Channel Estimation For Communications Research Articles

Attention-Enhanced Defensive Distillation Network for Channel Estimation in V2X mm-Wave Secure Communication

Millimeter-wave (mm-wave) technology, crucial for future networks and vehicle-to-everything (V2X) communication in intelligent transportation, offers high data rates and bandwidth but is vulnerable to adversarial attacks, like interference and eavesdropping. It is crucial to protect V2X mm-wave communication from cybersecurity attacks, as traditional security measures often fail to counter sophisticated threats and complex attacks. To tackle these difficulties, the current study introduces an attention-enhanced defensive distillation network (AEDDN) to improve robustness and accuracy in V2X mm-wave communication under adversarial attacks. The AEDDN model combines the transformer algorithm with defensive distillation, leveraging the transformer’s attention mechanism to focus on critical channel features and adapt to complex conditions. This helps mitigate adversarial examples by filtering misleading data. Defensive distillation further strengthens the model by smoothing decision boundaries, making it less sensitive to small perturbations. To evaluate and validate the AEDDN model, this study uses a publicly available dataset called 6g-channel-estimation and a proprietary dataset named MMMC, comparing the simulation results with the convolutional neural network (CNN) model. The findings from the experiments indicate that the AEDDN, especially in the complex V2X mm-wave environment, demonstrates enhanced performance.

Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications

Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications

Deep Learning-Based Joint Channel Estimation and CSI Feedback for RIS-Assisted Communications

Deep Learning-Based Joint Channel Estimation and CSI Feedback for RIS-Assisted Communications

Deep Learning based Channel Estimation and Hybrid Beamforming for 5G Massive MIMO Wireless Communications

Hybrid beamforming (BF), which divides beamforming operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave(mmWave) frequencies. This paper also introduce offline training and prediction schemes for channel estimation and hybrid beamforming. The aim of this paper is that to increase spectral efficiency over more data streams by leveraging the deep learning based LSTM network. The LSTM network is used to train the numeric values from sequence data and predict on new sequence data. The performance is evaluated under different parameters including number of data streams (1, 2, 3 and 4) with different signal-to-noise ratio (SNR) for different carrier frequencies (28GHz, 38GHz, 60GHz and 73GHz) through computer simulation using MATLAB. The simulation results verified that the proposed method can achieve higher spectral efficiency when the number of data streams increases and the value of SNR-Test increases too.

A reconstruction and recovery network-based channel estimation in high-speed railway wireless communications

The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development. Due to the considerable mobility of trains and the complex nature of the environment, the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics, which presents significant hurdles in terms of channel estimation. In addition, the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation. To address the aforementioned issues, this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network. In this method, the time-frequency response of the channel is considered as a two-dimensional image. The Fast Super-Resolution Convolution Neural Network (FSRCNN) is used to first reconstruct channel images. Next, the Denoising Convolution Neural Network (DnCNN) is applied to reduce the channel noise and improve the accuracy of channel estimation. Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method, while also exhibiting reduced algorithmic complexity.

Channel Estimation for Underwater Acoustic Communications in Impulsive Noise Environments: A Sparse, Robust, and Efficient Alternating Direction Method of Multipliers-Based Approach

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l1−l1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method.

Online machine learning-based channel estimation for underwater acoustic communications

In recent years, machine learning-assisted underwater acoustic (UWA) communication technology has been proved its promising performance in simulations and ideal training and testing environments. However, given the unique characteristics of real-world UWA communication, offline training-based methods face two major challenges: (1) The severe temporal and spatial variations result in a significant gap between offline training and actual deployment conditions. (2) The availability of high-quality labeled samples is extremely limited, making it difficult to construct an appropriate offline training sample set. To address this, we propose two online learning methods for UWA channel estimation and apply them to an OFDM communication system. The first method employs an unsupervised learning approach, where pilot signals are split and input-output pairs for model construction are formed. Thus, the received real-time data can be applied for the online training directly. The second method adjusts the loss function during the machine learning training phase to embed the learned channel within it. It allows online-sampled data to be trained sequentially by leveraging prior knowledge of transmitted information as the actual learning target. Based on the experimental results of measured underwater acoustic channels, it is demonstrated that the proposed online learning methods can approximate the near-optimal solution.

Enhancing Channel Estimation in Terrestrial Broadcast Communications Using Machine Learning

Enhancing Channel Estimation in Terrestrial Broadcast Communications Using Machine Learning

IM-Based Pilot-Assisted Channel Estimation for FTN Signaling HF Communications

IM-Based Pilot-Assisted Channel Estimation for FTN Signaling HF Communications

Beam Structured Channel Estimation for HF Skywave Massive MIMO-OFDM Communications

Beam Structured Channel Estimation for HF Skywave Massive MIMO-OFDM Communications

Uplink Channel Estimation and ICI Elimination for Cell-Free Massive MIMO High-Speed Trains Communications

Uplink Channel Estimation and ICI Elimination for Cell-Free Massive MIMO High-Speed Trains Communications

Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications

Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications

Automatic High-Performance Neural Network Construction for Channel Estimation in IRS-Aided Communications

Automatic High-Performance Neural Network Construction for Channel Estimation in IRS-Aided Communications

Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications

Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications

Parallel Attention-Based Transformer for Channel Estimation in RIS-Aided 6G Wireless Communications

Parallel Attention-Based Transformer for Channel Estimation in RIS-Aided 6G Wireless Communications

Structured Channel Estimation for RIS-Assisted THz Communications

Structured Channel Estimation for RIS-Assisted THz Communications

Retracted: Channel Estimation for Switch-Based Millimeter-Wave Communications via Atomic Norm

Retracted: Channel Estimation for Switch-Based Millimeter-Wave Communications via Atomic Norm

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed.

Progressive Channel Estimation for Single-Carrier Communications Aided by Reconfigurable Intelligent Surfaces

Progressive Channel Estimation for Single-Carrier Communications Aided by Reconfigurable Intelligent Surfaces

Machine Learning Based Channel Estimation for 5G NR-V2V Communications: Sparse Bayesian Learning and Gaussian Progress Regression

Machine Learning Based Channel Estimation for 5G NR-V2V Communications: Sparse Bayesian Learning and Gaussian Progress Regression