Channel Estimation Based on Compressed Sensing for Massive MIMO Systems With Lens Antenna Array

Sharifi, Elham; Feghhi, Mahmood Mohassel; Azarnia, Ghanbar; Nouri, Sajjad; Lee, Duehee; Piran, Md. Jalil

doi:10.1109/access.2023.3297884

Cited by 6 publications

(1 citation statement)

References 67 publications

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“…From Figure 12, the spectrum efficiency is improved by varying the RIS reflection elements 20, 40, 60, and 80 and attains 99.9% higher results, by comparing the introduced MRKOMP algorithm is related with the previous approaches. PANDA Likewise, the Normalized Mean Square Error (NMSE) of the introduced method is compared with other existing methods such as MO-EST, PGM, OMPABC, NOMPA, Oracle Least Square (Oracle LS), 30 Eigenspace Projection (EP), 35 Deep Learning Frequency Selective Cascaded Channel Estimation (DL-FS-CE), 30 Regularized Zero Forcing-Deterministic Equivalent (RZF-DE), 24 Group Lasso with Effective Support and Greedy Algorithm with Intelligent Selection Support (GLES and GAISS), 36 and DA-OMP-BS, 37 as illustrated in Figure 13. Figure 13 presents the evaluations of NMSE vs SNR of the channel estimation.…”

Section: F I G U R E 5 Performance Analysis Of Iteration Vs Snrmentioning

confidence: 99%

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

Panda

2024

Int J Communication

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SummaryIn the context of Reconfigurable Intelligent Surface (RIS) assisted millimeter wave communications within the Multiple Input Multiple Output (MIMO) system, Channel Estimation (CE) poses a significant challenge due to signal sparsity and blockages between transmitters and receivers. Consequently, communication faces difficulties without a direct line‐of‐sight path, leading to increased pilot overhead. To address these obstacles, this research introduces a channel estimation approach for RIS‐aided millimeter wave communication employing the Multidimensional Runge Kutta Orthogonal Matching Pursuit (MRKOMP) algorithm in MIMO systems. The proposed method aims to enhance network coverage and channel capacity for efficient data transmission. By utilizing the multiscale cumulative residual distribution entropy function, the method establishes an indirect path when the line‐of‐sight is obstructed. Furthermore, the multidimensional orthogonal matching pursuit methodology calculates sparsity levels and facilitates sparse recovery. Optimization of the weight parameter through the Runge Kutta optimizer effectively eliminates sparsity, resulting in improved spectral efficiency and RIS gain. Simulation results demonstrate a significant 99.9% enhancement in network capacity and spectral efficiency compared to existing methods.

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Section: F I G U R E 5 Performance Analysis Of Iteration Vs Snrmentioning

confidence: 99%

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

Panda

2024

Int J Communication

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Millimeter wave–3D massive MIMO: Deep prior‐aided graph neural network combining with hierarchical residual learning for beamspace channel estimation

Sudarsan,

Mahendran,

Rathika

et al. 2024

Int J Communication

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SummaryMillimeter Wave (mmWave) communication has emerged as a transformative technology at the forefront of wireless communication. One of the key challenges in harnessing the potential of mmWave technology is overcoming the increased susceptibility to propagation losses and environmental obstacles. To address these challenges, Three‐Dimensional Massive Multiple‐Input Multiple‐Output (3D Massive MIMO) systems have gained traction. The 3D aspect extends this concept by considering the elevation dimension, allowing for enhanced spatial resolution and coverage. Accurate estimation of the channel in 3D Massive MIMO scenarios is particularly challenging because of the complex propagation characteristics of mmWave signals. This paper introduces an efficient‐Aided Graph Neural Network Combining with Hierarchical Residual Learning (DPrGNN‐HrResNetL), designed specifically for beamspace Channel Estimation (CE)in mmWave‐Massive MIMO environments. The proposed model leverages deep priors and GNN mechanisms to enhance the extraction of spatial features, while hierarchical residual connections facilitate effective information flow through the network. DPrGNN enables the model to capture and understand complex spatial relationships among different antenna elements. The incorporation of deep priors provides a mechanism for leveraging prior knowledge about channel characteristics. This enhances the efficiency of the learning process, allowing the model to learn and adapt more effectively. The integration of hierarchical residual connections facilitates effective information flow through the network. This is particularly important for modeling complex dependencies within the beamspace channel data, enhancing the learning capacity of the network. The performance of the DPrGNN‐HrResNetL model is evaluated across a range of Signal‐to‐Noise Ratios (SNRs), utilizing metrics such as Normalized Mean Squared Error (NMSE) to measure the accuracy of the estimation. The outcomes underscore the resilience and efficacy of the DPrGNN‐HrResNetL approach in achieving precise CE within demanding mmWave scenarios.

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Unlocking the power of recalling enhanced recurrent neural network: channel estimation and data analysis for 5G MIMO communication system

Alqahtani,

Pathak,

Pandiaraj

et al. 2023

Opt Quant Electron

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Channel Estimation Based on Compressed Sensing for Massive MIMO Systems With Lens Antenna Array

Cited by 6 publications

References 67 publications

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

Millimeter wave–3D massive MIMO: Deep prior‐aided graph neural network combining with hierarchical residual learning for beamspace channel estimation

Unlocking the power of recalling enhanced recurrent neural network: channel estimation and data analysis for 5G MIMO communication system

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