arXiv subjects

Yijian Chen

Publications and source records attributed to Yijian Chen.

17 recordsLinked to original sources

arXiv2025

Revisiting XL-MIMO Channel Estimation: When Dual-Wideband Effects Meet Near Field

The deployment of extremely large antenna arrays (ELAAs) in extremely large-scale multiple-input multiple-output (XL-MIMO) systems introduces significant near-field effects, such as spherical wavefront propagation and spatially non-stationary (SnS) properties. When combined with the dual-wideband effects inherent to wideband systems, these phenomena fundamentally alter the channel's sparsity patterns in the angular-delay domain, rendering existing estimation methods insufficient. To address these challenges, this paper reconsiders the channel estimation problem for wideband XL-MIMO systems. Leveraging the spatial-chirp property of array responses, we first quantitatively characterize the angular-delay domain sparsity of wideband XL-MIMO channels, revealing both global block sparsity and local common-delay sparsity. To effectively capture this structured sparsity, we then propose a novel column-wise hierarchical prior model that integrates a precision sharing mechanism and a Markov random field (MRF) structure. Building on this prior model, the channel estimation task is formulated as a multiple measurement vector (MMV)-based Bayesian inference problem. Tailored to the complex factor graph induced by this hierarchical prior, we develop a MMV-based hybrid message passing (MMV-HMP) algorithm. This algorithm performs message updates along the edges of the factor graph, and selectively applies either the variational message passing (VMP) or sum-product (SP) rules, depending on the factor-node structure and message tractability. Simulation results validate the effectiveness of the proposed column-wise hierarchical prior model through ablation studies and demonstrate that the MMV-HMP algorithm, while maintaining moderate computational complexity, consistently outperforms existing baselines which fail to capture the structured sparsity of wideband XL-MIMO channels.

cs.IT↗

arXiv2025

Revealing the evanescent components in Kronecker-product based codebooks: insights and implications

The orthogonal bases of discrete Fourier transform (DFT) has been recognized as the standard spatial-domain bases for Type I, Type II and enhanced Type II codewords by the 3rd Generation Partnership Project (3GPP). For uniform planar arrays, these spatial-domain bases are derived as the Kronecker product of one-dimensional DFT bases. Theoretically, each spatial basis corresponds to a beam directed towards a specific angle of departure and the set of bases represent the orthogonal beams that cover the front hemisphere of an array. While the Kronecker-product based precoding scheme facilitates the concise indexing of a codeword in the codebooks through precoding matrix indicators (PMIs) in channel state information feedback, it introduces redundant spatial beams characterized by high spatial-frequency components. This paper investigates the presence of codewords representing high spatial-frequency components within the Kronecker-product based codebooks. Through theoretical analysis and simulations, we confirm the redundancy of these codewords in MIMO communications, advocating for their removal from the codebooks to enhance system performance. Several topics relevant to the high spatial components are also involved in the discussion. Practical suggestions regarding future standard design are provided based on our theoretical analysis and simulation results.

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arXiv2025

Channel Estimation for XL-MIMO Systems with Decentralized Baseband Processing: Integrating Local Reconstruction with Global Refinement

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results validate the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity.

cs.IT↗

arXiv2025

Joint Size and Placement Optimization for IRS-Aided Communications with Active and Passive Elements

Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS element allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)-HIRS-user (BHU); 2) BS-AIRS-PIRS-user (BAPU); 3) BS-PIRS-AIRS-user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS element allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups.

eess.SP↗

arXiv2025

Static IRS Meets Distributed MIMO: A New Architecture for Dynamic Beamforming

Intelligent reflecting surface (IRS) has been considered as a revolutionary technology to enhance the wireless communication performance. To cater for multiple mobile users, adjusting IRS beamforming patterns over time, i.e., dynamic IRS beamforming (DIBF), is generally needed for achieving satisfactory performance, which results in high controlling power consumption and overhead. To avoid such cost, we propose a new architecture based on the static regulated IRS for wireless coverage enhancement, where the principle of distributed multiple-input multiple-output (D-MIMO) is integrated into the system to exploite the diversity of spatial directions provided by multiple access points (APs). For this new D-MIMO empowered static IRS architecture, the total target area is partitioned into several subareas and each subarea is served by an assigned AP. We consider to maximize the worst-case received power over all locations in the target area by jointly optimizing a single set of IRS beamforming pattern and AP-subarea association. Then, a two-step algorithm is proposed to obtain its high-quality solution. Theoretical analysis unveils that the fundamental squared power gain can still be achieved over all locations in the target area. The performance gap relative to the DIBF scheme is also analytically quantified. Numerical results validate our theoretical findings and demonstrate the effectiveness of our proposed design over benchmark schemes.

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arXiv2024

Spatial Bandwidth of Bilateral Near-Field Channels for Linear Large-Scale Antenna Array System

This paper analyzes the spatial bandwidth of line-of-sight (LoS) channels in massive MIMO systems. For the linear large-scale antenna arrays (LSAA) of transceivers placed in random locations in 3D space, a simple but accurate closed-form expression is derived to characterize the spatial bandwidth. Subsequent analysis of the LSAA's spatial bandwidth properties is also provided, leading to the formulation of an approximate expression for the effective degrees of freedom (EDoF) of bilateral near-field channels. Interestingly, as proved in this work, when the transmit and receive arrays are coplanar, with the receive array positioned perpendicular to the axis joining the centroids of the transmit and receive arrays, the EDoF of the LoS channel is found to be approximately maximized.

cs.IT↗

arXiv2024

Spatially Non-Stationary XL-MIMO Channel Estimation: A Three-Layer Generalized Approximate Message Passing Method

In this paper, channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of the spherical wavefront effect and the spatially non-stationary (SnS) property. Due to the diversities of SnS characteristics among different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase channel estimation scheme. In the first phase, the angles of departure (AoDs) on the user side are estimated, and a carefully designed pilot transmission scheme enables the decomposition of the received signal from different paths. In the second phase, the subchannel estimation corresponding to different paths is formulated as a three-layer Bayesian inference problem. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS property in the antenna domain, and the third layer decouples the subchannels from the observed signals. To efficiently facilitate Bayesian inference, we propose a novel three-layer generalized approximate message passing (TL-GAMP) algorithm based on structured variational massage passing and belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed algorithm, showcasing its robustness to different channel scenarios.

cs.IT↗

arXiv2024

Patterned Beam Training: A Novel Low-Complexity and Low-Overhead Scheme for ELAA

Extremely large antenna arrays (ELAAs) can provide higher spectral efficiency. However, the use of narrower beams for data transmission significantly increases the overhead associated with beam training. In this letter, we propose a novel patterned beam training (PBT) scheme characterized by its low overhead and complexity. This scheme requires only a single linear operation by both the base station and the user equipment to determine the optimal beam, reducing the training overhead to half or even less compared to traditional exhaustive search methods. Furthermore, We discuss the pattern design principles in detail and provide specific forms. Simulation results demonstrate that the proposed scheme outperforms the compared methods in terms of beam alignment accuracy and achieves a balance between signal-to-noise ratio (SNR) conditions and training overhead, making it a promising alternative.

eess.SP↗

arXiv2024

Fast Beam Training and Performance Analysis for Extremely Large Aperture Array

Extremely large aperture array (ELAA) can significantly enhance beamforming gain and spectral efficiency. Unfortunately, the use of narrower beams for data transmission results in a substantial increase in the cost of beam training. In this paper, we study a high-efficiency and low-overhead scheme named hash beam training. Specifically, two improved hash codebook design methods, random and fixed, are proposed. Moreover, we analyze beam alignment performance. Since the derived beam alignment success probability is a complex function, we also propose a heuristic metric to evaluate the impact of codebook parameter on performance. Finally, simulation results validate the theoretical analysis, indicating that the proposed beam training scheme can achieve fast beam alignment with lower overhead and higher accuracy.

eess.SP↗

arXiv2024

Joint Visibility Region and Channel Estimation for Extremely Large-scale MIMO Systems

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios.

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arXiv2023

Low-Complexity Channel Estimation for Extremely Large-Scale MIMO in Near Field

The extremely large-scale massive multiple-input multiple-output (XL-MIMO) has the potential to achieve boosted spectral efficiency and refined spatial resolution for future wireless networks. However, channel estimation for XL-MIMO is challenging since the large number of antennas results in high computational complexity with the near-field effect. In this letter, we propose a low-complexity sequential angle-distance channel estimation (SADCE) method for near-field XL-MIMO systems equipped with uniformly planar arrays (UPA). Specifically, we first successfully decouple the angle and distance parameters, which allows us to devise a two-dimensional discrete Fourier transform (2D-DFT) method for angle parameters estimation. Then, a low-complexity distance estimation method is proposed with a closed-form solution. Compared with existing methods, the proposed method achieves significant performance gain with noticeably reduced computational complexity.Numerical results verify the superiority of the proposed near-field channel estimation algorithm.

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arXiv2023

Extremely Large-scale Array Systems: Near-Field Codebook Design and Performance Analysis

Extremely Large-scale Array (ELAA) promises to deliver ultra-high data rates with increased antenna elements. However, increasing antenna elements leads to a wider realm of near-field, which challenges the traditional design of codebooks. In this paper, we propose novel near-field codebook schemes based on the fitting formula of codewords' quantization performance. First, we analyze the quantization performance properties of uniform linear array (ULA) and uniform planar array (UPA) codewords. Our findings reveal an intriguing property: the correlation formula for ULA codewords can be represented by the elliptic formula, while the correlation formula for UPA codewords can be approximated using the ellipsoid formula. Building on this insight, we propose a ULA uniform codebook that maximizes the minimum correlation based on the derived formula. Moreover, we introduce a ULA dislocation codebook to further reduce quantization overhead. Continuing our exploration, we propose UPA uniform and dislocation codebook schemes. Our investigation demonstrates that oversampling in the angular domain offers distinct advantages, achieving heightened accuracy while minimizing overhead in quantifying near-field channels. Numerical results demonstrate the appealing advantages of the proposed codebook over existing methods in decreasing quantization overhead and increasing quantization accuracy.

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arXiv2023

How Practical Phase-shift Errors Affect Beamforming of Reconfigurable Intelligent Surface?

Reconfigurable intelligent surface (RIS) is a new technique that is able to manipulate the wireless environment smartly and has been exploited for assisting the wireless communications, especially at high frequency band. However, it suffers from hardware impairments (HWIs) in practical designs, which inevitably degrades its performance and thus limits its full potential. To address this practical issue, we first propose a new RIS reflection model involving phase-shift errors, which is then verified by the measurement results from field trials. With this beamforming model, various phase-shift errors caused by different HWIs can be analyzed. The phase-shift errors are classified into three categories: (1) globally independent and identically distributed errors, (2) grouped independent and identically distributed errors and (3) grouped fixed errors. The impact of typical HWIs, including frequency mismatch, PIN diode failures and panel deformation, on RIS beamforming ability are studied with the theoretical model and are compared with numerical results. The impact of frequency mismatch are discussed separately for narrow-band and wide-band beamforming. Finally, useful insights and guidelines on the RIS design and its deployment are highlighted for practical wireless systems.

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arXiv2022

Distributed and Joint Optimization of Precoding and Power for User-Centric Cell-Free Massive MIMO

In the cell-free massive multiple-input multiple-output (CF mMIMO) system, the centralized transmission scheme is widely adopted to manage the inter-user interference. Unfortunately, its implementation is limited by the extensive signaling overhead between the central process unit (CPU) and the access points (APs). To solve this problem, we propose a distributed downlink transmission scheme in this letter. First, the null space-based precoding is used to cancel the interference to partial users, where only a portion of channel state information (CSI) needs to be shared among the AP cluster. Based on this, the dual decomposition method is adopted to jointly optimize the precoder and power control, where the calculation can be performed independently by each AP cluster with closed-form expression. With very few iterations, our distributed scheme achieves the same performance as the centralized one. Moreover, it significantly reduces the information exchange to the CPU.

eess.SP↗

arXiv2022

Achieving Efficient Distributed Machine Learning Using a Novel Non-Linear Class of Aggregation Functions

Distributed machine learning (DML) over time-varying networks can be an enabler for emerging decentralized ML applications such as autonomous driving and drone fleeting. However, the commonly used weighted arithmetic mean model aggregation function in existing DML systems can result in high model loss, low model accuracy, and slow convergence speed over time-varying networks. To address this issue, in this paper, we propose a novel non-linear class of model aggregation functions to achieve efficient DML over time-varying networks. Instead of taking a linear aggregation of neighboring models as most existing studies do, our mechanism uses a nonlinear aggregation, a weighted power-p mean (WPM), as the aggregation function of local models from neighbors. The subsequent optimizing steps are taken using mirror descent defined by a Bregman divergence that maintains convergence to optimality. In this paper, we analyze properties of the WPM and rigorously prove convergence properties of our aggregation mechanism. Additionally, through extensive experiments, we show that when p > 1, our design significantly improves the convergence speed of the model and the scalability of DML under time-varying networks compared with arithmetic mean aggregation functions, with little additional computation overhead.

cs.LG↗

arXiv2020

Efficient Parallel Schemes for Cell-free Massive MIMO Using Radio Stripes

Cell-free massive MIMO provides ubiquitous connectivity for multiple users, and implementation using radio stripes is very efficient. This paper proposes a parallel scheme for radio stripes to allow access point to do signal processing simultaneously. Simple maximum ratio (MR) processing works in this scheme, but its performance is bad. Therefore, this paper further proposes two efficient parallel schemes to gain better performance. The first is interference-aware MR processing with a tailored user-centric strategy. The second is distributed regularized zero-forcing (D-RZF) algorithm which derives from LMMSE. Simulation results show that the proposed parallel schemes gain better performance than existing works.

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arXiv2020

Physical Layer Security Enhancement Exploiting Intelligent Reflecting Surface

In this letter, the use of intelligent reflecting surface (IRS) to enhance the physical layer security of downlink wireless communication is investigated. Assuming a single-antenna legitimate user and a multi-antenna eavesdropper, we propose an effective algorithm to jointly optimize the active and passive beamforming. In the proposed algorithm, the optimal transmit beamforming vector at the BS under fixed IRS phase shifts is derived, and a low-complexity algorithm based on fractional programming (FP) and manifold optimization (MO) is proposed to obtain near optimal IRS phase shifts. Simulation results demonstrate that the proposed algorithm can almost achieve the performance upper bound with a fast convergence rate.

eess.SP↗