Joint Channel Estimation Research Articles

A D* orthogonal matching pursuit algorithm for time-varying channel estimation.

Orthogonal matching pursuit (OMP) combined with the A* search algorithm (A*OMP) exhibits robust reconstruction capabilities for synthesizing sparse data and signals, achieving relatively low reconstruction errors and a higher exact recovery ability than conventional OMP. However, A*OMP is only suitable for static channel estimation and cannot be applied to dynamic scenarios. This is because the channel delays for several consecutive orthogonal frequency-division multiplexing blocks per frame are similar and the path gains exhibit temporal correlation. This paper introduces a dynamic OMP approach (D*OMP) that employs a heuristic function of A*OMP and a unique reverse process, enabling sparse solutions to be identified in unknown and changing environments. The proposed method is highly practical for joint channel estimation across multiple blocks. Simulation and sea trial results indicate that D*OMP not only possesses superior channel recovery accuracy, but also has a more efficient channel reconstruction process, outperforming both A*OMP and conventional OMP.

Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted algorithms, resulting in high pilot overhead. To address this issue, this article proposes a semi-blind joint channel and symbol estimation receiver without a pilot training stage for RIS-aided multiple-input multiple-output (MIMO) (including massive MIMO) communication systems. In a semi-blind system, a transmission symbol matrix and two channel matrices are coupled within the received signals at the base station (BS). We decouple them by building two parallel factor (PARAFAC) tensor models. Leveraging PARAFAC tensor decomposition, we transform the joint channel and symbol estimation problem into least square (LS) problems, which can be solved by Alternating Least Squares (ALSs). Our proposed scheme allows duplex communication. Compared to recently proposed pilot-based methods and semi-blind receivers, our results demonstrate the superior performance of our proposed algorithm in estimation accuracy and speed.

A scheme of combining DFO and channel estimation scheme for mobile OFDM systems

AbstractTo reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high‐speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time‐varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high‐precision DFO estimator based on the convolutional neural network with anti‐noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time‐varying channel. Moreover, to improve the practicality of the algorithm, the non‐ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high‐speed mobile scenarios.

Optimized Bayesian Matching Pursuit for Joint Channel Estimation and Impulse Noise Mitigation

Optimized Bayesian Matching Pursuit for Joint Channel Estimation and Impulse Noise Mitigation

Performance comparison of various end-to-end learning technologies with a bandwidth-limited OWC system

Recently, end-to-end (E2E) learning methodologies have garnered significant attention as a compelling approach to attain global optimal communication within the domain of 6 G native intelligent systems. Nevertheless, a precise evaluation of the diverse E2E techniques is still lacking, leading to uncertainties regarding their applicable scenarios and effectiveness. In this paper, we present a comprehensive comparison applying three advanced E2E methods including the autoencoder-based geometric shaping (AEGS) model, comprehensive autoencoder (CAE) model, and wave-wise auto-equalization (WWAE) model in a real bandwidth-limited optical wireless communication (OWC) system. A novel attention-based comprehensive noise joint channel estimator (ACNJCE) is proposed to serve as a universal channel model adaptable to the existing E2E methods. Based on traditional carrier-less amplitude and phase modulation (CAP) modulation, AEGS, WWAE, and CAE are compared under the conditions of 2 GBaud and 3 GBaud respectively. The final results demonstrate that the CAE exhibits the capability to autonomously allocate bandwidth and achieves the highest dynamic adjustment range, which is increased by 69% compared with CAP based on neural network (NN) equalization. In contrast, AEGS has obvious advantages in terms of received optical power (ROP) gain. Based on bit-power loading discrete multi-tone modulation (DMT) modulation, WWAE can effectively compensate the signal spectrum after modulation order optimization and finally achieves the highest data rate under the condition that the −3 dB bandwidth of the channel is only close to 1 GHz. The BER of WWAE with DMT at this rate is 25.2% of that using the NN equalization. Furthermore, experimental results under turbulent conditions reveal that AEGS exhibits superior and more stable performance amidst the perturbations caused by turbulence due to its ability to achieve end-to-end autonomous optimization while integrating traditional modulation and bringing additional shaping gain. According to our knowledge, this marks the first comprehensive evaluation and comparison of existing major E2E algorithms and traditional communication algorithms in a real OWC system.

Iterative Joint Frequency Synchronization and Channel Estimation for Uplink Massive MIMO

Iterative Joint Frequency Synchronization and Channel Estimation for Uplink Massive MIMO

Robust Equalization for Single-Carrier Underwater Acoustic Communications Based on Parameterized Interference Model

There are several sources of artificial and naturally generated impulsive interference in marine environment which could severely degrade the acoustic communication performances. This letter tackles the equalization for single-carrier modulated systems with impulsive interference based on parameterized model, and a two-step equalization algorithm is proposed. In the first step, an iterative interference impulse estimation/cancellation can be performed and initial symbol estimation is obtained by conventional decision feedback equalization. In the second step, a joint channel, impulse, and symbol estimation for single-carrier modulation is applied to enhance the performance. The proposed algorithm is evaluated using partial-band partial-block-duration interference. Simulation results show that the proposed joint estimation algorithm has significant performance advantages over conventional algorithm in various signal-to-interference ratio levels.

Joint Device Activity Detection and Channel Estimation for mMTC

Joint Device Activity Detection and Channel Estimation for mMTC

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

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

Joint PAPR reduction and channel estimation in low-complexity VLC systems

In recent times, there has been a surge in interest surrounding visible light communication (VLC) as a compelling adjunct to radio frequency (RF) technology. This enthusiasm stems from deployments showcasing its efficacy in high-speed data transmission, characterized by low complexity, robust security, and high reliability. Among the variants of orthogonal frequency division multiplexing (OFDM), direct current-biased optical OFDM (DCO-OFDM) stands out in the realm of VLC systems, owing to its ability to furnish rapid data provisioning. However, a significant drawback of selective mapping (SLM) based DCO-OFDM transceivers lies in its computational complexity and practical implementation challenges. To address this limitation, this paper presents a modified SLM technique termed selective phase modulation (SPM) aimed at mitigating the peak-to-average power ratio (PAPR) in the transmitter section while simplifying the data decoding process at the receiver end. The SPM method utilizes a phase modulation and demodulation process based on clusters and assisted by pilot signals, streamlining the transceiver architecture. The SPM methodology offers reduced computational complexity at the transmitter by selectively applying phase sequences to data, circumventing the necessity for side information (SI) transmission and SI estimation at the receiver with a known pilot symbol inserted in the data. This dual-functionality mechanism not only achieves complexity reduction but also facilitates channel estimation at the receiver for proper data recovery. Based on complexity analysis and simulation outcomes, the computational complexity reduction strategy outperforms the conventional selective mapping (CSLM) technique, VLC-based pilot-assisted PAPR reduction systems, and embedded coded modulation (ECM). This allows for smooth incorporation of this technology into future VLC systems.

Cascaded ELM-based joint frame synchronization and channel estimation over Rician fading channel with hardware imperfections

Cascaded ELM-based joint frame synchronization and channel estimation over Rician fading channel with hardware imperfections

Sensing Aided Uplink Transmission in OTFS ISAC With Joint Parameter Association, Channel Estimation and Signal Detection

Sensing Aided Uplink Transmission in OTFS ISAC With Joint Parameter Association, Channel Estimation and Signal Detection

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

A comparative study of deep learning and iterative algorithms for joint channel estimation and signal detection in OFDM systems

Joint channel estimation and signal detection (JCESD) is crucial in orthogonal frequency division multiplexing (OFDM) systems, but traditional algorithms perform poorly in low signal-to-noise ratio (SNR) scenarios. Deep learning (DL) methods have been investigated, but concerns regarding computational expense and lack of validation in low-SNR settings remain. Hence, the development of a robust and low-complexity model that can deliver excellent performance across a wide range of SNRs is highly desirable. In this paper, we aim to establish a benchmark where traditional algorithms and DL methods are validated on different channel models, Doppler, and SNR settings, particularly focusing on the semi-blind setting. In particular, we propose a new DL model where the backbone network is formed by unrolling the iterative algorithm, and the hyperparameters are estimated by hypernetworks. Additionally, we adapt a lightweight DenseNet to the task of JCESD for comparison. We evaluate different methods in three aspects: generalization in terms of bit error rate (BER), robustness, and complexity. Our results indicate that DL approaches outperform traditional algorithms in the challenging low-SNR setting, while the iterative algorithm performs better in high-SNR settings. Furthermore, the iterative algorithm is more robust in the presence of carrier frequency offset, whereas DL methods excel when signals are corrupted by asymmetric Gaussian noise.

A novel sparse-based approach for joint radio frequency fingerprint and channel estimation

Radio Frequency Fingerprint (RFF) plays a pivotal role in specific emitter identification (SEI) and enhancing the physical layer security of wireless networks. However, past research on RFF extraction has encountered significant challenges, including algorithm instability in the presence of multi-path channels, disparities between actual RFF and proposed models, and the need to compensate for receiver RFF. In this study, we introduce an innovative algorithm designed to simultaneously estimate the transmitter's RFF and the non-line-of-sight (NLOS) channel state in the presence of receivers’ antenna effect. Moreover, we introduce the transmitter antenna feeding current as a modified RFF, accounting for transimtter components such as power amplifier (PA) nonlinearity, in-phase quadrature (IQ) modulator imbalance. Our approach commences with an exploration of wave propagation theory fundamentals, followed by a transformation of the inverse scattering equations into linear equations for joint channel and RFF estimation. Due to the non-convex nature of the derived equation, we employ the sparse lift method to render the problem convex. Leveraging the sparsity of channel coefficients, we devise a compressive sensing linear solution, denoted as the sparse-based approach for joint (SBJ) RFF and channel estimator. Additionally, we propose a calibration method to mitigate the effect of the receiver antennas on the estimated RFF of the transmitter. Through simulation, we demonstrate a 10 dB enhancement in RFF estimation accuracy compared to existing methods. Furthermore, our results indicate that the SBJ algorithm remains robust when faced with the combined effect of transmitter components, whereas previous models exhibit diminished precision as additional component effects are considered.

Joint channel estimation with an enhanced companding PAPR reduction transform for ACO-OFDM system

Joint channel estimation with an enhanced companding PAPR reduction transform for ACO-OFDM system

Correlation-Aided Joint Activity Detection and Channel Estimation for Multidevice Collaborative Massive Access

Correlation-Aided Joint Activity Detection and Channel Estimation for Multidevice Collaborative Massive Access

Joint Scattering Environment Sensing and Channel Estimation Based on Non-Stationary Markov Random Field

Joint Scattering Environment Sensing and Channel Estimation Based on Non-Stationary Markov Random Field

Temporally-correlated massive access: joint user activity detection and channel estimation via vector approximate message passing

In the paper, we investigate a massive machine-type communication (mMTC), where numerous single-antenna users communicate with a single-antenna base station while being active. However, the status of user can undergoes multiple transitions between active and inactive states across whole consecutive intervals. Then, we formulate the problem of joint user activity detection and channel estimation within the dynamic compressed sensing (DCS) framework, considering the temporally-correlated user activity across the entire consecutive intervals. To be specific, we introduce a new hybrid vector approximate message passing algorithm for DCS (HyVAMP-DCS). The proposed algorithm comprises a VAMP block for estimating channel and a loopy belief propagation (LBP) block for detecting user activity. Moreover, these two blocks can exchange messages, enhancing the performance of both channel estimation and user activity detection. Importantly, compared to the fragile GAMP algorithm, VAMP is robust and applicable to a much broader class of large random matrices. Furthermore, the fixed points of VAMP’s state evolution align with the replica prediction of the minimum mean-squared error. The simulation results illustrate the superiority of HyVAMP-DCS, demonstrating its significant outperformance over HyGAMP-DCS.

Digital Self-Interference Canceler with Joint Channel Estimator for Simultaneous Transmit and Receive System.

Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate.