Channel Estimation Using Multi-stage Compressed Sensing for Millimeter Wave MIMO Systems

Hadji, Baghdad; Bey, Abdeldjalil Aïssa El; Fergani, Lamya; Djeddou, Mustapha

doi:10.1109/vtc2021-spring51267.2021.9448773

Cited by 2 publications

(22 citation statements)

References 17 publications

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“…In this subsection, we revisit the sparse representation of the channel estimation problem proposed in [26] which is based on the multi-stage CS approach. For enabling the sparse formulation of mmWave channel estimation, we exploit the open-loop beam training method, where the Tx sends M known pilot followed by N −(M +1) unknown data symbols.…”

Section: B Sparse Formulation Based On a Multi-stage Cs Approchmentioning

confidence: 99%

“…Algorithm 1 summarizes all steps for designing the optimal sensing matrix. Starting with the first stage, Φ P is constructed by random pilots and hybrid training precoders/combiners which are generated randomly using six quantization bits to design RF phase shifters according to the multi-stage CS approach [26]. Moreover, we introduce Ψ as a given sparsifying dictionary to get the equivalent dictionary D P = Φ P Ψ.…”

Section: Sensing Matrix Design For Cs-based Channel Estimationmentioning

confidence: 99%

“…As mentioned above, the second stage is dedicated to detect unknown data symbols by employing the estimated channel from the first stage. As in [26], we adopt in this work the QPSK modulation scheme to transmit unknown data. From the system model in (11), each sensing submatrix φ n of the collected sensing matrix Φ D can be expressed as…”

Section: Joint Hybrid Precoder and Combiner Designmentioning

confidence: 99%

“…As k = (N − (M + 1)) E s is a constant term, it does not change the set of optimal solutions, hence, it can be ignored from (26). GtD is the target gram matrix which is chosen from a convex set H µwelch as defined in (18).…”

Section: Joint Hybrid Precoder and Combiner Designmentioning

confidence: 99%

“…Unlike the design of symbol pilots based on the total coherence minimization problem, the authors in [25] decompose the minimization problem into separate transmit and receive coherence minimization problems. In our previous work [26], we proposed a multi-stage CS-based algorithm to estimate the channel of the hybrid mmWave MIMO transceiver by using limited random pilot numbers and detected data symbols as training beams for reducing the effect of the overlapping between training beams throughout the estimation process to maximize spatial diversity. In the all discussed works above, the hybrid precoder and combiner are designed separately to estimate the mmWave channel.…”

Section: Introductionmentioning

confidence: 99%

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Joint Hybrid Precoding and Combining Design Based Multi-Stage Compressed Sensing Approach for mmWave MIMO Channel Estimation

Hadji,

Aïssa-El-Bey,

Fergani

et al. 2023

IEEE Access

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Although the design of hybrid precoders and combiners separately from the complete channel state information (CSI) offers satisfactory performance, the resulting spatial multiplexing channel may not always be orthogonal during communication. Also, acquiring CSI to design optimal precoders and combiners poses several challenges, particularly in millimeter wave (mmWave) channel estimation, and getting the sensing matrix is equivalent to designing the precoders and combiners. For this, we propose a new iterative method based on alternating minimization to design the optimal sensing matrix (incoherent projection matrix) with the given dictionary to minimize the mutual coherence values (µ mx , µ ave and µ all ) simultaneously according to the equiangular tight frame (ETF) properties for achieving better-compressed sensing (CS) recovery performance. Then, in order to derive the best hybrid precoders and combiners jointly from the optimally designed sensing matrix, we formulate the optimization design problem as the nearest Kronecker product (NKP) problem. The proposed sensing matrix design works better at lowering the mutual coherence values concurrently with the straightforward shrinkage function, according to simulation findings of mutual coherence values evolution versus outer iteration numbers. In comparison to existing codebookbased hybrid precoder/combiner schemes, the proposed joint hybrid precoder and combiner design improves the performance of the simulation results obtained by multi-stage CS-based mmWave channel estimation in terms of channel estimation accuracy and spectral efficiency (SE).INDEX TERMS Millimeter-wave channel estimation, Multi-stage CS approach, Hybrid mmWave MIMO transceiver, Joint hybrid precoder and combiner design, Equiangular tight frame, Mutual coherence values, Incoherent projection matrix.

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Section: B Sparse Formulation Based On a Multi-stage Cs Approchmentioning

confidence: 99%

Section: Sensing Matrix Design For Cs-based Channel Estimationmentioning

confidence: 99%

Section: Joint Hybrid Precoder and Combiner Designmentioning

confidence: 99%

Section: Joint Hybrid Precoder and Combiner Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Hybrid Precoding and Combining Design Based Multi-Stage Compressed Sensing Approach for mmWave MIMO Channel Estimation

Hadji,

Aïssa-El-Bey,

Fergani

et al. 2023

IEEE Access

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Residual Learning and Multi-Path Feature Fusion-Based Channel Estimation for Millimeter-Wave Massive MIMO System

Zheng

Liu

Liang

et al. 2022

Entropy

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Channel estimation is a challenging task in a millimeter-wave (mm Wave) massive multiple-input multiple-output (MIMO) system. The existing deep learning scheme, which learns the mapping from the input to the target channel, has great difficulty in estimating the exact channel state information (CSI). In this paper, we consider the quantized received measurements as a low-resolution image, and we adopt the deep learning-based image super-resolution technique to reconstruct the mm Wave channel. Specifically, we exploit a state-of-the-art channel estimation framework based on residual learning and multi-path feature fusion (RL-MFF-Net). Firstly, residual learning makes the channel estimator focus on learning high-frequency residual information between the quantized received measurements and the mm Wave channel, while abundant low-frequency information is bypassed through skip connections. Moreover, to address the estimator’s gradient dispersion problem, a dense connection is added to the residual blocks to ensure the maximum information flow between the layers. Furthermore, the underlying mm Wave channel local features extracted from different residual blocks are preserved by multi-path feature fusion. The simulation results demonstrate that the proposed scheme outperforms traditional methods as well as existing deep learning methods, especially in the low signal-to-noise-ration (SNR) region.

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Channel Estimation Using Multi-stage Compressed Sensing for Millimeter Wave MIMO Systems

Cited by 2 publications

References 17 publications

Joint Hybrid Precoding and Combining Design Based Multi-Stage Compressed Sensing Approach for mmWave MIMO Channel Estimation

Joint Hybrid Precoding and Combining Design Based Multi-Stage Compressed Sensing Approach for mmWave MIMO Channel Estimation

Residual Learning and Multi-Path Feature Fusion-Based Channel Estimation for Millimeter-Wave Massive MIMO System

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