closed-form expression, which serves as the upper bound of the sum rate. Inspired by this result, a lower bound of the sum rate under an arbitrarily finite number of pinching beamforming vectors is obtained. Numerical results validate our theoretical findings and also illustrate the practical significance of using dynamic pinching beamforming to improve the sum rate.

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arXiv2025

Holographic MIMO Multi-Cell Communications

Metamaterial antennas are appealing for next-generation wireless networks due to their simplified hardware and much-reduced size, power, and cost. This paper investigates the holographic multiple-input multiple-output (HMIMO)-aided multi-cell systems with practical per-radio frequency (RF) chain power constraints. With multiple antennas at both base stations (BSs) and users, we design the baseband digital precoder and the tuning response of HMIMO metamaterial elements to maximize the weighted sum user rate. Specifically, under the framework of block coordinate descent (BCD) and weighted minimum mean square error (WMMSE) techniques, we derive the low-complexity closed-form solution for baseband precoder without requiring bisection search and matrix inversion. Then, for the design of HMIMO metamaterial elements under binary tuning constraints, we first propose a low-complexity suboptimal algorithm with closed-form solutions by exploiting the hidden convexity (HC) in the quadratic problem and then further propose an accelerated sphere decoding (SD)-based algorithm which yields global optimal solution in the iteration. For HMIMO metamaterial element design under the Lorentzian-constrained phase model, we propose a maximization-minorization (MM) algorithm with closed-form solutions at each iteration step. Furthermore, in a simplified multiple-input single-output (MISO) scenario, we derive the scaling law of downlink single-to-noise (SNR) for HMIMO with binary and Lorentzian tuning constraints and theoretically compare it with conventional fully digital/hybrid arrays. Simulation results demonstrate the effectiveness of our algorithms compared to benchmarks and the benefits of HMIMO compared to conventional arrays.

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arXiv2025

Low-Complexity Iterative Precoding Design for Near-field Multiuser Systems With Spatial Non-Stationarity

Extremely large antenna arrays (ELAA) are regarded as a promising technology for supporting sixth-generation (6G) networks. However, the large number of antennas significantly increases the computational complexity in precoding design, even for linearly regularized zero-forcing (RZF) precoding. To address this issue, a series of low-complexity iterative precoding are investigated. The main idea of these methods is to avoid matrix inversion of RZF precoding. Specifically, RZF precoding is equivalent to a system of linear equations that can be solved by fast iterative algorithms, such as random Kaczmarz (RK) algorithm. Yet, the performance of RK-based precoding algorithm is limited by the energy distributions of multiple users, which restricts its application in ELAA-assisted systems. To accelerate the RK-based precoding, we introduce the greedy random Kaczmarz (GRK)-based precoding by using the greedy criterion-based selection strategy. To further reduce the complexity of the GRK-based precoding, we propose a visibility region (VR)-based orthogonal GRK (VR-OGRK) precoding that leverages near-field spatial non-stationarity, which is characterized by the concept of VR. Next, by utilizing the information from multiple hyperplanes in each iteration, we extend the GRK-based precoding to the aggregation hyperplane Kaczmarz (AHK)-based pecoding algorithm, which further enhances the convergence rate. Building upon the AHK algorithm, we propose a VR-based orthogonal AHK (VR-OAHK) precoding to further reduce the computational complexity. Furthermore, the proposed iterative precoding algorithms are proven to converge to RZF globally at an exponential rate. Simulation results show that the proposed algorithms achieve faster convergence and lower computational complexity than benchmark algorithms, and yield very similar performance to the RZF precoding.

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arXiv2024

Fluid Antenna Systems Enabling 6G:Principles, Applications, and Research Directions

Fluid antenna system (FAS) as a new version of reconfigurable antenna technologies promoting shape and position flexibility, has emerged as an exciting and possibly transformative technology for wireless communications systems. FAS represents any software-controlled fluidic, conductive or dielectric structure that can dynamically alter antenna's shape and position to change the gain, the radiation pattern, the operating frequency, and other critical radiation characteristics. With its capability, it is highly anticipated that FAS can contribute greatly to the upcoming sixth generation (6G) wireless networks. This article substantiates this thought by addressing four major questions: 1) Is FAS crucial to 6G? 2) How to characterize FAS? 3) What are the applications of FAS? 4) What are the relevant challenges and future research directions? In particular, five promising research directions that underscore the potential of FAS are discussed. We conclude this article by showcasing the impressive performance of FAS.

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arXiv2024

Cooperative Multistatic Target Detection in Cell-Free Communication Networks

In this work, we consider the target detection problem in a multistatic integrated sensing and communication (ISAC) scenario characterized by the cell-free MIMO communication network deployment, where multiple radio units (RUs) in the network cooperate with each other for the sensing task. By exploiting the angle resolution from multiple arrays deployed in the network and the delay resolution from the communication signals, i.e., orthogonal frequency division multiplexing (OFDM) signals, we formulate a cooperative sensing problem with coherent data fusion of multiple RUs' observations and propose a sparse Bayesian learning (SBL)-based method, where the global coordinates of target locations are directly detected. Intensive numerical results indicate promising target detection performance of the proposed SBL-based method. Additionally, a theoretical analysis of the considered cooperative multistatic sensing task is provided using the pairwise error probability (PEP) analysis, which can be used to provide design insights, e.g., illumination and beam patterns, for the considered problem.

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arXiv2024

FAS-RIS: A Block-Correlation Model Analysis

In this correspondence, we analyze the performance of a reconfigurable intelligent surface (RIS)-aided communication system that involves a fluid antenna system (FAS)-enabled receiver. By applying the central limit theorem (CLT), we derive approximate expressions for the system outage probability when the RIS has a large number of elements. Also, we adopt the block-correlation channel model to simplify the outage probability expressions, reducing the computational complexity and shedding light on the impact of the number of ports. Numerical results validate the effectiveness of our analysis, especially in scenarios with a large number of RIS elements.

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arXiv2024

Near-Field Mobile Tracking: A Framework of Using XL-RIS Information

This paper introduces a novel mobile tracking framework leveraging the high-dimensional signal received from extremely large-scale (XL) reconfigurable intelligent surfaces (RIS). This received signal, named XL-RIS information, has a much larger data dimension and therefore offers a richer feature set compared to the traditional base station (BS) received signal, i.e., BS information, enabling more accurate tracking of mobile users (MUs). As the first step, we present an XL-RIS information reconstruction (XL-RIS-IR) algorithm to reconstruct the high-dimensional XL-RIS information from the low-dimensional BS information. Building on this, this paper proposes a comprehensive framework for mobile tracking, consisting of a Feature Extraction Module and a Mobile Tracking Module. The Feature Extraction Module incorporates a convolutional neural network (CNN) extractor for spatial features, a time and frequency (T$\&$F) extractor for domain features, and a near-field angles of arrival (AoAs) extractor for capturing AoA features within the XL-RIS. These features are combined into a comprehensive feature vector, forming a time-varying sequence fed into the Mobile Tracking Module, which employs an Auto-encoder (AE) with a stacked bidirectional long short-term memory (Bi-LSTM) encoder and a standard LSTM decoder to predict MUs' positions in the upcoming time slot. Simulation results confirm that the tracking accuracy of our proposed framework is significantly enhanced by using reconstructed XL-RIS information and exhibits substantial robustness to signal-to-noise ratio (SNR) variations.

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arXiv2024

A Framework of FAS-RIS Systems: Performance Analysis and Throughput Optimization

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems.

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arXiv2024

Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In \emph{Stage I}, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in \emph{Stage II}, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).

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arXiv2024

Transmission Design for XL-RIS-Aided Massive MIMO System with Visibility Regions

This paper proposes a two-timescale transmission scheme for extremely large-scale (XL)-reconfigurable intelligent surfaces (RIS)-aided massive multi-input multi-output (MIMO) systems considering visibility regions (VRs). The beamforming of base stations (BS) is designed based on rapidly changing instantaneous channel state information (CSI), while the phase shifts of RIS are configured based on slowly changing statistical CSI. Specifically, we first formulate a system model with spatially correlated Rician fading channels and introduce the concept of VRs. Then, we derive a closed-form approximate expression for the achievable rate applicable to any number of BS antennas and RIS elements, and analyze the impact of VRs on system performance and complexity. Next, we solve the problem of maximizing the minimum user rate by optimizing the phase shifts of RIS through an algorithm based on accelerated gradient ascent. Finally, we present numerical results to demonstrate the performance of the gradient algorithm from different aspects and reveal the low system complexity of deploying XL-RIS in massive MIMO systems with the help of VRs.

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