Signal Processing最新文献_第2页

RIS-assisted differential transmitted spatial modulation design ris辅助差分传输空间调制设计

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-22 DOI: 10.1016/j.sigpro.2024.109767

Chaorong Zhang, Yuyan Liu, Benjamin K. Ng, Chan-Tong Lam

In this paper, we propose a novel reconfigurable intelligent surface (RIS)-assisted wireless communication design called the RIS-assisted differential transmitted spatial modulation (DTSM) scheme. The encoding process of the differential spatial modulation (DSM) is integrated into the DTSM scheme, where only one transmit antenna is activated per time slot to transmit the

M

-ary phase shift keying (PSK) modulation symbol through the RIS. Due to the detection characteristics of DSM, the bit error rate (BER) performance remains satisfactory without requiring channel state information estimation, thereby enhancing robustness. The RIS application in the proposed scheme mitigates the effects of shadow area fading by adjusting the phase of the reflected signals to improve the signal-to-noise ratio at the receiver. In simulation results by comparing to other RIS-assisted spatial modulation schemes, we can find that the proposed DTSM scheme demonstrates good BER performance across various scenarios, including Nakagami-

m

fading, which also indicates its potential for practical applications.

在本文中，我们提出了一种新的可重构智能表面（RIS）辅助无线通信设计，称为RIS辅助差分传输空间调制（DTSM）方案。将差分空间调制（DSM）的编码过程集成到DTSM方案中，每个时隙只激活一个发射天线，通过RIS传输M-ary相移键控（PSK）调制符号。由于DSM的检测特性，在不需要信道状态信息估计的情况下，保持了令人满意的误码率性能，从而增强了鲁棒性。在该方案中，RIS通过调整反射信号的相位来减轻阴影区域衰落的影响，从而提高接收机的信噪比。仿真结果表明，与其它ris辅助空间调制方案相比，本文提出的DTSM方案在包括Nakagami-m衰落在内的各种场景下均具有良好的误码率性能，具有较好的实际应用潜力。

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引用次数: 0

Mitigating impulsive noise in airborne PLC: Introducing the S-SAMP-PV algorithm for MIMO OFDM systems 机载PLC中脉冲噪声的抑制：介绍MIMO OFDM系统中的S-SAMP-PV算法

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-22 DOI: 10.1016/j.sigpro.2024.109798

Ruowen Yan, Qiao Li, Huagang Xiong

Power Line Communication (PLC) offers an efficient solution for data transmission over electrical power lines, presenting a promising avenue for in-flight communication in More Electrical Aircraft (MEA). A significant challenge in airborne PLC is Impulsive Noise (IN), which hampers transmission reliability. Existing noise mitigation methods, while valuable, face limitations in airborne settings due to computational intensiveness and sub-optimal sparse recovery performance. This paper introduces the Structured Sparsity Adaptive Matching Pursuit with Preliminary partial support estimation and Variable step-size (S-SAMP-PV) algorithm, devised for Multiple-Input-Multiple-Output (MIMO) systems. It uniquely pre-estimates partial support of sparse IN signals, enabling adaptive convergence without prior sparsity knowledge. This methodology substantially reduces computational demands, satisfying stringent real-time requirements of airborne applications. In simulation, the S-SAMP-PV algorithm exhibits marked advantages over traditional algorithms such as Orthogonal Matching Pursuit (OMP). Specifically, it realizes an approximate 81.3

%

reduction in Normalized Mean Square Error (NMSE) and demonstrates around 37

%

improvement in computational efficiency relative to OMP. Moreover, its Bit Error Rate (BER) performance at high Signal to Noise Ratio (SNR) approaches the ideal scenario where IN is assumed to be perfectly eliminated. These results emphasize the promise of S-SAMP-PV in elevating the performance of airborne PLC systems by efficient IN mitigation.

电力线通信（PLC）为电力线上的数据传输提供了一种有效的解决方案，为多电气飞机（MEA）的飞行通信提供了一条有前途的途径。机载PLC面临的一个重大挑战是脉冲噪声，它影响了传输的可靠性。现有的噪声缓解方法虽然有价值，但由于计算强度和次优稀疏恢复性能，在机载环境中面临局限性。本文介绍了针对多输入多输出（MIMO）系统设计的具有初步部分支持估计和变步长的结构化稀疏自适应匹配追踪（S-SAMP-PV）算法。它独特地预估了稀疏IN信号的部分支持度，使自适应收敛无需先验稀疏性知识。这种方法大大减少了计算需求，满足了机载应用的严格实时要求。在仿真中，S-SAMP-PV算法比传统的正交匹配追踪（OMP）算法具有明显的优势。具体来说，它实现了标准化均方误差（NMSE）降低了大约81.3%，并且相对于OMP的计算效率提高了大约37%。此外，在高信噪比（SNR）下，其误码率（BER）性能接近于假设完全消除IN的理想情况。这些结果强调了S-SAMP-PV通过有效的in缓解来提高机载PLC系统性能的前景。

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引用次数: 0

Tensor singular value decomposition and low-rank representation for hyperspectral image unmixing 高光谱图像解混的张量奇异值分解与低秩表示

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-22 DOI: 10.1016/j.sigpro.2024.109799

Zi-Yue Zhu , Ting-Zhu Huang , Jie Huang , Ling Wu

Hyperspectral unmixing (HU) finds pure spectra (endmembers) and their proportions (abundances) in hyperspectral images (HSIs). The matrix–vector non-negative tensor factorization (MV-NTF) describes the HSI as the sum of the outer products of the endmembers and their corresponding abundance maps. Concatenating these abundance maps in the third dimension is precisely the abundance tensor. Many subsequent studies have focused on exploiting different priors to improve the accuracy of MV-NTF. Most of them, however, explore the properties of abundance matrices or abundance maps, which is hard to fully utilize the structural similarity in abundance tensors corresponding to HSIs containing mixed materials. In this paper, we use the tensor singular value decomposition (T-SVD) to directly exploit the structural information in the abundance tensor. For this purpose, we propose a new low-rank representation by dividing the abundance tensor into a main feature tensor and a disturbance term. We characterize the low-rank property of the feature tensor after performing T-SVD and characterize the sparsity of the disturbance term. In this vein, we establish a model named abundance low-rank structure based on T-SVD (ALRSTD) and propose the solution algorithm. Experiments show that ALRSTD has better unmixing effect compared with several state-of-the-art methods, especially in the abundance estimation and the computation speed.

高光谱分解（HU）在高光谱图像（hsi）中发现纯光谱（端元）及其比例（丰度）。矩阵向量非负张量分解（MV-NTF）将HSI描述为端元的外积及其相应的丰度映射的和。将这些丰度图在三维空间中连接起来就是丰度张量。随后的许多研究都集中在利用不同的先验来提高MV-NTF的准确性。然而，它们大多是探索丰度矩阵或丰度图的性质，难以充分利用含有混合材料的hsi所对应的丰度张量的结构相似性。本文利用张量奇异值分解（T-SVD）直接挖掘丰度张量中的结构信息。为此，我们提出了一种新的低秩表示，将丰度张量划分为一个主特征张量和一个扰动项。我们在进行T-SVD后表征了特征张量的低秩性，并表征了扰动项的稀疏性。在此基础上，我们建立了基于T-SVD （ALRSTD）的丰度低秩结构模型，并提出了求解算法。实验表明，与现有的几种方法相比，该方法具有更好的解混效果，特别是在丰度估计和计算速度方面。

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引用次数: 0

PIPO-Net: A Penalty-based Independent Parameters Optimization deep unfolding Network PIPO-Net：基于惩罚的独立参数优化深度展开网络

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-22 DOI: 10.1016/j.sigpro.2024.109796

Xiumei Li , Zhijie Zhang , Huang Bai , Ljubiša Stanković , Junpeng Hao , Junmei Sun

Compressive sensing (CS) has been widely applied in signal and image processing fields. Traditional CS reconstruction algorithms have a complete theoretical foundation but suffer from the high computational complexity, while fashionable deep network-based methods can achieve high-accuracy reconstruction of CS but are short of interpretability. These facts motivate us to develop a deep unfolding network named the penalty-based independent parameters optimization network (PIPO-Net) to combine the merits of the above mentioned two kinds of CS methods. Each module of PIPO-Net can be viewed separately as an optimization problem with respective penalty function. The main characteristic of PIPO-Net is that, in each round of training, the learnable parameters in one module are updated independently from those of other modules. This makes the network more flexible to find the optimal solutions of the corresponding problems. Moreover, the mean-subtraction sampling and the high-frequency complementary blocks are developed to improve the performance of PIPO-Net. Experiments on reconstructing CS images demonstrate the effectiveness of the proposed PIPO-Net.

压缩传感（CS）已被广泛应用于信号和图像处理领域。传统的 CS 重构算法有完整的理论基础，但存在计算复杂度高的问题，而时下流行的基于深度网络的方法可以实现高精度的 CS 重构，但缺乏可解释性。这些事实促使我们结合上述两种 CS 方法的优点，开发了一种名为基于惩罚的独立参数优化网络（PIPO-Net）的深度展开网络。PIPO-Net 的每个模块都可单独视为一个优化问题，并带有各自的惩罚函数。PIPO-Net 的主要特点是，在每一轮训练中，一个模块的可学习参数的更新与其他模块无关。这使得网络能更灵活地找到相应问题的最优解。此外，为了提高 PIPO-Net 的性能，还开发了均值减法采样和高频互补块。重建 CS 图像的实验证明了所提出的 PIPO-Net 的有效性。

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引用次数: 0

Proximal gradient algorithm with dual momentum for robust compressive sensing MRI 鲁棒压缩感知MRI的双动量近端梯度算法

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-22 DOI: 10.1016/j.sigpro.2024.109817

Zhonghua Xie , Lingjun Liu , Zehong Chen , Cheng Wang

Adopting the new signal acquisition technology Compressive Sensing (CS) to Magnetic Resonance Imaging (MRI) reconstruction has been proved to be an effective scheme for reconstruction of high-resolution images with only a small fraction of data, thus making it the key to design a reconstruction algorithm with excellent performance. To achieve accelerated and robust CS-MRI reconstruction, a novel combination of Proximal Gradient (PG) and two types of momentum is developed. Firstly, to accelerate convergence of the PG iteration, we introduce the classical momentum method to solve the data-fitting subproblem for fast gradient search. Secondly, inspired by accelerated gradient strategies for convex optimizations, we further modify the obtained PG algorithm with the Nesterov's momentum technique to solve the prior subproblem, boosting its performance. We demonstrate the effectiveness and flexibility of the proposed method by combining it with two categories of prior models including a weighted nuclear norm regularization and a deep CNN (Convolutional Neural Network) prior model. As such, we obtain a dual momentum-based PG method, which can be equipped with any denoising engine. It is shown that the momentum-based PG method is closely related to the well-known Approximate Message Passing (AMP) algorithm. Experiments validate the effectiveness of leveraging dual momentum to accelerate the algorithm and demonstrate the superior performance of the proposed method both quantitatively and visually as compared with the existing methods.

将新的信号采集技术压缩感知（CS）应用于磁共振成像（MRI）重建已被证明是一种仅使用少量数据就能重建高分辨率图像的有效方案，因此设计一种性能优异的重建算法是关键。为了实现加速和稳健的CS-MRI重建，开发了一种新的近端梯度（PG）和两种动量的组合。首先，为了加快PG迭代的收敛速度，引入经典动量法求解快速梯度搜索的数据拟合子问题。其次，受凸优化的加速梯度策略的启发，我们进一步利用Nesterov动量技术对得到的PG算法进行改进，以解决先验子问题，提高其性能。我们通过将该方法与两类先验模型（包括加权核范数正则化和深度CNN（卷积神经网络）先验模型）相结合，证明了该方法的有效性和灵活性。因此，我们得到了一种基于双动量的PG方法，该方法可以配备任何去噪引擎。结果表明，基于动量的PG方法与著名的近似消息传递（AMP）算法密切相关。实验验证了利用双动量加速算法的有效性，并证明了与现有方法相比，该方法在定量和视觉上都具有优越的性能。

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引用次数: 0

An interference power allocation method against multi-objective radars based on optimized proximal policy optimization 基于优化近端策略的多目标雷达干扰功率分配方法

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-21 DOI: 10.1016/j.sigpro.2024.109785

Wenxu Zhang , Yajie Wang , Xiuming Zhou , Zhongkai Zhao , Feiran Liu

Aiming at the problem of interference resource scheduling in cognitive electronic warfare, a multi-objective interference power allocation method based on the proximal policy optimization (PPO) framework is proposed in this paper. Firstly, the confrontation between jammers and multi-objective radar networks is mapped as the interaction between the agent and the environment, and the radar target detection model under suppression interference is established. On this basis, an interference power allocation model against multi-objective radars based on PPO framework is constructed. Moreover, a reward normalization mechanism is introduced to optimize the reward setting, and an interference power allocation method based on optimized PPO is proposed. Meanwhile, this paper constructs a confrontation scenario in which the jammer covers the target aircraft to break through the multi-objective radar network. Simulation experiments are conducted based on this scenario to verify the effectiveness of the method proposed in this paper. The interference power allocation method proposed in this paper can intelligently adjust the power allocation scheme of the jammer according to the electromagnetic situation on the battlefield, optimize the resource utilization of the jammer, and occupy the initiative on the battlefield.

针对认知电子战中的干扰资源调度问题，本文提出了一种基于近端策略优化（PPO）框架的多目标干扰功率分配方法。首先，将干扰机与多目标雷达网络之间的对抗映射为代理与环境之间的相互作用，并建立了压制干扰下的雷达目标探测模型。在此基础上，构建了基于 PPO 框架的多目标雷达干扰功率分配模型。此外，还引入了奖励归一化机制来优化奖励设置，并提出了基于优化 PPO 的干扰功率分配方法。同时，本文构建了干扰机覆盖目标飞机以突破多目标雷达网络的对抗场景。基于该场景进行了仿真实验，以验证本文所提方法的有效性。本文提出的干扰功率分配方法可根据战场电磁态势智能调整干扰机功率分配方案，优化干扰机资源利用率，占据战场主动权。

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引用次数: 0

Sparse Bayesian Learning with Jeffreys’ Noninformative Prior for Off-Grid DOA Estimation 基于Jeffreys无信息先验的稀疏贝叶斯学习离网DOA估计

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-21 DOI: 10.1016/j.sigpro.2024.109809

Mahmood Karimi, Mohammadreza Zare, Mostafa Derakhtian

Sparse Bayesian learning (SBL) algorithms are attractive methods for direction-of-arrival (DOA) estimation and have certain advantages over other sparse representation-based DOA estimation methods. In this paper, a new computationally efficient SBL algorithm for DOA estimation is developed which considers a noninformative prior for hyperparameters. This noninformative prior is obtained using the well-known Jeffreys’ rule which is based on the Fisher information and the hyperparameters are powers of the source signals. The Jeffreys’ prior that is obtained for the hyperparameters is different from the conventional Jeffreys’ prior used in the literature. Moreover, a method for refining the DOA estimates obtained by the SBL algorithm is derived to reduce the off-grid error. Analysis indicates that the computational complexity of the proposed SBL algorithm per iteration is less than that of other existing SBL algorithms. Simulation results exhibit the superior performance of the proposed SBL algorithm compared to state-of-the-art SBL algorithms in terms of DOA estimation accuracy and total computational complexity. Moreover, simulations reveal that, unlike certain other state-of-the-art SBL algorithms, the proposed algorithm is robust to changes in noise power.

本文提出了一种新的计算效率高的SBL算法，该算法考虑了超参数的非信息先验。该非信息先验是利用著名的基于Fisher信息的Jeffreys规则获得的，超参数是源信号的幂。对超参数得到的杰弗里斯先验与文献中使用的传统杰弗里斯先验不同。此外，还提出了一种改进SBL算法得到的DOA估计的方法，以减小离网误差。分析表明，本文提出的SBL算法每次迭代的计算量低于现有的其他SBL算法。仿真结果表明，该算法在DOA估计精度和总计算复杂度方面优于现有的SBL算法。此外，仿真结果表明，与某些最先进的SBL算法不同，该算法对噪声功率的变化具有鲁棒性。

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引用次数: 0

Codesign of transmit waveform and reflective beamforming for active reconfigurable intelligent surface-aided MIMO ISAC system 为主动可重构智能表面辅助多输入多输出 ISAC 系统设计发射波形和反射波束成形

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-20 DOI: 10.1016/j.sigpro.2024.109795

Hongtao Li, Xu He, Shengyao Chen, Qi Feng, Sirui Tian, Feng Xi

This article discusses the active reconfigurable intelligent surface (ARIS)-aided integrated sensing and communication (ISAC) system for non-line-of-sight (NLoS) target sensing in cluttered environments while performing multi-user communication. To optimize sensing and communication performance simultaneously, we jointly design the shared transmit waveform, ARIS reflection coefficients and radar receive filter by using the multi-user interference and the reciprocal of radar output signal-to-interference-plus-noise ratio as metrics. Limited by practical requirements, the transmit waveform suffers from constant modulus or total energy constraints and the ARIS is subject to both maximum power and amplification gain constraints. Based on these considerations, the proposed codesign is formulated into a nonconvex constrained fractional function minimization problem. To tackle it effectively, we first translate the fractional objective into an integral form by employing Dinkelbach transform and then propose an alternating optimization-based algorithm, where the transmit waveform and ARIS reflection coefficients are respectively optimized by the customized algorithms based on the consensus alternating direction method of multipliers, and the receive filter has a closed-form optimal solution. Numerical results demonstrate that the ARIS-aided ISAC concurrently achieve superior NLoS sensing and communication performance to passive reconfigurable intelligent surface-aided and traditional ISACs in cluttered environments, regardless of waveform constraints and sensing-communication trade-off factor.

本文讨论了主动可重构智能表面（ARIS）辅助综合传感与通信（ISAC）系统，该系统可在杂波环境中进行非视距（NLoS）目标传感，同时执行多用户通信。为了同时优化传感和通信性能，我们以多用户干扰和雷达输出信号与干扰加噪声比的倒数为指标，联合设计了共享发射波形、ARIS 反射系数和雷达接收滤波器。受实际要求的限制，发射波形受到恒定模数或总能量的约束，而 ARIS 则受到最大功率和放大增益的约束。基于这些考虑，我们将拟议的编码设计表述为一个非凸约束分数函数最小化问题。为了有效解决这个问题，我们首先通过丁克巴赫变换将分数目标转化为积分形式，然后提出了一种基于交替优化的算法，其中发射波形和 ARIS 反射系数分别由基于乘法器共识交替方向法的定制算法进行优化，接收滤波器有一个闭式最优解。数值结果表明，在杂波环境中，ARIS辅助ISAC同时实现了优于无源可重构智能表面辅助和传统ISAC的NLoS传感和通信性能，不受波形约束和传感-通信权衡系数的影响。

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引用次数: 0

Adaptive three-dimensional histogram modification for JPEG reversible data hiding 用于 JPEG 可逆数据隐藏的自适应三维直方图修改

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-19 DOI: 10.1016/j.sigpro.2024.109786

Fengyong Li , Qiankuan Wang , Xinpeng Zhang , Chuan Qin

JPEG reversible data hiding (RDH) is a data hiding technique that requires both accurate data extraction and perfect recovery of the original JPEG image. Existing JPEG RDH schemes often rely on the distortion model of DCT coefficient frequency itself, failing to fully utilize the correlation between adjacent coefficients, resulting in inferior visual quality and significant file size expansion for JPEG image containing hidden data. To address the problem, we design a new JPEG RDH scheme by introducing three-dimensional (3D) histogram modification mechanism. We firstly evaluate the costs of each DCT block and frequency band to build coefficient triplet grouping mechanism. Furthermore, we construct a series of three-dimensional histogram mappings to perform data embedding according to the grouped DCT coefficient triplets, and then optimize the embedding efficiency by adaptively integrating multi-dimensional histogram mapping for the given embedding capacity. Extensive experiments demonstrate that our scheme significantly outperforms the state-of-the-art JPEG RDH schemes and can achieve efficient balance between higher visual quality and smaller file size changes while keeping JPEG file format unchanged.

JPEG 可逆数据隐藏（RDH）是一种数据隐藏技术，要求既能准确提取数据，又能完美恢复原始 JPEG 图像。现有的 JPEG RDH 方案往往依赖于 DCT 系数频率本身的失真模型，不能充分利用相邻系数之间的相关性，导致包含隐藏数据的 JPEG 图像视觉质量较差，文件大小明显扩大。针对这一问题，我们通过引入三维（3D）直方图修改机制，设计了一种新的 JPEG RDH 方案。我们首先评估每个 DCT 块和频带的成本，以建立系数三重分组机制。此外，我们还构建了一系列三维直方图映射，根据分组后的 DCT 系数三元组执行数据嵌入，然后通过自适应集成多维直方图映射，优化嵌入效率，以获得给定的嵌入容量。大量实验证明，我们的方案明显优于最先进的 JPEG RDH 方案，并能在保持 JPEG 文件格式不变的情况下，在更高的视觉质量和更小的文件大小变化之间实现有效平衡。

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引用次数: 0

A fast-converging Bayesian tensor inference method for wireless channel estimation 用于无线信道估计的快速融合贝叶斯张量推理方法

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2024-11-19 DOI: 10.1016/j.sigpro.2024.109770

Yuzhe Sun, Wei Wang, Yuanfeng He, Yufan Wang

In variational inference-based tensor channel estimation, high order singular value decomposition (HOSVD) initialization effectively captures the latent features of factor matrices, and accelerates convergence speed. However, HOSVD-based initialization further exacerbates the overfitting issue of the tensor variation Bayesian (TVB) method on each factor matrix element, leading to inaccurate rank estimation, and then significantly degrading channel parameter estimation performance. To prevent overfitting, we propose a new TVB method based on array spatial prior (ASP), which incorporates space correlations in tensor data, without introducing additional hierarchical probabilistic models. By analyzing the inferred posterior distribution and the non-decreasing property of the evidence lower bound (ELBO), we confirm the favorable convergence characteristics and global search capability of the proposed algorithm. Through simulations and experiments, we observe that compared to traditional TVB, the proposed algorithm achieves accurate automatic rank determination (ARD) in just a few iterations, significantly reducing convergence time. Meanwhile, it demonstrates superior parameter estimation accuracy with fewer iterations than the compared method.

在基于变分推理的张量信道估计中，高阶奇异值分解（HOSVD）初始化能有效捕捉因子矩阵的潜在特征，并加快收敛速度。然而，基于高阶奇异值分解的初始化会进一步加剧张量变化贝叶斯（TVB）方法对每个因子矩阵元素的过拟合问题，导致秩估计不准确，进而显著降低信道参数估计性能。为了防止过拟合，我们提出了一种基于阵列空间先验（ASP）的新 TVB 方法，该方法将空间相关性纳入了张量数据中，而无需引入额外的分层概率模型。通过分析推断的后验分布和证据下界（ELBO）的非递减特性，我们证实了所提算法的良好收敛特性和全局搜索能力。通过仿真和实验，我们发现与传统的 TVB 相比，所提出的算法只需几次迭代就能实现精确的自动秩确定（ARD），大大缩短了收敛时间。同时，与其他方法相比，该算法以更少的迭代次数实现了更高的参数估计精度。

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引用次数: 0