Channel Estimation Method Research Articles

A Multi-Resolution Channel Structure Learning Estimation Method of Geometry-Based Stochastic Model With Multi-Scene

Vehicle-to-vehicle (V2V) communication is the most typical application of the Internet of Vehicles, and it has many envisaged applications, such as driverless driving, collision warning and addressing traffic congestion. The realization of future intelligent transportation systems (ITSs) will require V2V communication technology, which in turn relies on accurate V2V channel estimation. As the possibilities that 5G technologies can be used for V2V communications are increasingly being explored, corresponding channel models used to describe the characteristics of V2V channels are also being updated, so the new channel estimation algorithms needed to be developed. Although the channel estimation method based on Convolutional Neural Network (CNN) has achieved remarkable success in communication problems in recent years, it cannot be well adapted to V2V channels under different scenarios and different modeling methods. In this article, a method is proposed for learning and estimating the channel structure in the 5G NR downlink that can adapt to the V2V channel under multi-scenes. The inherent block sparsity characteristics of the channel structure is adopted to combine the group lasso Alternating Direction Method of Multipliers (ADMM) algorithm with CNN, and residual dense network is employed to estimate and refine the V2V channel structure. Furthermore, the classifier and interpolation with pooling method are used to estimate the V2V channel structure under multi-scene and multi-resolution. Simulation results show that the performance of this method is better than other deep learning based estimation algorithms.

Estimation of bit error rate in 2×2 and 4×4 multi-input multi-output-orthogonal frequency division multiplexing systems

<span lang="EN-US">Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-to-noise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the </span><em><span lang="EN-US">i<sup>th</sup></span></em><span lang="EN-US"> transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track time-varying channels rapidly.</span>

Joint Channel Estimation and Mixed-ADCs Allocation for Massive MIMO via Deep Learning

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and power consumption grow significantly as the number of radio frequency (RF) components increases, which hampers the deployment of practical massive MIMO systems. To address this issue and further facilitate the commercialization of massive MIMO, mixed analog-to-digital converters (ADCs) architecture has been considered, where parts of conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In this paper, we first propose a deep learning-based (DL) joint pilot design and channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we devise a pilot design neural network whose weights directly represent the optimized pilots, and develop a Runge-Kutta model-driven densely connected network as the channel estimator. Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy. Moreover, we adopt an autoencoder-inspired end-to-end architecture to jointly optimize the pilot design, channel estimation and mixed-ADCs allocation networks. Simulation results show that the proposed DL-based methods have advantages over the traditional channel estimators as well as the state-of-the-art networks.

Efficient Channel Estimation for RIS-Aided MIMO Communications With Unitary Approximate Message Passing

Reconfigurable intelligent surface (RIS) is very promising for wireless networks to achieve high energy efficiency, extended coverage, improved capacity, massive connectivity, etc. To unleash the full potentials of RIS-aided communications, acquiring accurate channel state information is crucial, which however is very challenging. For RIS-aided multiple-input and multiple-output (MIMO) communications, the existing channel estimation methods have computational complexity growing rapidly with the number of RIS units <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> (e.g., in the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N^{2}$ </tex-math></inline-formula> or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N^{3}$ </tex-math></inline-formula> ) and/or have special requirements on the matrices involved (e.g., the matrices need to be sparse for algorithm convergence to achieve satisfactory performance), which hinder their applications. In this work, instead of using the conventional signal model in the literature, we derive a new signal model obtained through proper vectorization and reduction operations. Then, leveraging the unitary approximate message passing (UAMP), we develop a more efficient channel estimator that has complexity linear with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> and does not have special requirements on the relevant matrices, thanks to the robustness of UAMP. These facilitate the applications of the proposed algorithm to a general RIS-aided MIMO system with a larger <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> . Moreover, extensive numerical results show that the proposed estimator delivers much better performance and/or requires significantly less number of training symbols, thereby leading to notable reductions in both training overhead and latency.

Channel estimation using hybrid optimizer based recurrent neural network long short term memory for MIMO communications in 5G network

In the fifth-generation (5G) networks, multiple input multiple-output (MIMO) systems are further developed to enhance transmission reliability. However, channel estimation is one of the major challenges which needs focus for improved data transmission in MIMO. Although efficient estimation techniques have been recently proposed, estimation accuracy needs to be upgraded further. Hence, an optimized Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) network is presented in this paper for channel estimation. At first, history of channel responses of pilot block is collected or estimated using Least Square (LS) channel estimation method. Using these collected channel responses, the proposed RNN-LSTM is trained where weight parameters are chosen optimally using hybrid Particle Swarm Optimization (PSO)-Adam optimizer. Using the trained PSO-Adam optimizer based RNN-LSTM, the current channel response is predicted or estimated. The performance of the proposed channel estimation scheme is analysed by varying pilot sequence length and number of antennas to evaluate the metrics Bit Error Rate (BER) and Mean Square Error (MSE). Complexity analysis of the proposed scheme is compared with standard estimators like LS and Minimum Mean-Square Error (MMSE).

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

Low-Complexity Adaptive Background Compensation of Coherent Optical Transmitters

Next generation optical communications will demand coherent transceivers with data rates in the 1.6 Terabit-per-second range and beyond, requiring both high symbol rate and large constellations. In this scenario coherent systems become increasingly sensitive to transmitter imperfections such as In-phase/Quadrature time skew, amplitude and phase errors, bandwidth limitations and mismatches, etc. These impairments must be compensated to achieve satisfactory performance. In this work, we propose a novel and efficient technique for the precompensation of such impairments, requiring minimum additional hardware in current transmitter designs. This scheme is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive</i> , as it readjusts itself to cope with time and environment dependent impairments, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">background</i> , as interruptions of normal operation are not required for the continuous recalibration of the system. Naturally, such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-field</i> adaptation demands the real-time system identification of the coherent optical transmitter, which is performed from the sole information provided by a low bandwidth photodiode/analog-to-digital converter chain at the transmitter output. This fact represents a meaningful advance with respect to the usual channel estimation methods, which require the full bandwidth coherent demodulation of the optical output. A theoretical foundation for the proposed scheme is presented. Numerical simulations of a realistic coherent optical transmitter show excellent results for high symbol rate modulation formats.

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.

An improved least squares (LS) channel estimation method based on CNN for OFDM systems

&lt;abstract&gt;&lt;p&gt;Least squares (LS) is a commonly used pilot-based channel estimation algorithm in orthogonal frequency division multiplexing (OFDM) systems. This algorithm is simple and easy to implement because of its low computation complexity. However, it has poor performance, especially at low signal-to-noise ratio (SNR). To solve this problem, an improved LS channel estimation method based on convolutional neural network (CNN) is proposed on the basis of analyzing the traditional LS channel estimation methods. A channel estimation compensated network is designed based on CNN, which can solve the problem of performance degradation of the mean square error (MSE) through the online and offline modules. By designing the input-output relations, training data set, and testing data set, a CNN network is iteratively trained to learn the relevant features of the channels, so that the traditional LS estimation value can be corrected to improve the accuracy. Simulation results show that the proposed method can achieve better performance in bit error rate (BER) and MSE, compared with the traditional channel estimation methods.&lt;/p&gt;&lt;/abstract&gt;

OTFS-Aided RIS-Assisted SAGIN Systems Outperform Their OFDM Counterparts in Doubly Selective High-Doppler Scenarios

The recently developed reconfigurable intelligent surfaces (RISs) are capable of improving the coverage of space–air–ground integrated networks (SAGINs), where the signals can be reflected in the desired direction without relying on power-thirsty radio-frequency (RF) chains. However, in the face of the substantially increased Doppler frequency, the classic orthogonal frequency-division multiplexing (OFDM) becomes inadequate in supporting RIS for the following reasons. First, the detrimental doubly selective fading leads to intersymbol interference (ISI) and intercarrier interference (ICI), which result in error floors for OFDM operating in the time–frequency (TF) domain. Second, it is far from trivial to configure RIS based on the time-varying fading channels. Third, the interpolation-based TF-domain channel estimation methods become impractical for the high-Doppler and high-dimensional RIS systems. Against this background, in this article, we propose the powerful 2-D orthogonal time–frequency space (OTFS) modulation for RIS-aided SAGINs, which transforms the time-varying fading encountered in the TF-domain to the time-invariant fading in the delay-Doppler (DD) domain. More explicitly, first, for the first time in the literature, we devise the DD-domain channel model of RIS-assisted SAGINs in the face of doubly selective fading. Second, in order to facilitate the RIS configuration in the DD-domain, we propose to create “virtual” Doppler frequencies that guide the phase changes at the RIS, even though the RIS phase rotations do not suffer from Doppler effects. Third, we conceive an attractive DD-domain RIS channel estimation method that can support both OFDM and OTFS, where the TF-domain interpolation is eliminated. Our simulation results demonstrate that the proposed DD-domain RIS configuration and channel estimation methods for both OFDM and OTFS are capable of mitigating the error floors encountered in the TF-domain. Furthermore, our simulation results confirm that OTFS-based RIS-assisted SAGIN systems are capable of outperforming their OFDM counterparts and exhibit excellent performance across a wide range of SAGIN channel parameters including the Ricean K factor, Doppler frequency, delay spread, coverage distance, and carrier frequency.

Data Aided Channel Estimation for MIMO-OFDM Wireless Systems Using Reliable Carriers

Wireless channel estimation is one of the challenging problems in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless systems. The MIMO-OFDM exploits the spatial resources and increases the reliability and capacity of wireless systems. However, the performance of these systems depends on accurate channel estimation since the receivers require perfect Channel State Information (CSI) for coherent signal detection. Thus, wireless channel estimation is a necessary component of OFDM systems. In this article, we have proposed an algorithm for MIMO-OFDM systems that combines pilot symbols with reliable data symbols for channel estimation. The reliable data symbols serve as virtual pilots and enhance the spectral efficiency. The proposed Data Aided Channel Estimation (DACE) algorithm eliminates the requirement of any additional resources such as excessive number of training symbols to attain the desired performance. Also, it outperforms the traditional Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) methods for channel estimation in terms of Mean Square Error (MSE) and Bit Error Rate (BER) performance of the system.

Range-Division Multiplexing for MIMO OFDM Joint Radar and Communications

An orthogonal frequency-division multiplexing (OFDM) waveform enables simultaneous radar sensing and communications. In the multiple-input multiple-output (MIMO) case with several transmit (Tx) antennas, multiplexing the transmit signals is usually implemented using equidistant subcarrier interleaving (ESI), thus allowing the separation of the transmit signals in frequency domain. In this work, a multiplexing technique denoted as range-division multiplexing (RDMult) is analyzed, which allows separating signals radiated by different Tx antennas along the range axis of a range-Doppler map. This work also investigates the effects of RDMult on the communication task. It turns out that RDMult heavily distorts the channel statistics by introducing a repetitive pattern of deep fading holes, which severely distort the transmission of data. To combat this issue, this work proposes a communication system especially designed for RDMult, including methods for data estimation, channel estimation and synchronization. Simulations confirm the effectiveness of these concepts in terms of bit error ratio (BER) performance.

LSTM projected layer neural network-based signal estimation and channel state estimator for OFDM wireless communication systems

&lt;abstract&gt;&lt;p&gt;Advanced wireless communication technologies, such as 5G, are faced with significant challenges in accurately estimating the transmitted signal and characterizing the channel. One of the major obstacles is the interference caused by the delay spread, which results from receiving multiple signal copies through different paths. To mitigate this issue, the orthogonal frequency division modulation (OFDM) technique is often employed. Efficient signal detection and optimal channel estimation are crucial for enhancing the performance of multi-carrier wireless communication systems. To this end, this paper proposes a Long Short Term Memory-Projected Layer (LSTM-PL) deep neural network(DNN) based channel estimator to detect received OFDM signal. The results show that the LSTM-PL algorithm outperforms traditional methods such as Least Squares(LS), Minimum Mean Square Error (MMSE) and other LSTM deep learning channel estimation methods like Long Short Term Memory(LSTM)-DNN and Bidirectional-LSTM(Bi-LSTM)-DNN, as evidenced by Symbol-Error Rate (SER) outcomes.&lt;/p&gt;&lt;/abstract&gt;

Procena kanala dubokog učenja za 5G bežične komunikacije

Introduction/purpose: In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have demonstrated remarkable performance in 5G communication systems by significantly improving the accuracy of channel estimation compared to conventional methods. This article aims to provide a comprehensive review of the existing literature on CNN-based channel estimation techniques, as well as to enhance the state-of-the-art CNN-based channel estimation methods by proposing a novel method called VDSR (Very Deep Super Resolution), inspired by Image Super-Resolution techniques. Methods: To evaluate the effectiveness of various approaches, we conduct a comprehensive comparison considering different scenarios, including low Signal-to-Noise Ratio (SNR) and high SNR, as well as Lineof-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios. Through this comparative analysis, we assess the performance of the existing methods and highlight the advantages offered by the proposed VDSR-based technique. Results: Our findings reveal a significant potential of CNN-based channel estimation in 5G communication systems, with the VDSR method demonstrating a consistent performance across all scenarios. This research contributes to the advancement of channel estimation techniques in 5G networks, paving the way for enhanced wireless communication systems with improved reliability. Conclusion: The VDSR architecture demonstrates remarkable adaptability to different types of channels, which results in achieving requested performances for all analyzed SNR values.

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

Millimeter wave (mmWave) communications are widely preferred due to the rich bandwidth and potentially huge spectrum resources. Nowadays, mixed-ADC architecture combined with mmWave massive MIMO has become a communication mainstream, which can effectively solve the issue of high total power consumption and cost of base station (BS) circuits. However, the channel estimation problem for mmWave massive MIMO systems with mixed-ADC architecture has not been studied yet. In this paper, we develop the sparse channel estimation method on this framework. Specifically, by exploiting the sparsity of mmWave channels, the beamspace channel estimation problem can be transformed into a sparse matrix recovery problem, the channel parameters are recovered using compressive sensing (CS) techniques. Simulation results show that the algorithms quantized by the mixed-ADC outperforms the low-resolution ADC, and the best performance can be achieved when the low-resolution ADC in the mixed-ADC architecture reaches five-bit.

Efficient Image Transmission Over OFDM Channel with Energy Conservation and LS Channel Estimation with Various Modulation Schemes

To send data like text, images, music, and videos, packetization procedures are commonly used in communication apps. Just a portion of carriers in OFDM systems may be suitable for transmitting images due to channel fading. To reduce high channel errors at the receiver, the transmitter can proactively map descriptions onto good subcarriers and delete remaining descriptions if it has access to channel status information. In this study, discrete wavelet processing as an energy-saving approach for transmitting compressed image frames via OFDM channels. By utilizing one-bit channel status information at the transmitter, the descriptions are sorted and allocated to high-quality channels in a descending order of importance. To conserve energy, faulty sub-channel descriptions are removed at the transmitter. In this project, image transmission across OFDM channels is packetized to enable efficient data transfer while minimizing energy consumption. We also use the LS Channel estimation method to get a more accurate channel estimate with different modulation schemes and SNR. We obtained energy savings of up to 38% and improved channel estimation with various modulation schemes by utilizing MATLAB simulations to test on a variety of modulation schemes and channel SNR ratios.

Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re‘nyi entropy function

This study focuses on channel estimation for reconfigurable intelligent surface (RIS)-assisted mmWave systems, in which the RIS is used to facilitate base-to-user data transfer. For beamforming to work with active and passive elements, a large-size cascade channel matrix should always be known. Low training costs are achieved by using the mmWave channels’ inherent sparsity. The research provides a unique compressive sensing-based channel estimation approach for reducing pilot overhead issues to a minimum. The proposed technique estimates channel data signals in a downlink for RIS-assisted mmWave systems. The mmWave systems often have a sparse distribution of signal sources due to the spatial correlations of the domains. This distribution pattern makes it possible to use compressive sensing methods to resolve the channel estimation issue. In order to decrease the pilot overhead, which is necessary to predict the channel, the proposed method extends the Re‘nyi entropy function as the sparsity-promoting regularizer. In contrast to conventional compressive sensing techniques, which necessitate an initial knowledge of the signal’s sparsity level, the presented method employs sparsity adaptive matching pursuit (SAMP) techniques to gradually determine the signal’s sparsity level. Furthermore, it introduces a threshold parameter based on the signal’s energy level to eliminate the sparsity level requirement. Extensive simulations show that the presented channel estimation approach surpasses the traditional OMP-based channel estimation methods in terms of normalized mean square error performance. In addition, the computational cost of channel estimation is lowered. Based on the simulations, our approach can estimate the channel well while reducing training overhead by a large amount.

A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems

In the design of unmanned aerial vehicle (UAV) communication systems, orthogonal frequency division multiplexing (OFDM) is a commonly used communication technology. An efficient channel estimation and equalization algorithm is required to recover the amplitude, phase, and frequency of the signal in OFDM systems. At present, the more precise channel estimation method is based on the pilot. However, its spectrum utilization is relatively low. Therefore, this paper presents the design of a new pilot based on the LS channel estimation, which extends the role of the traditional pilot and improves the utilization of the spectrum. In addition to the channel estimation and equalization, the new pilot can also be utilized for ranging. Simulation results show that the proposed scheme can achieve both channel estimation and communication ranging functions by using the new pilot, and it outperforms the conventional method in channel estimation performance. The proposed method can complete ranging when the bit error rate (BER) is above 0 dB. Moreover, compared with the traditional channel estimation, it reduces the requirement for SNR by about 1 dB under the same BER.

Spectral Efficiency Analysis of Cell-Free Distributed Massive MIMO Systems With Imperfect Covariance Matrix

In this paper, the impacts of imperfect channel covariance matrix on the spectral efficiency (SE) of cell-free distributed massive multiple-input multiple-output (MIMO) systems are analyzed. We propose to estimate the channel covariance matrix by alternately using the assigned pilots and their phase-shifted pilots in different coherent blocks, which improves the accuracy of channel estimation with imperfect covariance matrix and reduces pilot overhead. Under this scheme, the closed-form expressions of SE with maximum ratio combination (MRC) and zero-forcing (ZF) receivers are derived, which enables us to select key parameters for better system performance. Simulation results verify the effectiveness of the proposed channel estimation method and the accuracy of the derived closed-form expressions. When more coherent blocks are used to estimate the covariance matrix, we can get better system performance. Moreover, some insightful conclusions are arrived at from the SE comparisons between different receiving schemes (ZF and MRC) and different pilot allocation schemes (orthogonal pilot and pilot reuse).

Pattern search and compressed sensing based phase noise and channel estimation for mmWave massive MIMO-OFDM systems

Phase noise (PN) can seriously affect the performance of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, especially when orthogonal frequency division multiplexing (OFDM) is employed. This paper studies the PN effect on mmWave massive MIMO-OFDM systems, and proposes a time variant training mode to save training time. In addition, an iterative channel and PN estimation method is proposed, which can estimate the PN with pattern search and estimate the channel with compressed sensing, and can converge according to the residual of the received signal. Moreover, the error vector magnitude and the computational complexity are analyzed. Finally, our simulation results demonstrate that by employing the proposed method, the performance of the mmWave massive MIMO-OFDM system can be improved.