Channel Estimation Technique Research Articles

Pilot design and doubly-selective channel estimation for faster-than-Nyquist signaling

Being capable of enhancing the spectral efficiency (SE), faster-than-Nyquist (FTN) signaling is a promising approach for wireless communication systems. This paper investigates the doubly-selective (i.e., time- and frequency-selective) channel estimation and data detection of FTN signaling. We consider the intersymbol interference (ISI) resulting from both the FTN signaling and the frequency-selective channel and adopt an efficient frame structure with reduced overhead. We propose a novel channel estimation technique of FTN signaling based on the least sum of squared errors (LSSE) approach to estimate the complex channel coefficients at the pilot locations within the frame. In particular, we find the optimal pilot sequence that minimizes the mean square error (MSE) of the channel estimation. To address the time-selective nature of the channel, we use a low-complexity linear interpolation to track the complex channel coefficients at the data symbols locations within the frame. To detect the data symbols of FTN signaling, we adopt a turbo equalization technique based on a linear soft-input soft-output (SISO) minimum mean square error (MMSE) equalizer. Simulation results show that the MSE of the proposed FTN signaling channel estimation employing the designed optimal pilot sequence is lower than its counterpart designed for conventional Nyquist transmission. The bit error rate (BER) of the FTN signaling employing the proposed optimal pilot sequence shows improvement compared to the FTN signaling employing the conventional Nyquist pilot sequence. Additionally, for the same SE, the proposed FTN signaling channel estimation employing the designed optimal pilot sequence shows better performance when compared to competing techniques from the literature.

Phase-Domain Injected Training for Channel Estimation in Constant Envelope OFDM

Constant envelope orthogonal frequency division multiplexing (CE-OFDM) is a multi-carrier waveform with 0 dB peak-to-average power ratio (PAPR). This property enables the exploitation of multi-carrier waveforms with non-linear power amplifiers, avoiding the undesirable clipping effects. However, the existing channel estimation techniques designed for OFDM cannot be reused, since the use of a phase modulator makes CE-OFDM a non-linear waveform. Previous works assumed that the channel estimation process relies on the transmission of preambles, and the data symbols are equalized using a frequency domain equalizer (FDE). To avoid the overhead induced by preambles, a phase-domain injected training (PIT) is proposed, where the pilot sequence is embedded in the phase dimension of the data symbols. This novel approach does not waste time and/or frequency resources as in preamble-based schemes. Moreover, it does not require additional power for the training. The received symbols are averaged with a dual procedure, and owing to the particular structure of CE-OFDM, the channel estimates are recovered. Also, the analytical expression of the channel estimation mean squared error (MSE) is derived. Finally, several numerical results illustrate the performance of the proposal, showing that the MSE, bit error rate (BER) and achievable rate are improved, as compared to the existing works.

Channel Estimation for Reconfigurable Intelligent Surface With a Few Active Elements

In this paper, a channel estimation technique for reconfigurable intelligent surface (RIS)-aided multi-user multiple-input single-output communication systems is proposed. By deploying a small number of active elements at the RIS, the RIS can receive and process the training signals. Through the partial channel state information (CSI) obtained from the active elements, the overall training overhead to estimate the entire channel can be dramatically reduced. To minimize the estimation complexity, the proposed technique is based on the linear combination of partial CSI, which only requires linear matrix operations. By exploiting the spatial correlation among the RIS elements, proper weights for the linear combination and normalization factors are developed. Numerical results show that the proposed technique outperforms other schemes using the active elements at the RIS in terms of the normalized mean squared error when the number of active elements is small, which is necessary to maintain the low cost and power consumption of RIS.

GRU-Based Deep Learning Channel Estimation Scheme for the IEEE 802.11p Standard

In the IEEE 802.11p standard, channel estimation is quite challenging in the presence of high mobility due to rapid channel variations. Extensive research has been conducted on data pilot aided (DPA) channel estimation to address this issue. Nonetheless, the estimation performance of state-of-the-art DPA schemes is unsatisfactory because of error propagation. In this letter, a channel estimation technique is proposed using a gated recurrent unit (GRU) based deep learning scheme for extracting time and frequency domain features to suppress error propagation of the DPA process applied to IEEE 802.11p. Simulation results demonstrate that the proposed scheme outperforms other deep-learning-based channel estimation schemes without increasing the computational complexity.

Deep learning-based energy harvesting with intelligent deployment of RIS-assisted UAV-CFmMIMOs

The ever-evolving internet of things (IoT) has spawned hundreds of wireless sensors that communicate via the internet infrastructure. The lifetime and self-sustainability of these sensors are pivotal factors dictating the performance of respective application infrastructure. Radio frequency energy harvesting (RFEH) technology has exhibited the capability of effectively augmenting the battery lifetime of these sensors. In this work, we introduce a novel framework called CURe, which combines the advantages of cell-free massive multiple-input multiple-output (CFmMIMO) and reconfigurable intelligent surfaces (RISs) to provide uninterrupted energy harvesting for IoT devices through RFEH. CFmMIMO integrates the advantages of distributed systems and massive MIMO, while RIS improves the signal strength of the information transfer and RFEH via its passive reflection capabilities. Moreover, we consider unmanned aerial vehicles (UAVs) equipped with CFmMIMO as mobile access points (APs) to better serve the moving sensory devices. To further enhance RFEH, we propose DeNCE, a channel estimation technique based on deep learning (DL) that eliminates the need for traditional closed-form equation-based channel estimation methods. Through evaluation, we first validate the performance of CURe by comparing it with the modified bisection search for max–min fairness (MBS-MMF) algorithm and later corroborate that DeNCE significantly improves the performances of both models. Finally, to optimize the UAV deployment and ensure continuous RFEH coverage, we propose dARL, a deep reinforcement learning (DRL)-based scheduling framework that enables UAV-CFmMIMO swarms to perform continuous energy harvesting in the coverage area collaboratively.

Channel estimation for OFDM-based indoor broadband power line communication systems

State-of-the-art indoor broadband power line communications (PLC) systems use orthogonal frequency division multiplexing (OFDM) signals with constellations up to 10 bits/symbol, which makes channel estimation a key aspect. This paper focuses on the initial channel estimation used in the payload, which might be further adapted by means of a decision-directed strategy. This initial estimate has to be computed on a per-frame basis and can be accomplished from the preamble, the header symbol (assuming that it has been correctly decoded) or from a combination of both. This work proposes simple channel estimation techniques for this problem and derives their most appropriate parameters. They are compared with the linear minimum mean squared-error (LMMSE) estimator and others commonly used in wireless scenarios by considering both their performance and computational complexity. The factors limiting the performance of the estimators based on the preamble and the header symbols are analyzed and the implications of the differences between PLC and wireless channels, such as the absence of fading, in the design of estimation techniques that require knowledge of the channel statistics are discussed. A performance analysis of the considered techniques is accomplished in a set of 171 measured indoor PLC channels. Obtained results indicate that estimators from the header perform better than those based on the preamble symbols and we provide a computationally simple estimator that gives nearly optimum performance by combining estimates from both the preamble and the header symbols.

Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)

In wireless systems, channel estimation is considered a problematic technology, due to the fact of the difference in time between wireless channels and the noise effect. Orthogonal frequency-division multiplexing (OFDM) is a promising candidate for future optical communications and has received wide concern. The article proposed a Coherent Optical (CO) orthogonal frequency division multiplexing (OFDM) scheme, which gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Nevertheless, both coherent detection and OFDM are prone to phase noise due to the phase mismatch between the laser oscillators at the transmitter and receiver sides and the relatively long OFDM symbol duration compared to that of single carrier communications. An Extreme Learning Machine (ELM) with Pilot Assisted Equalization (PEM) is proposed for compensation of impairments caused by fibre nonlinearity in coherent optical communication systems. Channel estimation using ELM and the value of distortion is sent to the OSTBC receiving end based on the distortion information the data is decoded and pilot data is removed. FFT is applied to the data and QPSK demodulation is done in the data to get its original form. In addition, the article utilized a free-space optical communication system of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) with a modified receiver structure. Simulation reveals that the proposed model exhibits significant BER (0.0112) performance and provides better spectral efficiency as compared with conventional systems and less computational complexity. This suggested that the proposed method shows better performance by using the CO-OFDM-FSO-MIMO-ELM-based channel estimation technique for high-speed data communication networks in real-time scenarios respectively.

Self-interference cancellation in underwater acoustic communications systems using orthogonal pilots in IBFD

This paper proposes a Self-interference (SI) cancellation system model of Underwater acoustic (UWA) communication for in-band full-duplex (IBFD) technology. The SI channel is separated from the Far channel by exploiting a concurrently orthogonal pilot channel estimation technique using two orthogonal frequency-division multiplexing (OFDM) blocks to establish orthogonality between them based on a unitary matrix. Compared to the half-duplex channel estimator, the mean squared error (MSE) and the bit error rate (BER) provided strong evidence for the efficiency of the proposed SI cancellation. Since full-duplex systems are more efficient than half-duplex ones, the proposed approach might be seen as a viable option for them. The proposed method proved effective when used with a fixed full-duplex (FD) position and FD shifting of up to 4°. Different channel lengths and distances are adopted to evaluate the proposed method. Initial findings indicate that MSE for the SI channel minimum mean-square error (MMSE) estimator at 20 dB is 0.118 · 10−3, for fixed FD. In addition, this paper presents a geometry channel model for the Far channel in the IBFD underwater communication system that describes the propagation delay of the multipath reflection. The simulation results for the multipath propagation delay spread are similar to the traditional results, with the delay spread of the suggested model reaching (79 ms), which is close to the Bellhop simulator result (78 ms).

Modified entropy based least square channel estimation technique for 5G massive multiple input multiple output universal filtered multicarrier systems

These days’ Massive multiple input multiple output (mMIMO) systems have become popular because of their enhanced data transmission rates, robustness against multipath fading, enhanced spectral efficacy, and ability to communicate with a more significant number of users with immense coverage. The critical challenge of the mMIMO systems is precisely replenishing the channel state information (CSI), along with the synchronization between receiver and transmitter. The CSI has recovered with the help of various channel estimation (CE) techniques. This paper presents a modified entropy-based least square CE technique for 5G mMIMO-UFMC systems. The proposed CE technique performance is novel and better than the Least Square (LS) and minimum mean square error (MMSE) CE techniques. The proposed CE technique was evaluated using MATLAB, and its performance results are shown in the simulation. The performance results of the proposed CE are evaluated based on the mean square error (MSE) and bit error rate (BER) of the obtained signal. The executed results prove that the proposed CE is efficient compared to conventional CE methods. The performance of the MCM techniques with respect to the proposed CE is also presented in this paper. This paper also explains the analytical assessment of the LS, MMSE, and MELS CE techniques. The results show that at high values of SNR, the proposed algorithm outperforms the LS and MMSE for both BER and MSE.

Channel Estimation and Performance Analysis of a Wide-FOV Visible Light Communication System With Random Receiver Orientation and Location

A practical visible light communications (VLC) system accounting for the effect of the random orientation and the random location of the photo-detector receiver is considered in this paper. The characterizations of the random orientation of the receiver, modeled as the receiver employing wide field of view (FOV) for data detection, and the random location of the receiver are utilized to derive the statistics for the scenarios in which (i) only the orientation of the receiver is random, (ii) only the location of the receiver is random, and (iii) both the orientation and location of the receiver are random. Furthermore, the receiver structures for a fully coherent VLC system and a VLC system employing the least-squares technique for channel estimation are proposed. Employing the receiver structures, the closed/series-form expressions for the symbol error probabilities are derived for all three scenarios of practical VLC channel models. The numerical results particularly show the crucial role of the ratio of the distance of the location of the receiver from the center of the receiving plane to the distance from the light-emitting diode (LED) to the center of the receiving plane in the performance of all the considered VLC systems.

Joint Pilot Spacing and Power Optimization Scheme for Nonstationary Wireless Channel: A Deep Reinforcement Learning Approach

Pilot-assisted channel estimation techniques are essential for wireless communication systems. Most studies focus on the estimation algorithms and interpolation techniques. However, the design of pilot pattern is often neglected. In this letter, we propose a joint pilot spacing and power optimization scheme based on deep reinforcement learning (DRL) to address the mismatch problem of pilot configuration for nonstationary wireless channel. First, we model the adaptive pilot design decision-making process as a Markov Decision Process (MDP) to reduce pilot overhead and power loss. Then a deep Q-network (DQN) based learning algorithm is proposed to optimize the spacing and power of pilots so as to maximize estimation performance while reducing system cost. Simulation results show that the performance of the proposed approach is better than conventional pilot configuration algorithms. Moreover, we analyze the key factors that affect the performance of the proposed scheme.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.

Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model

For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cram\'er-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.

Channel Estimation for Reconfigurable Intelligent Surface Assisted High-Mobility Wireless Systems

Next-generation communication systems aim for providing pervasive services, including the high-mobility scenarios routinely encountered in mission-critical applications. Hence we harness the recently-developed reconfigurable intelligent surfaces (RIS) to assist the high-mobility cell-edge users. However, in the face of high Doppler frequencies, the existing RIS channel estimation techniques that assume block fading generally result in irreducible error floors. In order to mitigate this problem, we propose a new RIS channel estimation technique, which is the first one that performs minimum mean square error (MMSE) based interpolation for the sake of taking into account the time-varying nature of fading even within the coherence time. The RIS modelling invokes only passive elements without relying on RF chains, where both the direct link and RIS-reflected links as well as both the line-of-sight (LoS) and non-LoS (NLoS) paths are taken into account. As a result, the cascaded base station (BS)-RIS-user links involve the multiplicative concatenation of the channel coefficients in the LoS and NLoS paths across the two segments of the BS-RIS and RIS-user links. Against this background, we model the multiplicative RIS fading correlation functions for the first time in the literature, which facilitates MMSE interpolation for estimating the high-dimensional and high-Doppler RIS-reflected fading channels. Our simulation results demonstrate that for a vehicle travelling at a speed as high as 90 mph, employing a low-complexity RIS at the cell-edge using as few as 16 RIS elements is sufficient for achieving substantial power-effieincy gains, where the Doppler-induced error floor is mitigated by the proposed channel estimation technique.

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.

Physical Layer Security in Downlink of Cell-Free Massive MIMO With Imperfect CSI

This paper investigates the threat of passive eavesdropping on downlink cell-free massive MIMO (CF-MaMIMO) systems, examining in a particular system under the effects of imperfect channel estimation. Physical layer security (PLS) techniques and power allocation algorithms are typically adopted to deteriorate the quality of eavesdropped signals. In the downlink stream, artificial noise (AN) is broadcasted simultaneously with the users’ data streams, with the aim of jamming the eavesdropper’s signal without sacrificing the quality of service (QoS). However, imperfect channel estimation results in AN leakage to legitimate users. Our work considers this condition, and the power allocation algorithms we present determine the minimum power that is needed to maintain the user’s Qos even in the presence of leaked AN. We first propose the cooperative PLS algorithm (COP), which facilitates AN broadcasting via access point (AP) cooperation. Unfortunately, this cooperation wastes some of the power used to send the AN and, in most cases, increases the computational complexity. We then present the independent PLS algorithm (IND), where the goal is to regain this power by allowing each AP to broadcast AN independently. However, to meet the feasibility criterion, this occurs at the cost of increasing the number of used antennas. This technique reduces the power allocation complexity but increases the computational complexities of the precoder design and the channel estimation process. Our results reveal that the proposed algorithms enhance the security performance of CF-MaMIMO. We evaluate such performance from numerous angles including the number of users, number of APs, QoS, beamforming, AN leakage, and channel estimation techniques.

A New Adaptive OMP-MAP Algorithm-based Iterative Sparse Channel Estimation for OFDM Underwater Communication

A sparse channel estimation approach based on doubly spread underwater acoustic (UWA) channels is widely used todetect coherent acoustic orthogonal frequency division multiplexing (OFDM) signals. A new time-domain channelestimation (CE) technique for OFDM based UWA communication with Rician fading is used to exploit the channel sparsity.First, to improve the estimation accuracy in high noise conditions, we have exploited the channel sparsity to generate aclosed-form equation for the termination condition. Then, in low-level noise instances, the additional criterion to balanceestimation accuracy and computing costs has been established. By incorporating these two requirements within theorthogonal-matching-pursuit (OMP) structure, an adaptive-OMP (AOMP) algorithm has been proposed. The AOMP andmaximum a posteriori probability (MAP) techniques are combined to provide a computationally efficient, and a newAOMP-MAP scheme for estimating the sparse complex channel path gain has been proposed. Further, The minimumvariance unbiased estimator is used to improve the proposed CE technique. Exploiting the experimental channel data,computer simulations reveal that the proposed CE technique obtains the outstanding outcomes.

Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation

Terahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beamwidth. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefront becomes spherical in the near-field. Therefore, near-field signal model should be considered for channel acquisition in THz systems. Unlike prior works which mostly ignore the impact of near-field beam-squint (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB. The model-based approach is based on orthogonal matching pursuit (OMP) algorithm, for which we design an NB-aware dictionary. The key idea is to exploit the angular and range deviations due to the NB. We then employ the OMP algorithm, which accounts for the deviations thereby ipso facto mitigating the effect of NB. We further introduce a federated learning (FL)-based approach as a model-free solution for channel estimation in a multi-user scenario to achieve reduced complexity and training overhead. Through numerical simulations, we demonstrate the effectiveness of the proposed channel estimation techniques for wideband THz systems in comparison with the existing state-of-the-art techniques.

Performance analysis of channel estimation techniques for 5G massive MIMO-OFDM system

Performance analysis of channel estimation techniques for 5G massive MIMO-OFDM system

Noncoherent OFDM Transmission via Off-the-Grid Joint Channel and Data Estimation

Pilot-aided channel estimation techniques are known to waste the spectral bandwidth. An off-the-grid blind estimator for time-variant orthogonal frequency division multiplexing (OFDM) systems is studied in this letter. In this regard, we propose a blind estimator based on atomic norm minimization (ANM) for OFDM systems. To do so, at the first transmission block, using a lifted ANM (LANM) and simple constraint on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> norm of data, we simultaneously estimate the channel and data. For the subsequent blocks, we use a penalized ANM (PANM) to simultaneously track the channel’s parameters and detect transmit signals. The proposed problems require an infinite dimensional search, hence are NP-hard. Therefore, we propose two semidefinite programs (SDPs) to implement them. We then derive the total computational complexity of the proposed estimator. The simulation results show the superiority of the proposed estimator to the state-of-the-arts.