Channel Estimation Research Articles

Cell-Free Bistatic Backscatter Communication: Channel Estimation, Optimization, and Performance Analysis

Cell-Free Bistatic Backscatter Communication: Channel Estimation, Optimization, and Performance Analysis

Channel Estimation and Signal Detection for NLOS Ultraviolet Scattering Communication With Space Division Multiple Access

Channel Estimation and Signal Detection for NLOS Ultraviolet Scattering Communication With Space Division Multiple Access

Hierarchical MTC User Activity Detection and Channel Estimation With Unknown Spatial Covariance

Hierarchical MTC User Activity Detection and Channel Estimation With Unknown Spatial Covariance

The Inter-channel Interference Cancellation Algorithm for Wireless Magnetic Induction MIMO Communication System Based on Zero-forcing Precoding

Abstract The wireless magnetic induction MIMO system can enhance the data transmission rate in near-field magnetic induction communication, but it also suffers from severe inter-channel interference issues. This paper introduces precoding techniques into the wireless magnetic induction MIMO system, employing the zero-forcing precoding algorithm to eliminate interference between different channels. Firstly, leveraging the characteristic that the channel state of magnetic induction transmit-receive coils is determined solely by the physical parameters of the coils, channel estimation is performed to obtain the channel gain matrix for wireless magnetic induction communication. Subsequently, the obtained channel matrix is subjected to zero-forcing precoding to eliminate interference caused by coupling between different channels. Simulation results indicate that the data transmission rate of the wireless magnetic induction MIMO system is significantly higher after applying zero-forcing precoding compared to the rate without it. Additionally, the system’s data transmission rate decreases as the distance between the transmitting and receiving coils increases.

Channel Estimation in Integrated Radar and Communication Systems with Power Amplifier Distortion

Channel Estimation in Integrated Radar and Communication Systems with Power Amplifier Distortion

Diagonally Reconstructed Channel Estimation for MIMO-AFDM With Inter-Doppler Interference in Doubly Selective Channels

Diagonally Reconstructed Channel Estimation for MIMO-AFDM With Inter-Doppler Interference in Doubly Selective Channels

Decision-Directed Hybrid RIS Channel Estimation With Minimal Pilot Overhead

Decision-Directed Hybrid RIS Channel Estimation With Minimal Pilot Overhead

Channel Estimation for Deep Space Communications Under the Effect of Solar Scintillation

AbstractDuring probe‐to‐Earth superior conjunction, deep space communication channels will suffer from solar scintillation, leading to amplitude attenuation of received signals. This study aims to obtain the channel state information (CSI) on deep space channels affected by solar scintillation. Classical least squares (LS) and minimum mean squared error (MMSE) methods are adopted to perform channel estimation and compensate for the channel fading. Simulation results indicate that under the effect of solar scintillation, performing channel estimation technology can significantly improve bit error rate (BER) performance compared to systems without CSI, and the MMSE algorithm outperforms the LS for both BER and normalized mean squared error (NMSE). In addition, we also find that pilot density, geometric parameters, and the outer scale of solar wind turbulence has great influence on the estimation performance.

Non-coherent short-packet communications: Novel z-domain user multiplexing

In the evolution of fifth generation (5G) and beyond wireless communication systems, non-coherent (NC) short packet communication (SPC) is crucial for achieving ultra-reliable low-latency communication (URLLC). Frame design, latency, and reliability are some of the challenges associated with short-packet communication. Recently, to address these challenges, a novel modulation scheme known as modulation on conjugate-reciprocal zeros (MOCZ) has been proposed. MOCZ modulates information on conjugate reciprocal zeros in the z-domain, thereby eliminating the need for channel estimation and providing a robust solution for NC communication. However, in multi-user MOCZ (MU-MOCZ) scheme, adding guard intervals to each short packet to mitigate channel impact remains an issue, as it increases the transmission time and consequently reduces efficiency. To address the aforementioned problem, this paper introduces a novel frame design approach called z-domain user multiplexing MOCZ (ZDUM-MOCZ or ZDM-MOCZ). Unlike traditional time division multiplexing (TDM), which serves users consecutively in the time domain, this method multiplexes users in the z-domain. In this approach, each user is allocated a specific set of zeros in the z-domain, which collectively form a unique sequence in the time domain. The findings illustrate the potential for reduced latency in downlink transmission, highlighting the benefits of this novel methodology over conventional MU-MOCZ method. The proposed ZDUM-MOCZ scheme not only addresses the existing issues in the frame design of the MU-MOCZ scheme but also facilitates more efficient and reliable short packet communication in 5G and beyond wireless systems.

Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems With Hybrid Combining

Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems With Hybrid Combining

Successive interference cancellation-based channel estimation for self-interference cancellation in in-band full-duplex systems

Successive interference cancellation-based channel estimation for self-interference cancellation in in-band full-duplex systems

Manifold Optimization Empowered Two-Timescale Channel Estimation for RIS-Assisted Systems

Manifold Optimization Empowered Two-Timescale Channel Estimation for RIS-Assisted Systems

Signal Processing based Iterative Channel Estimation Techniques for Noma-OFDM Systems

Signal Processing based Iterative Channel Estimation Techniques for Noma-OFDM Systems

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

Encrypted-OFDM: A secured wireless waveform

Wireless communication has been a broadcast system since its inception, which violates security and privacy issues at the physical layer between the intended transmit and receive pairs. Consequently, it is essential to secure the wireless signal such that only the intended receiver can realize the signal properties. In this paper, we propose Encrypted-OFDM, a new waveform, where the signal structure is altered to encrypt the waveform with a shared secret key. We achieve the signal level security by modifying the OFDM signal in time-domain, thus erasing the OFDM properties and obfuscating the signal properties to an eavesdropper. We present a two-stage encryption algorithm to increase the robustness of the transmitted waveform and achieve a high level of secrecy, even when low entropy keys are used. We also introduce a novel channel estimation algorithm by removing the pilots, so that only the intended receiver can estimate the channel correctly. Furthermore, we perform both secrecy and error analysis for the transmitted and received Encrypted-OFDM waveform. Extensive simulation and over-the-air experiments show that the performance of Encrypted-OFDM is comparable to legacy OFDM, and the SNR penalty due to the secured waveform varies between ≈ 1–4 dB. In all these scenarios, Encrypted-OFDM remains unrecognized at the eavesdropper.

A Deep Learning-based Approach for Channel Estimation in Multi-access Multi-antenna Systems

This paper studies estimating the channel state information at the end of receiver (CSIR) for multiple transmitters communicating with only one receiver so that the latter can decode the incoming signal more efficiently. The transmitters and the receiver are all equipped with multi-antennas and using orthogonal space-time block codes (OSTBC). An algorithm is developed based on deep learning for estimating multi-user multiple-input multiple-output (MU-MIMO) channels. The algorithm could estimate the CSIR using a single pilot block. The proposed convolutional neural network (CNN) architecture designed for this task begins with an input layer that accepts grayscale images, followed by six convolutional blocks for feature extraction and processing. The network concludes with a fully connected layer to output the estimated channel information. It is trained using a regression loss function to map input images to accurate channel information accurately. The performance of the proposed method is compared with classical methods like least square and subspace-based methods, including Capon and rank revealing QR (RRQR) methods. CNN achieved better performance in comparison with the reference. Computer simulations are included to validate the proposed method.

Performance Analysis of Antenna-relay Selection in CNOMA Systems under Practical Impairments

Selection strategies prove to be a valuable approach in mitigating complexity associated with antenna selection (AS) and relay selection (RS), optimizing signal transmission through a streamlined number of antennas/relays, and enhancing overall system performance. This paper offers a comprehensive analysis, deriving closed-form expressions for the outage probability (OP) and throughput in proposed scenarios that leverage the best relay selection (BRS) and transmit antenna selection (TAS) protocol for cooperative non-orthogonal multiple access (CNOMA), along with partial relay selection (PRS) and TAS protocol for CNOMA. The study extends to Rayleigh fading channels, considering practical impairments such as successful interference cancellation (SIC) error, channel estimation error (CEE), and feedback delay error. In comparing the proposed system to conventional CNOMA, our findings highlight the substantial impact of SIC, CEE, and feedback delay imperfections on the performance of both proposed scenarios. Notably, the application of BRS-based TAS protocol outperforms PRS-based TAS in terms of OP and throughput. The close alignment between analytical, asymptotic, and simulation results attests to the credibility of conducted analysis.

Adaptive Hybrid Deep Learning Based Effective Channel Estimation in MIMO-Noma for Millimeter-Wave Systems with an Enhanced Optimization Algorithm

The channel estimation is crucial in the “millimeter Wave (mmWave) Massive Multiple-Input MultipleOutput (MIMO) and Non-Orthogonal Multiple Access (NOMA)” devices. Hybrid beamforming techniques are employed nowadays to minimize the complexity and equipment price. However, the absence of digital beam forming in mmWave affects the dynamic range and accuracy of the channel estimation. Previous research has concentrated mainly on predicting narrow-band mmWave channels using deep learning networks, as the wideband channels of mmWave create a considerable range and noise issue. Accurate channel estimation in the MIMO system is challenging because of the increased number of antennas and radio frequency (RF) chains. MIMO system communications using mmWave are frequently chosen because of their massive spectrum resources. Therefore, it is essential to tackle the obstacles obtained in the standard channel estimation framework by developing a MIMO-NOMA network with the help of deep learning methods Hence, this paper proposes an efficient hybrid deep learning model for channel estimation in MIMO-NOMA for mmWave systems. At first, the channel estimation is carried out using the Adaptive Hybrid Deep Learning (AHDL) model, where it combines both Autoencoder and Recurrent Neural Network (RNN). Here, the parameters are optimized using the Improved Red-tailed Hawk Algorithm (IRHA). Later the hardware cost and system complexity are reduced by performing the hybrid beam-forming process. Numerical results show that the proposed channel estimation and pilot estimation process outperforms the state-of-the-art approaches.

Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights

By adjusting the propagation environment using reconfigurable reflecting elements, intelligent reflecting surfaces (IRSs) have become potential techniques used to improve the efficiency of wireless communication networks. In IRS-assisted communication systems, accurate channel estimation is crucial for optimizing signal transmission and achieving high spectral efficiency. As mobile data traffic continues to surge and the demand for high-capacity and low-latency wireless connectivity grows, IRSs are becoming pivotal technologies in the development of next-generation communication networks. IRSs offer the potential to revolutionize wireless propagation environments, improving network capacity and coverage, particularly in high-frequency wave scenarios where traditional signals encounter obstacles. Amidst this evolving landscape, machine learning (ML) emerges as a powerful tool to harness the full potential of IRS-assisted communication systems, particularly given the escalating computational complexity associated with deploying and operating IRSs in dynamic environments. This paper presents an overview of preliminary results for IRS-assisted communication using recurrent neural networks (RNNs). We first implement single- and double-layer LSTM, BiLSTM, and GRU techniques for an IRS-based communication system. In the next phase, we explore a hybrid approach, combining different RNN techniques, including LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, as well as their reverse configurations. These RNN algorithms were evaluated with respect to bit error rate (BER) and symbol error rate (SER) for IRS-enhanced communication. According to the experimental results, the BiLSTM double-layer model and the BiLSTM-GRU combination demonstrated the highest BER and SER accuracy compared to other approaches.

Transformer-empowered receiver design of OFDM communication systems

With deep learning, we perform channel estimation and signal detection in massive Multiple Input Multiple Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems in this paper. Specifically, we design and extend the basic framework of receivers for MIMO-OFDM systems in an end-to-end approach. A Transformer-based MIMO-OFDM receiver called TCD-Receiver is proposed, which introduces a multi-attention mechanism to learn the channel characteristics by introducing a generic and flexible Transformer network structure. The network parameters are updated based on the relationship between the received signal and the original signal, where the final signal information is obtained without explicit channel estimation and the predicted transmit bits are directly output. The experimental results show that the TCD-Receiver proposed can effectively solve the channel distortion and detect the transmitted signals compared with the traditional communication receivers, and its performance can be comparable to that of the traditional OFDM receivers, and it also has obvious advantages in combating the complex and difficult-to-model channel environment as well as the nonlinear interference factors.