Channel Estimation Technique Research Articles

Joint PAPR reduction with channel estimation for OFDM-based VLC systems

Abstract This paper presents a new combination of channel estimation (CE) methods and peak-to-average power ratio (PAPR) reduction techniques for orthogonal frequency division multiplexing (OFDM)-based visible light communication (VLC) systems. We first propose iterative clipping and filtering to minimize the PAPR of optical OFDM signals. Then, we introduce a computationally efficient, high-performance channel estimation approach using block-type pilots with modified clipped signals. The proposed method integrates clipping with three distinct CE techniques, resulting in both significant PAPR reduction and enhanced bit error rate (BER) performance. Among the CE methods evaluated, the expectation-maximization (EM) technique provides better BER results compared to other approaches. Simulation results confirm that the combined use of clipping and advanced CE methods leads to notable improvements in PAPR reduction and BER performance for OFDM-based VLC systems.

A deep learning‐based channel estimation and joint adaptive hybrid beamforming approach for mm‐wave massive MIMO with group‐of‐subarrays

SummaryMillimeter‐wave massive multiple‐input‐multiple‐output (MIMO) is a wireless communication that enables communication at extremely high data rates. It is widely used to expand communication bandwidth to improve spectral and energy efficiency. The mm‐wave system is usually based on the radio frequency (RF) chain. Beamforming is used to minimize the interface signal and maximize each user's received signal power. The hybrid beamforming method uses channel estimation and beam generation techniques that improve overall performance and save energy. There are various limitations and disadvantages arising from different techniques of the MIMO system. Hence, the proposed model is introduced to enhance the performance and maximize the spectral and energy efficiency. Initially, the optimal RF chain selection and channel optimization are done by the differential evolution firefly‐assisted optimized channel compression‐reconstruction (DEF_OCCR) network. In this research, the RF chain selection process is done using hybrid differential evolution firefly optimization. A robust autoencoder‐driven deep learning model is proposed for channel estimation after RF chain selection. Finally, the deep learning convolutional joint adaptive hybrid beamforming network (DConvHB) is used for beamforming to maximize spectral efficiency. The overall performance of this work is analyzed using several existing models to describe its superiority. The average system throughput is 12.85 bits, and the spectral efficiency of the proposed model is 5 bits for 10 dB and 32.18 bits for 13 dB. The proposed energy efficiency model is 1.73 bits and 4.96 bits for 10 dB.

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

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

Enhanced channel estimation with atomic norm minimization and reconfigurable intelligent surfaces in mmWave MIMO systems

SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.

A switching norm based least mean Square/Fourth adaptive technique for sparse channel estimation and echo cancellation

To realize rapid data transmission, the broadband transmission technique is being extensively explored and applied in existing wireless communication systems. The multi-path channel in broadband wireless communication systems is sparse and this sparsity can be used as prior knowledge to estimate the channel. To make use of sparsity, this paper recommends a switching norm-based least mean square/fourth (SN-LMS/F) adaptive approach for sparse channel estimation and echo cancellation. The suggested SN-LMS/F is implemented by adding a soft parameter adjustment function (SPF) into the conventional LMS/F adaptive method's cost function and utilizes both the l0 and l1 norm to exploit system sparsity with reduced complexity. The simulated output indicates that the suggested SN-LMS/F adaptive technique provides a more desirable performance for sparse channel estimation and echo cancellation with reduced execution time.

An in-depth assessment of the physical layer performance in the proposed B5G framework

The introduction of fifth-generation (5G) technology marks a significant milestone in next-generation networks, offering higher data rates and new services. Achieving optimal performance in 5G and beyond 5G (B5G) systems requires addressing key requirements like increased capacity, high efficiency, improved performance, low latency, support for many connections, and quality of service. It is well-known that suboptimal network configuration, hardware impairments, or malfunctioning components can degrade system performance. The physical layer of the radio access network, particularly channel estimation and synchronization, plays a crucial role. Hence, this paper offers an in-depth evaluation of the 5G Physical Downlink Shared Channel (PDSCH), along with its related channel models such as the Clustered Delay Line (CDL) and the Tapped Delay Line (TDL). This work assesses 5G network performance through practical and IA-based channel estimation and synchronization techniques, and anticipates numerologies for B5G networks. Extensive simulations leveraging the Matlab 5G New Radio (NR) toolbox assess standardized channel scenarios in both macro-urban and indoor environments, following configurations set by the 3rd Generation Partnership Project (3GPP). The numerical results offer valuable insights into achieving the maximum achievable throughput across various channel environments, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The throughput comparisons are performed under assumptions of ideal, realistic, and convolutional neural networks (CNN)-based channel estimation with both perfect and realistic synchronization conditions. Importantly, the study pinpoints certain physical layer elements that have a pronounced impact on system performance, providing essential insights for devising effective strategies or refining CNN-based methods for forthcoming mobile B5G networks.

Compressive Sensing-Based Channel Estimation for Uplink and Downlink Reconfigurable Intelligent Surface-Aided Millimeter Wave Massive MIMO Systems

This paper investigates single-user uplink and two-user downlink channel estimation in reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. Because of the difficulty associated with the estimation of channels in RIS-aided wireless communication systems, channel state information (CSI) is assumed to be known at the receiver in some previous works in the literature. By assuming that prior knowledge of the line-of-sight (LoS) channel between the RIS and the base station (BS) is known, two compressive sensing-based channel estimation schemes that are based on simultaneous orthogonal matching pursuit and structured matching pursuit (StrMP) algorithms are proposed for estimation of uplink channel between RIS and user equipment (UE), and joint estimations of downlink channels between BS and a UE, and between RIS and another UE, respectively. The proposed channel estimation schemes exploit the inherent common sparsity shared by the angular domain mmWave channels at different subcarriers. The superiority of one of the proposed channel estimation techniques, the StrMP-based channel estimation technique, with negligibly higher computational complexity cost compared with other channel estimators, is documented through extensive computer simulation. Specifically, with a reduced pilot overhead, the proposed StrMP-based channel estimation scheme exhibits better performance than other channel estimation schemes considered in this paper for signal-to-noise ratio (SNR) between 0 dB and 5 dB upward at different instances for both uplink and downlink scenarios, respectively. However, below these values of SNR the proposed StrMP-based channel estimation scheme will require higher pilot overhead to perform optimally.

Deep learning-based channel estimation with application to 5G and beyond networks

Channel state information (CSI) feedback estimation for a downlink medium in a massive multiple input multiple output (MIMO) system is an essential and critical task to improve channel capacity and performance yield, especially in a frequency division duplex (FDD) multiplexing system. However, spectral efficiency degradation is a massive issue due to high channel feedback overhead. This work proposes a deep learning-based channel estimation (DLCE) model to improve channel reconstruction efficiency and channel overhead reduction accuracy. The proposed deep learning (DL) mechanism consists of encoder and decoder network where encoder network is utilized to compress CSI matrices whereas decoder network is used to decompress obtained CSI matrices. Here, inverse discrete Fourier transform (IDFT) method is utilized to convert CSI matrices of frequency domain into CSI matrices of delay domain. Simulation results are evaluated between uplink and downlink medium in the massive MIMO system considering a co-operation in science and technology (COST) 2,100 model. Here, a significant improvement in correlation and normalized mean square error (NMSE) results is observed. The proposed DLCE model shows superior performance against varied channel estimation techniques in terms of NMSE and correlation efficiency.

Statistical analysis in cellular systems for channel capacity improvement with dynamic pilots across different angles users

Accurate channel state information (CSI) is crucial for optimizing wireless communication systems. In scenarios with varying user-to-base station angles, the angle-dependent coherence time impacts conventional pilot strategies. Due to small angles, the coherence time of the user decreases dramatically because of doppler shift, which causes an increase in the number of pilots. We introduces an innovative sub-block design approach for systems with different user angles. This method harmonizes coherence time of high and low-angle users, while maintaining a constant pilot count. This not only improves spectral efficiency but also ensures accurate channel estimation. Through simulations, we demonstrate the effectiveness of our approach in enhancing both spectral efficiency upt to 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10 \\%$$\\end{document} and CSI precision. This breakthrough contributes to the advancement of channel estimation techniques in scenarios with angle-dependent coherence time, offering practical benefits to wireless communication systems.

Predicted position-driven deep learning channel estimation for massive MIMO systems

Multiple-input multiple-output (MIMO) can significantly improve the energy efficiency and spectral efficiency of wireless communication systems, and obtaining accurate channel state information is a key prerequisite. However, traditional channel estimation techniques often have high computational complexity, poor channel estimation performance, and do not fully utilize the information in the communication scene. In this paper, a channel estimator (PrePD-CNN) with convolutional neural network (CNN) driven by predicted position information is proposed based on high-precision localization technology for 6G. The channel estimation performance is improved by rationally transforming the sensing information and accurately reconstructing the quasi-channel state as auxiliary information. Simulation results show that the BER performance and MSE performance of PrePD-CNN are significantly lower than that of traditional channel estimation algorithms. Therefore, the proposed scheme combines stronger robustness and good generalization, which not only reduces the complexity of the algorithm but also improves the performance.

Channel estimation techniques based on intrinsic interference mitigation for FBMC/OQAM systems

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) emerges as a noteworthy alternative to orthogonal frequency division multiplexing (OFDM). FBMC/OQAM addresses the limitations of OFDM by utilizing real-valued, even-symmetrical prototype filters with smaller side lobes and faster spectrum decay. This results in lower out-of-band energy and higher spectral efficiency (SE). However, the orthogonality condition of FBMC/OQAM in the real domain introduces an intrinsic interference (II) component that poses challenges for channel estimation (CE). The conventional straightforward OFDM CE techniques cannot be applied directly. To obtain a precise CSI with the FBMC/OQAM system, Kalman filter (KF) is altered and incorporated into the use of AP and coding. The first proposed method, KalCod, employs a data spreading method to diminish the II effect. The simulation results indicate that KalCod has a superior capacity to the other CE methods, while still maintaining a reasonable level of complexity. In the second proposed method, modified Kalman-auxiliary pilot (KAP), an AP is used to eliminate the II at pilot symbols then updating the channel coefficients using modified KF. When used as a CE method for FBMC/OQAM systems, the modified KAP outperforms its competitors in terms of estimation accuracy, simpler implementation, as well as higher throughput and spectral efficiency.

The Evolution of Mobile Communication: A Comprehensive Survey on 5G Technology

This comprehensive survey meticulously navigates the evolution of mobile communication, focusing on the revolutionary strides introduced by 5G technology. The study elucidates primary objectives, research methodology, pivotal findings, and implications inherent in the trajectory of mobile communication. It aims for a detailed examination of the technological landscape, outlining features that distinguish 5G from its predecessors. The research methodology involves an exhaustive literature review, culminating in a comprehensive overview of 5G technology. Key findings highlight 5G's transformative capacities, spanning unparalleled data transfer speeds, low-latency communication, and applications like IoT, augmented reality, and autonomous vehicles. The study underscores 5G's far-reaching impact across various sectors. Serving as both a retrospective analysis and a valuable resource, the survey guides researchers, industry professionals, and policymakers through the intricate landscape of 5G technology. Addressing the imperative of accommodating increasing mobile data traffic, the 5G network strives for improved spectrum utilization, energy efficiency, and QoS parameters. Notable innovations include Massive MIMO, promising high spectral efficiency. The focus on efficacy, accuracy, and channel estimation techniques is evident. The article explores specific requirements of 5G wireless communication systems, addressing challenges faced by earlier mobile generations. The advent of 5G emerges as a pivotal solution, aiming for a 1000-fold capacity gain and assuring quality-of-service, higher spectral efficiency, ultra-reliable communication, and facilitating massive machinetype communication. Challenges in traffic load and resource consumption necessitate innovative approaches. The research reviews emerging wireless technologies, including an analysis of Massive MIMO as an efficient solution. The paper promises to be a valuable resource, contributing to the seamless development of state-of-the-art 5G and B5G networks, recognizing the importance of continued research and innovation in the dynamic landscape of mobile communication technologies.

Noise-Canceling Channel Estimation Schemes Based on the CIR Length Estimation for IEEE 802.11p/OFDM Systems

This paper investigates methods for noise-canceling channel estimation (NC-CE) to track rapid time-varying channels in IEEE 802.11p/orthogonal frequency division multiplexing (OFDM) systems. To this end, we introduce a novel three-step channel estimation technique based on the estimated length of the channel impulse response (CIR). This approach aims to surpass the performance of conventional designs that rely on constructed data pilots (CDPs). In the first step, we not only eliminate noise components but also estimate the channel frequency responses (CFRs) of virtual subcarriers for long preamble parts. Moving on to the second step, we incorporate a modified CDP method without a frequency-domain reliability test and interpolation, taking into account the CFRs of virtual subcarriers obtained at the previous OFDM symbol time. The final step can be implemented as the operation of the inverse fast Fourier transform (IFFT)/nulling/FFT to reduce noise components from the CFRs obtained in the second step and generate CFRs for virtual subcarriers to be used in the next symbol time. The results of our simulations validate the effectiveness of our proposed channel estimation schemes.

Utilization of adaptive techniques for channel estimation and intrusion detection in OWC systems

Utilization of adaptive techniques for channel estimation and intrusion detection in OWC systems

Pilot based channel estimation improvement in orthogonal frequency-division multiplexing systems using linear predictive coding

Pilot based least square (LS) channel estimation is a commonly used channel estimation technique in orthogonal frequency-division multiplexing based systems due to its simplicity. However, LS estimation does not handle the noise effect and hence suffers from performance degradation. Since the channel coefficients are correlated in time and hence show a slower variation than the noise, it is possible to encode the channel using linear predictive coding (LPC) without the noise. In this work, the channel is estimated from the pilots using LS estimation and in a second step the channel’s LS estimated is encoded as LPC coefficients to produce an improved channel estimation. The estimation technique is simulated for space-time block coding (STBC) based orthogonal frequency-division multiplexing (OFDM) system and the bit error rate (BER) curves show improvement of the LPC estimation over the LS estimation of the channel.

Spectral decomposition-based optimization for MMSE receiver design in an uplink MU-MIMO cooperative communication system with imperfect CSI

The receiver’s architecture is always a critical issue in multiple-input multiple-output (MIMO) systems, as it has a great impact on the system’s performance and complexity. Accordingly, in this paper, a spectral decomposition-based minimum mean squared error (MMSE) receiver is proposed in an uplink multi-user MIMO (MU-MIMO) system with a full-duplex decode and forward relay. Imperfect channel state information is assumed at the relay and base station where, least-squares channel estimation technique is applied due to its simplicity and low complexity. An equivalent relay is applied for self-interference cancellation. While, multi-user interference is mitigated by using the block diagonalization concept. A closed-form expression for the mean squared error (MSE) of the estimated data symbol is derived in the presence of channel estimation error. Moreover, an optimization problem is solved to derive an expression for the proposed MMSE receiver’s matrix. Finally, the performance of the proposed receiver is compared to that of the conventional receiver and Zero-forcing (ZF) receiver. Results show the superiority of the proposed receiver over both receivers.

Robust Underwater Acoustic Channel Estimation Method Based on Bias-Free Convolutional Neural Network

In recent years, the study of deep learning techniques for underwater acoustic channel estimation has gained widespread attention. However, existing neural network channel estimation methods often overfit to training dataset noise levels, leading to diminished performance when confronted with new noise levels. In this research, a “bias-free” denoising convolutional neural network (DnCNN) method is proposed for robust underwater acoustic channel estimation. The paper offers a theoretical justification for bias removal and customizes the fundamental DnCNN framework to give a specialized design for channel estimation, referred to as the bias-free complex DnCNN (BF-CDN). It uses least squares channel estimation results as input and employs a CNN model to learn channel characteristics and noise distribution. The proposed method effectively utilizes the temporal correlation inherent in underwater acoustic channels to further enhance estimation performance and robustness. This method adapts to varying noise levels in underwater environments. Experimental results show the robustness of the method under different noise conditions, indicating its potential to improve the accuracy and reliability of channel estimation.

OFDM-Based VLC Systems: A Systematic Review

Visible Light Communications (VLC) represents a new technology of wireless communications allowing high data rate, high-speed internet access, green and friendly communication system, especially for indoor users, where the use of Light Emitting Diodes (LEDs) is growing as a viable alternative to traditional illumination. Orthogonal frequency division multiplexing (OFDM) is a promising modulation format for optical wireless communication (OWC); however, precise channel estimation is required for synchronization and equalization. Moreover, one of the challenging issues for OFDM is its high Peak-to-Average Power Ratio (PAPR) due to the superposition of all subcarriers; therefore, the system requires a high linearity and dynamic range. This paper provides a systematic overview of OFDM-based VLC systems, highlighting the essential aspects of PAPR reduction and channel estimation techniques. Several technical challenges have been addressed to realize the full potential of OFDM-based VLC technology. Moreover, we provide new insights into the over-explored and under-explored areas, which lead us to identify open research problems of VLC based on OFDM. Concisely, this paper serves as a guide and a starting point for researchers willing to research VLC using OFDM.

Channel estimation and it’s techniques: A survey

In wireless mobile networks, the signal transmitted from the transmitter to the receiver is vulnerable to many performance-degrading factors, such as distortion, noise, fading, and nonlinear distortion, due to the physical nature of the channel. To address these factors, accurate prediction and estimation must be employed to achieve optimized performance in wireless mobile networks. Several channel estimation techniques have been developed. In this article, we provide a review of some of these techniques under various categories, highlighting their strengths and limitations in different operational environments.

Power Optimization of Unmanned Aerial Vehicle-Assisted Future Wireless Communication Using Hybrid Beamforming Technique in Disaster Management

Several established procedures are required to be followed when responding to disasters. Although most of them have so far been successful, they pose their own set of difficulties. The primary factor is response time, which is crucial for disaster management. The use of unmanned aerial vehicles (UAVs) for wireless communication not only increases response time but also is energy efficient. However, UAVs have limited power capacities, which makes their use in wireless communication challenging. In wireless communication, hybrid beamforming is a technique for optimizing the battery consumption of unmanned aerial vehicles (UAVs). Massive MIMO technology facilitates hybrid beamforming by deploying antenna arrays, efficiently directing transmitted energy to intended receivers, and minimizing power waste. Beamforming relies heavily on accurate Channel State Information (CSI). Advanced channel estimation and tracking techniques optimize power allocation based on real-time channel conditions, thereby minimizing energy consumption and ensuring connection reliability. Hybrid beamforming combines the advantages of digital and analog beamforming to reduce power consumption while maintaining communication performance. This paper proposes a power optimization technique for UAV-based wireless communication using hybrid beamforming to maximize spectral efficiency and increase the battery life of UAVs.