Deep Learning Based Channel Estimation Research Articles

Evaluating Sensitivity and Stability in Secure Deep Learning Based Channel Estimation for NextG Networks with Defensive Distillation

In the domain of wireless networks, including 5G and beyond, envisions a highly interconnected environment, enabling swift data transmission, expanded cell capacity, and minimal latency to support various applications and advanced manufacturing. Next Generation (NextG) networks seek to optimize and enhance network functionalities through Artificial intelligence (AI) integration, although concerns persist regarding the security of AI based models, particularly regarding model poisoning. Protecting these networks against cybersecurity threats, especially adversarial attacks, is vital. Hence, effective mitigation techniques and secure solutions employing AI-based methods are crucial. This study delves into the pivotal aspects of stability and sensitivity in AI-driven models tailored for NextG networks, focusing on Deep learning (DL) based channel estimation models. Utilizing data from MATLAB's 5G toolbox, we scrutinize these models' susceptibility to adversarial attacks and evaluate the efficacy of defensive distillation techniques in mitigating vulnerabilities. The research highlights the urgent necessity to fortify DL-based models against potential threats within NextG networks. Through a comprehensive evaluation of defensive distillation strategies, we showcase their ability to bolster both the stability and sensitivity of channel estimation models. By addressing these critical aspects, the study aims to enhance the resilience of AI-driven solutions within NextG environments, strengthening defenses against cybersecurity risks and safeguarding network integrity. Ultimately, the findings underscore the importance of prioritizing sensitivity and stability in the development and deployment of AI-based models for NextG networks. Through proactive measures and advanced mitigation methods, efforts are made to mitigate the risks posed by adversarial attacks and foster a more secure and resilient network infrastructure for the future.

Optimized NOMA System Using Hybrid Coding and Deep Learning-Based Channel Estimation

Optimized NOMA System Using Hybrid Coding and Deep Learning-Based Channel Estimation

Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO Systems

Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO Systems

Deep learning-based channel estimation for wireless ultraviolet MIMO communication systems

Deep learning-based channel estimation for wireless ultraviolet MIMO communication systems

Deep learning based channel estimation method for mine OFDM system

In this paper, we present a channel estimation approach based on deep learning to solve the problem that the orthogonal frequency division multiplexing (OFDM) system channel estimation algorithm cannot accurately obtain the channel state information in the complex environment of the mine, resulting in system performance degradation. First, LS channel estimation matrix is considered as a low-resolution image and the actual channel state information is considered as a high-resolution image. Then the optimization of the LS channel estimation matrix is achieved by the FSRCNN image super-resolution algorithm. We validate the effectiveness of the proposed algorithm by conducting experiments in different channel environments, different number of pilots, and mismatched signal-to-noise ratio scenarios. The simulation results show that the proposed scheme is much better than the traditional LS channel estimation method and the DFT-LS channel estimation method, and the accuracy of the proposed scheme approaches that of the MMSE channel estimation method when the number of pilots is low.

Cluster-aware channel estimation with deep learning method in deep-water acoustic communications.

In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.

Deep Learning-Based Channel Estimation for Wideband Hybrid MmWave Massive MIMO

Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and energy consumption. However, channel estimation in the context of HAD is challenging due to only limited radio frequency (RF) chains at transceivers. Although various compressive sensing (CS) algorithms have been developed to solve this problem by exploiting inherent channel sparsity and sparsity structures, practical effects, such as power leakage and beam squint, can still make the real channel features deviate from the assumed models and result in performance degradation. Besides, the high complexity of CS algorithms caused by a large number of iterations hinders their applications in practice. To tackle these issues, we develop a deep learning (DL)-based channel estimation approach where the sparse Bayesian learning (SBL) algorithm is unfolded into a deep neural network (DNN). In each SBL layer, Gaussian variance parameters of the sparse angular domain channel are updated by a tailored DNN, which is able to capture complicated channel sparsity structures in various domains effectively and efficiently. The measurement matrix is jointly optimized for performance improvement. Then, the proposed approach is extended to the multi-block case where channel correlation in time is further exploited to adaptively predict the measurement matrix and facilitate the update of variance parameters. Simulation results show that the proposed approaches outperform existing approaches in terms of both performance and complexity.

Deep Learning Based Channel Estimation for MIMO-OFDM System with Modified ResNet Model

Objectives: The effectiveness of wireless communication systems is significantly influenced by channel estimation. In order to accurately estimate the Channel Impulse Response (CIR) of the channel under varied circumstances, channel estimation is a crucial procedure in the functioning of MIMO-OFDM (Multi-Input and Multi-Output – Orthogonal Frequency Division Multiplexing) systems. Methods: The proposed Deep Learning(DL) based Convolutional Neural Network (CNN) with modified ResNet architecture channel estimation method improves the Bit Error Rate(BER) and Mean Square Error(MSE) performance compared to conventional channel estimation methods. We have compared the proposed CNN method with the Least square (LS), Minimum Mean Square Error(MMSE) and Deep Neural Network(DNN) based channel estimation methods. The results have been discussed by using BER and MSE versus SNR graphs. The simulation results are being performed on the MATLAB platform of the R2021b version. Findings: The DL-based MIMO-OFDM channel estimation can achieve better performance over multipath fading channels if Channel coefficients are perfectly estimated at the receiver. The simulation test is carried out in different test conditions by considering the different number of transmitter and receiver antennas with respect to different QAM modulation order values. Novelty: The DL-based modified ResNet architecture comprises a set of layers modified to estimate the optimal channel parameters. For achieving a great reduction of MSE and BER compared to conventional channel estimation methods, the layers of the ResNet– model are modified. Keywords: Channel estimation; MIMOOFDM system; Deep learning; Neural network; ResNet

Multi-Strategy Enhanced Harris Hawks Optimization for Global Optimization and Deep Learning-Based Channel Estimation Problems

Harris Hawks Optimization (HHO) simulates the cooperative hunting behavior of Harris hawks and it has the advantages of fewer control parameters, simple principles, and excellent exploitation ability. However, HHO also has the disadvantages of slow convergence and easy falling into local optimality. Aiming at the above shortcomings, this paper proposes a Multi-strategy Enhanced Harris Hawks Optimization (MEHHO). Firstly, the map-compass operator and Cauchy mutation strategy are used to increase the population diversity and improve the ability of the algorithm to jump out of the local optimal. Secondly, a spiral motion strategy is introduced to improve the exploration phase to enhance search efficiency. Finally, the convergence speed and accuracy of the algorithm are improved by greedy selection to fully retain the dominant individuals. The global search capability of the proposed MEHHO is verified by 28 benchmark test functions, and then the parameters of the deep learning network used for channel estimation are optimized by using the MEHHO to verify the practicability of the MEHHO. Experimental results show that the proposed MEHHO has more advantages in solving global optimization problems and improving the accuracy of the channel estimation method based on deep learning.

Deep learning-based channel estimation in MIMO system for pilot decontamination

Deep learning-based channel estimation in MIMO system for pilot decontamination

Deep learning-based channel estimation in MIMO system for pilot decontamination

Deep learning-based channel estimation in MIMO system for pilot decontamination

Deep learning based channel estimation optimization in VLC systems

This paper aims to improve the channel estimation (CE) in the indoor visible light communication system. The proposal of this paper deals with a system that depends on a comparison between Deep Neural Network (DNN), Yolo v3, and Kalman filter (KF) algorithm, for two optical modulation techniques; asymmetrically clipped optical-orthogonal frequency-division multiplexing (ACO-OFDM) and direct current optical-orthogonal frequency division multiplexing (DCO-OFDM). The CE can be evaluated by the error rates in the received bits, where increased error means a performance decrease of the system and vice versa. Receiving less errors at the receiver indicates improved CE and system performance. Hence, the main aim of our work is to decrease the error rate by using different estimators. Furthermore, we apply automatic hyper-parameter approach and Bayesian optimization, to Yolo v3 model to improve the system performance and reduce the positioning error. The metric parameter of bit error rate (BER) aims to determine the improvement ratio in different systems. The model in this paper is based on training with OFDM samples of signal with labels which are received and are corresponding to the signals of OFDM. At a BER = 10−3 with DCO-OFDM, the DNN outperforms KF with 1.7 dB (8.09%) at the bit energy per noise (E_{b} {/}N_{o} ) axis. Also, for ACO-OFDM at BER = 10−3, the DNN achieves better results than KF by about 1.9 dB (11.8%) at the (E_{b} {/}N_{o} ){ } axis. For different values of M in QAM, the DNN outperforms KF for ACO-OFDM by average improvement of ~ 1.2 dB (~ 13%).

Deep Learning Based Channel Estimation for OFDM Systems With Doubly Selective Channel

In wireless communication orthogonal frequency division multiplexing (OFDM) systems with high mobility, channel estimation becomes challenging due to the double selectivity of channels. To solve this problem, a deep learning based method which fully considers the channel features and noise effects is proposed in this letter. In the pilot-based channel estimation scheme, a compression-and-reconstruction channel estimation network (CRCENet) is designed to improve the channel estimation performance, which fuses not only the low-level features of the rapid changes between adjacent subcarriers in the short term and the high-level features of the overall channel changes under large time spans in the backbone, but also the features using the attention mechanism in the up-sampling branches. Meanwhile, considering that the samples with different signal-to-noise ratios (SNRs) have different credibility, loss weights based on credibility are assigned to data with different SNRs during network training, to further improve the estimation performance of the network. The experimental results revealed that CRCENet performed better than the traditional least squares estimation algorithm and state-of-the-art learning networks.

Enhanced deep learning based channel estimation for indoor VLC systems

This paper aims to improve the channel estimation (CE) in the indoor visible light communication (VLC) system. We propose a system that depends on a comparison between Deep Neural Networks (DNN) and Kalman Filter (KF) algorithm for two optical modulation techniques; asymmetrically clipped optical-orthogonal frequency-division multiplexing (ACO-OFDM) and direct current optical-orthogonal frequency division multiplexing (DCO-OFDM). The channel estimation can be evaluated by changing the rate of errors in the received bits, where increased error means a performance decrease of the system and vice versa. Receiving less errors at the receiver indicates improved channel estimation and system performance. Thus, the main aim of our proposal is decreasing the error rate by using different estimators. Using the simulation results with the metric parameter of bit error rate (BER) aims to determine the improvement ratio between different systems. The proposed model is trained with OFDM signal samples with labels, where the labels represent the received signal after applying OFDM travelling across the medium. At a BER = 10–3 with DCO-OFDM, the DNN outperforms KF with 1.6 dB (7.6%) at the bit energy per noise ({{varvec{E}}}_{{varvec{b}}}/{{varvec{N}}}_{{varvec{o}}}) axis. Also, for ACO-OFDM at BER = 10–3, the DNN achieves better results than KF by about 1.3 dB (8.12%) at the ({{varvec{E}}}_{{varvec{b}}}/{{varvec{N}}}_{{varvec{o}}}). At different values of M in QAM, the DNN outperforms KF for ACO-OFDM by average improvement of ~ 1 dB (~ 11.5%).

Deep Learning-Based Channel Estimation for Massive MIMO With Hybrid Transceivers

Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algorithms usually suffer from unsatisfactory performance and high computational complexity. In this paper, we propose a novel deep learning (DL) based framework for uplink channel estimation in HAD massive MIMO systems. To better exploit the sparsity structure of channels in the angular domain, a novel angular space segmentation method is proposed, where the entire angular space is segmented into many small regions and a dedicated neural network is trained offline for each region. During online testing, the most suitable network is selected based on the information from the global positioning system. Inside each neural network, the region-specific measurement matrix and channel estimator are jointly optimized, which not only improves the signal measurement efficiency, but also enhances the channel estimation capability. Simulation results show that the proposed approach significantly outperforms the state-of-the-art CS algorithms in terms of estimation performance and computational complexity.

Deep Learning-Based Channel Estimation for mmWave Massive MIMO Systems in Mixed-ADC Architecture.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems can significantly reduce the number of radio frequency (RF) chains by using lens antenna arrays, because it is usually the case that the number of RF chains is often much smaller than the number of antennas, so channel estimation becomes very challenging in practical wireless communication. In this paper, we investigated channel estimation for mmWave massive MIMO system with lens antenna array, in which we use a mixed (low/high) resolution analog-to-digital converter (ADC) architecture to trade-off the power consumption and performance of the system. Specifically, most antennas are equipped with low-resolution ADC and the rest of the antennas use high-resolution ADC. By utilizing the sparsity of the mmWave channel, the beamspace channel estimation can be expressed as a sparse signal recovery problem, and the channel can be recovered by the algorithm based on compressed sensing. We compare the traditional channel estimation scheme with the deep learning channel-estimation scheme, which has a better advantage, such as that the estimation scheme based on deep neural network is significantly better than the traditional channel-estimation algorithm.

Deep Learning Based Over-the-Air Channel Estimation Using a ZYNQ SDR Platform

Deep learning based channel estimation techniques have recently found an overwhelming interest owing to data-driven learning-based adaptability compared to conventional estimation techniques which rely on model-based approach. This paper exploits the availability of low cost software defined radio (SDR) devices to implement and test over-the-air (OTA) deep learning driven channel estimation solutions in realistic settings for 5G and beyond wireless communications. We have designed and implemented a ZYNQ SDR-based deep learning driven channel estimation platform which utilises real-world 5G new radio (NR) signals to develop and test the performance of a deep learning solution for wireless channel estimators. To this end, we have considered the time-frequency response of an OTA communication channel for single-input-single-output (SISO) and single-input-multiple-output (SIMO) networks under various line-of-sight and non-line-of-sight scenarios as a two-dimensional image. We have utilised convolutional deep learning technique for channel estimation. The main purpose is to use OTA samples obtained via the SDR platform to determine the unknown values of the channel response using known values at the pilot locations and evaluate performance compared to conventional technique. Our results show that the performance of the deep learning channel estimator using OTA data is as good as that of conventional methods with the flexibility to adapt based on data analytics of the time-varying nature of the channel.

A Survey on Deep Learning Based Channel Estimation in Doubly Dispersive Environments

Wireless communications systems are impacted by multi-path fading and Doppler shift in dynamic environments, where the channel becomes doubly-dispersive and its estimation becomes an arduous task. Only a few pilots are used for channel estimation in conventional approaches to preserve high data rate transmission. Consequently, such estimators experience a significant performance degradation in high mobility scenarios. Recently, deep learning has been employed for doubly-dispersive channel estimation due to its low-complexity, robustness, and good generalization ability. Against this backdrop, the current paper presents a comprehensive survey on channel estimation techniques based on deep learning by deeply investigating different methods. The study also provides extensive experimental simulations followed by a computational complexity analysis. After considering different parameters such as modulation order, mobility, frame length, and deep learning architecture, the performance of the studied estimators is evaluated in several mobility scenarios. In addition, the source codes are made available online in order to make the results reproducible.

Knowledge Distillation-Aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems With Finite-Rate Feedback

We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN). Precoder selection is handled by a transmitter DNN. To emulate the traditional channel quantization, a binarization layer is adopted at each receiver DNN, and the binarization layer is also used to enable end-to-end learning. However, this can lead to inaccurate gradients, which can trap the receiver DNNs at a poor local minimum during training. To address this, we consider knowledge distillation, in which the existing DNNs are jointly trained with an auxiliary transmitter DNN. The use of an auxiliary DNN as a teacher network allows the receiver DNNs to additionally exploit lossless gradients, which is useful in avoiding a poor local minimum. For the same number of feedback bits, our DNN-based precoding scheme can achieve a higher downlink rate compared to conventional linear precoding with codebook-based limited feedback.

Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems.

Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.