检索结果-内蒙古大学图书馆

joint channel estimation and data detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第20期63卷 5369-5382页

作者： Prasad, Ranjitha Murthy, Chandra R. Rao, Bhaskar D. Indian Inst Sci IISc Dept Elect & Commun Engn ECE Bangalore Karnataka India IISc Dept Elect & Commun Engn ECE Bangalore Karnataka India San Diego State Univ Dept Elect & Comp Engn San Diego CA 92037 USA

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

关键词： Sparse Bayesian learning joint channel estimation and data detection joint sparsity cluster sparsity multiple measurement vectors

来源：评论

学校读者我要写书评

暂无评论

joint channel estimation and data detection for IRS-Assisted OFDM Systems

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第9期72卷 11963-11974页

作者： Ji, Chen Dong, Haoyang Xu, Weichao Dai, Jisheng Jiangsu Univ Dept Elect Engn & Informat Sci Zhenjiang 212013 Jiangsu Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Guangdong Univ Technol Dept Automat Control Guangzhou 510006 Peoples R China

Intelligent reconfigurable surface (IRS) becomes a promising technology for improving spectrum efficiency and reducing the energy consumption in the next-generation networks. To reap the passive beamforming gain of IRS, accurate channel estimation is essentially required. However, the existing channel estimation methods for the IRS-assisted orthogonal frequency division multiplexing (OFDM) systems suffer from serious performance loss, due to using the least-squares (LS) estimator and ignoring the channel sparsity in the time domain. Another drawback of the existing methods is that the channel estimation and data detection are separated from each other so that any inaccurate channel estimation would cause an error accumulation problem for the data detection. To overcome these drawbacks, we propose a novel joint sparse channel estimation and data detection method for the IRS-assisted OFDM systems. The novelties of the proposed method are twofold: i) present a new common sparse representation model for the joint channel estimation and data detection, which can utilize the common channel sparsity between the pilots and data streams to significantly improve the channel estimation performance and reduce the training overhead;and ii) propose an efficient variational Bayesian inference (VBI) framework to simultaneously perform the Bayesian inference for the sparse channel recovery and data detection, where the coupling effect caused by the reflection pattern is trickly evaded by adopting an additional column-independent factorization. Besides exploiting the common sparsity for the channel estimation and data detection, the proposed method also avoids using the LS estimator, and thus its performance can be greatly enhanced. Simulation results verify the superiority of the proposed method.

关键词： Intelligent reconfigurable surface (IRS) joint channel estimation and data detection variational Bayesian inference (VBI) orthogonal frequency division multiplexing (OFDM)

来源：评论

学校读者我要写书评

暂无评论

joint channel estimation and data detection for Hybrid RIS Aided Millimeter Wave OTFS Systems

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第10期70卷 6832-6848页

作者： Li, Muye Zhang, Shun Ge, Yao Gao, Feifei Fan, Pingzhi Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Nanyang Technol Univ Continental NTU Corp Lab Singapore 637553 Singapore Tsinghua Univ Dept Automat State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ State Key Informat Sci & Technol TNList Beijing 100084 Peoples R China Southwest Jiaotong Univ Key Lab Informat Coding & Transmiss Sichuan Prov CSNMT Int Cooperat Res Ctr MoST Chengdu 610031 Peoples R China

For high mobility communication scenario, the recently emerged orthogonal time frequency space (OTFS) modulation introduces a new delay-Doppler domain signal space, and can provide better communication performance than traditional orthogonal frequency division multiplexing system. This article focuses on the joint channel estimation and data detection (JCEDD) for hybrid reconfigurable intelligent surface (HRIS) aided millimeter wave (mmWave) OTFS systems. Firstly, a new transmission structure is designed. Within the pilot durations of the designed structure, partial HRIS elements are alternatively activated. The time domain channel model is then exhibited. Secondly, the received signal model for both the HRIS over time domain and the base station over delay-Doppler domain are studied. Thirdly, by utilizing channel parameters acquired at the HRIS, an HRIS beamforming design strategy is proposed. For the OTFS transmission, we propose a JCEDD scheme over delay-Doppler domain. In this scheme, message passing (MP) algorithm is designed to simultaneously obtain the equivalent channel gain and the data symbols. On the other hand, the channel parameters, i.e., the Doppler shift, the channel sparsity, and the channel variance, are updated through expectation-maximization (EM) algorithm. By iteratively executing the MP and EM algorithm, both the channel and the unknown data symbols can be accurately acquired. Finally, simulation results are provided to validate the effectiveness of our proposed JCEDD scheme.

关键词： Human-robot interaction channel estimation Symbols Signal processing algorithms Radio frequency OFDM Array signal processing OTFS hybrid RIS parameter extraction joint channel estimation and data detection message passing

来源：评论

学校读者我要写书评

暂无评论

joint channel estimation and data detection in MIMO-OFDM Systems Using Sparse Bayesian Learning

Joint Channel Estimation and Data Detection in MIMO-OFDM Sys...

引用

20th National Conference on Communications (NCC)

作者： Prasad, Ranjitha Murthy, Chandra R. Indian Inst Sci Dept ECE Bangalore 560012 Karnataka India

ISBN: (纸本)9781479923618

The impulse response of wireless channels between each transmit and receive antenna in a MIMO-OFDM system is known to be approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. Moreover, it is known that the channel impulse responses in a MIMO-OFDM system are approximately group-sparse (a-group-sparse), i.e., the time-lags of the significant paths of channel impulse response between every transmit and receive antenna pair coincide. Accordingly, we cast the problem of estimating the a-group-sparse channels in the Bayesian framework, and propose novel algorithms that employ the multiple measurement vectors at the N-r receive antennas. First, we adapt the known MSBL algorithm for pilot-based a-group-sparse channel estimation in MIMO-OFDM systems. Subsequently, we generalize the MSBL algorithm to obtain a novel J-MSBL algorithm for joint a-group-sparse channel estimation and data detection. We illustrate the efficacy of the proposed techniques in terms of the mean square error and coded bit error rate performance using Monte Carlo simulations.

关键词： joint channel estimation and data detection Group sparsity MIMO OFDM Sparse Bayesian learning Expectation maximization

来源：评论

学校读者我要写书评

暂无评论

joint channel estimation and data detection IN MASSIVE MIMO SYSTEMS BASED ON DIFFUSION MODELS 49

JOINT CHANNEL ESTIMATION AND DATA DETECTION IN MASSIVE MIMO ...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zilberstein, Nicolas Swami, Ananthram Segarra, Santiago Rice Univ Houston TX 77005 USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350344868;9798350344851

We propose a joint channel estimation and data detection algorithm for massive multilple-input multiple-output systems based on diffusion models. Our proposed method solves the blind inverse problem by sampling from the joint posterior distribution of the symbols and channels and computing an approximate maximum a posteriori estimation. To achieve this, we construct a diffusion process that models the joint distribution of the channels and symbols given noisy observations, and then run the reverse process to generate the samples. A unique contribution of the algorithm is to include the discrete prior distribution of the symbols and a learned prior for the channels. Indeed, this is key as it allows a more efficient exploration of the joint search space and, therefore, enhances the sampling process. Through numerical experiments, we demonstrate that our method yields a lower normalized mean squared error than competing approaches and reduces the pilot overhead.

关键词： joint channel estimation and data detection score-based generative models Langevin diffusion blind inverse problems

来源：评论

学校读者我要写书评

暂无评论

Optimal joint channel estimation and data detection for Massive SIMO Wireless Systems: A Polynomial Complexity Solution

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 2020年第3期66卷 1822-1844页

作者： Xu, Weiyu Alshamary, Haider Ali Jasim Al-Naffouri, Tareq Zaib, Alam Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Diyala Dept Commun Engn Baqubah *** Iraq King Abdullah Univ Sci & Technol Elect Engn Program Thuwal 23955 Saudi Arabia King Abdullah Univ Sci & Technol Thuwal 23955 Saudi Arabia COMSATS Univ Islamabad Dept Elect & Comp Engn Abbottabad Campus Abbottabad 22060 Pakistan

By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constant-modulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (T) and the number of receive antennas (N), even when the number of receive antennas grows polynomially in the channel coherence time (N = O(T-11) suffices to guarantee an expected computational complexity cubic in T and linear in N). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.

关键词： MIMO communication Receiving antennas Computational complexity channel estimation Wireless communication Coherence joint channel estimation and data detection massive SIMO generalized likelihood ratio test tree search algorithm

来源：评论

学校读者我要写书评

暂无评论

Bilinear Expectation Propagation for Distributed Semi-Blind joint channel estimation and data detection in Cell-Free Massive MIMO

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

引用

IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2024年 5卷 284-293页

作者： Karataev, Alexander Forsch, Christian Cottatellucci, Laura Friedrich Alexander Univ Erlangen Nurnberg Inst Digital Commun D-91054 Erlangen Germany

We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink transmission and propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD). We formulate the problem in the framework of bilinear inference and develop a solution based on the expectation propagation (EP) method for both channel estimation and data detection. We propose a new approximation of the joint a posteriori distribution of the channel and data whose representation as a factor graph enables the application of the EP approach using the message-passing technique, local low-complexity computations at the nodes, and an effective modeling of channel-data interplay. The derived algorithm, called bilinear-EP JCD, allows for a distributed implementation among access points (APs) and the central processing unit (CPU) and has polynomial complexity. Our simulation results show that it outperforms other EP-based state-of-the-art polynomial time algorithms.

关键词： Expectation propagation bilinear inference Bayesian learning approximate inference distributed algorithms joint channel estimation and data detection cell-free massive MIMO

来源：评论

学校读者我要写书评

暂无评论

Message Passing-Aided joint data detection and estimation of Nonlinear Satellite channels

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第2期72卷 1763-1774页

作者： Zhang, Yikun Li, Bin Wu, Nan Ma, Yunsi Yuan, Weijie Hanzo, Lajos Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

Satellite communication is capable of supporting seamless global coverage. However, owing to the reliance on limited-duration solar power, the high power amplifier (HPA) is often driven close to its saturation point, which leads to severe nonlinear distortion in satellite channels. Thus, mitigating the effect of the nonlinear distortion becomes essential for reliable communications. In this article, we propose an efficient joint channel estimation and data detection method based on message passing within the associated factor graph modelling the HPA employed in nonlinear satellite channels. Then, we develop a combined belief propagation and mean field (BP-MF) method to cope with the hard constraints and dense short loops on the factor graph. In particular, the parametric message updating expressions relying on the canonical parameters are derived in the symbol detection part. To alleviate the impact of dense loops, we reformulate the system model into a compact form within the channel estimation part and then reconstruct a loop-free subgraph associated with vector-valued nodes to guarantee convergence. Furthermore, the proposed BP-MF method is also extended to the realistic scenario of having unknown noise variance. To further reduce the computational complexity of the large-scale matrix inversion of channel estimation, the generalized approximate message passing (GAMP) algorithm is employed to decouple the vector of channel coefficient estimation into a series of scalar estimations. Simulation results show that the proposed methods outperform the state-of-the-art benchmarks both in terms of bit error rate performance and channel estimation accuracy.

关键词： channel estimation Satellites Equalizers estimation Symbols Message passing Computational complexity Nonlinear satellite channel Volterra series joint channel estimation and data detection mean field approximation generalized approximate message passing

来源：评论

学校读者我要写书评

暂无评论

joint data detection AND DOMINANT SINGULAR MODE estimation IN TIME VARYING RECIPROCAL MIMO SYSTEMS

JOINT DATA DETECTION AND DOMINANT SINGULAR MODE ESTIMATION I...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Prasad, Ranjitha Bharath, B. N. Murthy, Chandra R. Indian Inst Sci Dept Elect Commun Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9781457705397

This paper proposes an algorithm for joint data detection and tracking of the dominant singular mode of a time varying channel at the transmitter and receiver of a time division duplex multiple input multiple output beamforming system. The method proposed is a modified expectation maximization algorithm which utilizes an initial estimate to track the dominant modes of the channel at the transmitter and the receiver blindly;and simultaneously detects the unknown data. Furthermore, the estimates are constrained to be within a confidence interval of the previous estimate in order to improve the tracking performance and mitigate the effect of error propagation. Monte-Carlo simulation results of the symbol error rate and the mean square inner product between the estimated and the true singular vector are plotted to show the performance benefits offered by the proposed method compared to existing techniques.

关键词： joint channel estimation and data detection expectation maximization reciprocal channels

来源：评论

学校读者我要写书评

暂无评论

LDPC-coded MIMO systems with unknown block fading channels: Soft MIMO detector design, channel estimation, and code optimization

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2006年第4期54卷 1504-1518页

作者： Zheng, J Rao, BD Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

This paper considers the design of a practical low-density parity check (LDPC)-coded multiple-input multiple-output (MIMO) system composed of M transmit and N receive antennas operating in a flat-fading environment where channel state information (CSI) is assumed to be unavailable both to the transmitter and the receiver. A soft iterative receiver structure is developed, which consists of three main blocks: a soft MIMO detector and two LDPC component soft decoders. We first propose at the component level several soft-input soft-output MIMO detectors whose performances are much better than the conventional minimal mean square error (MMSE)-based detectors. In particular, one optimal soft MIMO detector and two simplified suboptimal detectors are developed that do not require an explicit channel estimate and offer an effective tradeoff between complexity and performance. In addition, a modified expectation maximization (EM)-based MIMO detector is developed that completely removes positive feedback between input and output extrinsic information and provides much better performance compared with the direct EM-based detector that has strong correlations especially in fast-fading channels. At the structural level, the LDPC-coded MIMO receiver is constructed in an unconventional manner where the soft MIMO detector and LDPC variable node decoder form one super soft-decoding unit, and the LDPC check node decoder forms the other component of the iterative decoding scheme. By exploiting the proposed receiver structure, tractable extrinsic information transfer functions of the component soft decoders are obtained, which further lead to a simple and efficient LDPC code degree profile optimization algorithm with proven global optimality and guaranteed convergence from any initialization. Finally, numerical and simulation results are provided to confirm the advantages of the proposed design approach for the coded MIMO system.

关键词： degree profile optimization EM estimator extrinsic information transfer (EXIT)-chart iterative receiver structure joint channel estimation and data detection LDPC-coded multple-input multiple-output (MIMO) system soft-input soft-output MIMO detector

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：