Channel Estimation for mmWave Massive MIMO Systems With Mixed-ADC Architecture

Zhang, Rui; Yang, Longcheng; Tang, Maobin; Tan, Weijie; Zhao, Jianmin

doi:10.1109/ojcoms.2023.3242668

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…In the literature, various channel estimators have been conducted for guiding the followup decoding and beamforming operations in hybrid massive MIMO systems. Among them, many techniques refer to the non-blind detections, which perform explicit channel estimation basically by transmitting and dealing with pilot symbols [19][20][21]. However, as the number of antennas increases, the pilot overhead could be enlarged accordingly and makes a negative impact on spectral efficiency.…”

Section: Existing Solutionsmentioning

confidence: 99%

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Han,

Zeng,

Zheng

et al. 2024

Remote Sensing

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Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pilot overhead as well as substantial computational complexity. With benefits from the powerful detection characteristics of MIMO radar, we aim for designing a novel sensing-assisted semi-blind detection scheme in this paper, where both the inherent low-rankness of signal matrix and the essential knowledge about geometric environments are fully exploited under a designated cooperative manner. Specifically, to efficiently recover the channel factorizations via the formulated low-rank matrix completion problem, a low-complexity iterative algorithm stemming from the alternating steepest descent (ASD) method is adopted to obtain the solutions in case of unknown noise statistics. Moreover, we take one step forward by employing the denoising convolutional neural network (DnCNN) to preprocess the received signals due to its favorable performance of handling Gaussian denoising. The overall paradigm of our proposed scheme consists of three stages, namely (1) target parameter sensing, (2) communication signal denoising and (3) semi-blind detection refinement. Simulation results show that significant estimation gains can be achieved by the proposed scheme with reduced training overhead in a variety of system settings.

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Section: Existing Solutionsmentioning

confidence: 99%

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Han,

Zeng,

Zheng

et al. 2024

Remote Sensing

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“…Hybrid precoding vast MIMO systems operating at the millimetre wave do not incorporate channel estimates into these methods [25]. In addition, a beamforming design method based on deep learning develops a neural network-trained beamformer that exhibits optimal spectrum efficacy [26]. Furthermore, an innovative hybrid precoding approach that operated on the principles of deep reinforcement learning was implemented [27].…”

Section: Related Workmentioning

confidence: 99%

Metaheuristic hybrid deep learning-based channel estimation and precoding for millimeter-wave massive MIMO system

Shailender,

Khera,

Singh

2024

Preprint

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In mm-wave massive MIMO systems, the inefficiency of the digital or analogue precoders necessitates that each antenna has its own radio frequency chain. From this standpoint, a method of hybrid precoding that is economically viable has been devised. Digital beamformers are used for controlling antenna elements using short-dimensional precoding. From this standpoint, a method of hybrid precoding that is economically viable has been devised. During the channel estimation phase, we propose using deep recurrent convolutional neural networks (Deep RCNNs). Salp Swarm Algorithm (SSA) and Tuna Swarm Optimization (TSO) algorithms are combined in a hybrid meta-heuristic algorithm to improve Deep RCNN's channel estimation efficiency. O-BiLSTM, or optimised bidirectional long short-term memory, is utilised by hybrid precoding in order to execute the optimal precode across the anticipated channel. In relation to spectral efficiency and normalised mean square error (NMSE), the enhanced channel estimation approach surpasses the existing schemes through the implementation of Deep RCNN. The spectrum efficacy of the O-BiLSTM hybrid precoder design method is observed to be superior when compared to alternative approaches.

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“…𝑓 𝐶𝑁𝑁 (𝑥) = 𝑊 3 . 𝑣𝑒𝑐(𝑟(𝐶𝑜𝑛𝑣(𝑟(𝐶𝑜𝑛𝑣(𝑥))))) + 𝑏 3 (7) Where convolution layer operation is specified as 𝐶𝑜𝑛𝑣(. ).…”

Section: C) Channel Estimation Using Ewoa-cnnmentioning

confidence: 99%

“…By implementing the aforementioned hybrid precoding structure, it becomes possible to lessen overall number of ADCs and RF chains within the system. This reduction in hardware components helps achieve a more favorable balance in terms of cost [7][8][9]. Another alternative solution involves substituting high-resolution ADCs with low-resolution ones for the quantization process, which directly contributes to minimizing power loss and lowering the hardware cost.…”

Section: Introductionmentioning

confidence: 99%

Optimized CNN and adaptive RBFNN for channel estimation and hybrid precoding approaches for multi user millimeter wave massive MIMO

Rajan. B,

Muthukumaran

2024

International Journal of Electronics

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The Millimetre Wave (mmWave) communication satisfy the demand for high data rates due to the characteristic of wide bandwidth. Using massive Multiple-Input Multiple-Output (MIMO) technology, significant propagation loss of mmWave communication is effectively compensated. However, it is challenging to provide a specialised Radio Frequency (RF) chain for each antenna due to constrained physical area with closely spaced antennas and prohibitive power consumption in mmWave massive MIMO systems. This paper presents novel approaches for effective channel estimation and hybrid precoding in mmWave communication systems. To address the challenges of channel estimation, a Convolutional Neural Network (CNN) is utilized, and network parameters are optimized using Enhanced Whale Optimization Algorithm (EWOA). The proposed CNN-based channel estimation method aims to accurately estimate the channel in mmWave systems with enhanced efficiency and reduced complexity. By training CNN using EWOA optimization algorithm, the network parameters are fine-tuned to improve accuracy and generalization capability of channel estimation process. Furthermore, hybrid precoding is achieved using Adaptive Radial Basis Function Neural Networks (Adaptive RBFNN) which enables efficient precoding while minimizing complexity. Moreover, the Adaptive RBFNN approach determines the optimal precoding weights based on Channel State Information (CSI), resulting in improved performance and reduced computational overhead. The performance analysis is validated using MATLAB/Simulink software and offers in providing effectual and reliable mmWave communication systems, facilitating the realization of high-speed and high-capacity wireless networks.

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Channel Estimation for mmWave Massive MIMO Systems With Mixed-ADC Architecture

Cited by 5 publications

References 35 publications

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Metaheuristic hybrid deep learning-based channel estimation and precoding for millimeter-wave massive MIMO system

Optimized CNN and adaptive RBFNN for channel estimation and hybrid precoding approaches for multi user millimeter wave massive MIMO

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