Channel Estimation Performance Research Articles

Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results

In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions.

Sparse channel estimation for underwater acoustic OFDM systems with super-nested pilot design

Underwater acoustic channels are usually sparse and have large delay spread. In this paper, super-nested array structure in the field of array signal processing is borrowed to be the pilot design of underwater acoustic OFDM systems, in order to better estimate large delay spread channels with limited number of pilots. Specifically, by constructing the pilot subcarriers’ covariance matrix and the pilot position difference, the virtual pilot on the differential co-array are employed for sparse channel estimation. In order to reduce the error between the estimated pilot subcarriers’ covariance matrix and the ideal covariance matrix, the cross-correlation matrix of pilot subcarriers is estimated in advance for interference cancellation. Then the sparse iterative covariance estimation algorithm (SPICE) is adopted to further refine the covariance matrix and improve the channel estimation performance. Simulation, pool and sea experimental results show that the proposed method can effectively estimate the large delay spread sparse channels and improve the performance of underwater acoustic OFDM systems.

DFT-spread OTFS-NOMA for downlink integrated positioning and communication

In this article, a non-orthogonal multiple-access (NOMA) transmission system that incorporates discrete Fourier transform (DFT) spread orthogonal time-frequency space (OTFS) modulation is proposed for the downlink integrated positioning and communication (IPAC). This system is designed to decrease the Peak-to-Average Power Ratio (PAPR) within the OTFS-NOMA system while simultaneously serving both communication user (CU) and positioning user (PU) within the same time-frequency resources. Firstly, we demonstrate the applicability of the proposed DFT-spread OTFS-NOMA (DFT-s-OTFS-NOMA) system for downlink transmission. It efficiently serves both the CU and PU concurrently within the same time and frequency resources, thereby increasing spectrum utilization. Subsequently, we develop a two-stage parameter estimation algorithm focusing on accurate positioning parameter estimation for PU and precise channel estimation for CU. Then, the position of the PU is estimated using the time of arrival (TOA) model and the linear least squares (LLS) approach. Simulation demonstrates a 9 dB PAPR reduction in the proposed system compared to the existing OTFS-NOMA system. Additionally, the proposed two-stage positioning method for the PU achieves centimeter-level accuracy in position estimation and near millimeter-per-second-level accuracy in velocity estimation. What is more, the performance of channel estimation and symbol detection, aided by the PU signal in the DFT-s-OTFS-NOMA system, demonstrates resilience against Doppler effects without the need for reliance on pilots. This ensures sustained an effective Bit Error Rate (BER) even in time-variant high mobility scenarios.

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.

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.

On the Performance of MMSE Channel Estimation in Massive MIMO Systems over Spatially Correlated Rician Fading Channels

Massive multiple-input-multiple-output (M-MIMO) offers remarkable advantages in terms of spectral, energy, and hardware efficiency for future wireless systems. However, its performance relies on the accuracy of channel state information (CSI) available at the transceivers. This makes channel estimation pivotal in the context of M-MIMO systems. Prior research has focused on evaluating channel estimation methods under the assumption of spatially uncorrelated fading channel models. In this study, we evaluate the performance of the minimum-mean-square-error (MMSE) estimator in terms of the normalized mean square error (NMSE) in the uplink of M-MIMO systems over spatially correlated Rician fading. The NMSE allows for easy comparison of different M-MIMO configurations, serving as a relative performance indicator. Besides, it is an advantageous metric due to its normalization, scale invariance, and consistent performance indication across diverse scenarios. In the system model, we assume imperfections in channel estimation and that the random angles in the correlation model follow a Gaussian distribution. For this scenario, we derive an accurate closed-form expression for calculating the NMSE, which is validated via Monte-Carlo simulations. Our numerical results reveal that as the Rician K-factor decreases, approaching Rayleigh fading conditions, the NMSE improves. Additionally, spatial correlation and a reduction in the antenna array interelement spacing lead to a reduction in NMSE, further enhancing the overall system performance.

Evaluation of FBMC Channel Estimation using multiple Auxiliary symbols for high throughput and low BER 5G and beyond communications

Filter Bank Multi-Carrier (FBMC) modulation has been recognized as a consistent contender and a possible successor for Orthogonal Frequency Division Multiplexing (OFDM) in 5G and beyond because of its outstanding spectral properties. The channel is assessed in FBMC using pilot-symbol aided channel estimation that provides robust estimates even for severe channel conditions. In the present work, neutraliz ing the imaginary interference at the pilot positions is focused while estimating the channel. To neutralize the imaginary in terference, multiple auxiliary symbols have been proposed to enhance the throughput and channel capacity. The Iterative Minimum Mean Squared Error (IMMSE) cancellation scheme has been proposed to reduce the interference at the pilot and data positions. Transmission power, Bit Error Rate (BER) and throughput are computed for Filter Bank Multi Carrier (FBMC), OFDM and proven that better system performance is obtained for FBMC. The performance of channel estimation is evaluated through 5G standards and indicates that the usage of multiple auxiliary symbols per pilot leads to better throughput and low Bit Error Rate at low power transmission.

Visible Light Integrated Positioning and Communication: A Multi-Task Federated Learning Framework

Recently, visible light positioning and visible light communication are becoming a promising technology for integrated sensing and communication. However, the isolated design of positioning and communication has limited the system efficiency and performance. In this paper, a visible light integrated positioning and communication (VIPAC) framework is formulated, in which the positioning task for the sensing service and the channel estimation task for the communication service are integrated into a unified architecture. Firstly, a multi-task learning architecture, which is composed of a sparsity-aware shared network and two task-oriented sub-networks, is proposed to fully exploit the inherent sparse features of visible light channels, and achieve mutual benefits between the two tasks. The depth of the shared network can be adaptively adjusted to extract the optimal shared features, and the two sub-networks are further optimized for the two tasks, respectively. Moreover, the emerging federated learning technique is introduced to devise a multi-user cooperative VIPAC scheme, which further improves the generalization ability in spatiotemporally nonstationary environments while preserving data privacy. It is shown by theoretical analysis and simulation results that, the proposed scheme can significantly improve the performance of positioning and channel estimation in spatiotemporally nonstationary environments compared with existing benchmark schemes.

Random Access Protocol With Channel Oracle Enabled by a Reconfigurable Intelligent Surface

The widespread adoption of Reconfigurable Intelligent Surfaces (RISs) in future practical wireless systems is critically dependent on the integration of the RIS into higher-layer protocols beyond the physical (PHY) one, an issue that has received minimal attention in the research literature. In light of this, we consider a classical random access (RA) problem, where uncoordinated users’ equipment (UEs) transmit sporadically to an access point (AP). Differently from previous works, we ponder how a RIS can be integrated into the design of new medium access control (MAC) layer protocols to solve such a problem. We consider that the AP is able to control a RIS to change how its reflective elements are configured, namely, the RIS configurations. Thus, the RIS can be opportunistically controlled to favor the transmission of some of the UEs without the need to explicitly perform channel estimation (CHEST). We embrace this observation and propose a RIS-assisted RA protocol comprised of two modules: Channel Oracle and Access. During channel oracle, the UEs learn how the RIS configurations affect their channel conditions. During the access, the UEs tailor their access policies using the channel oracle knowledge. Our proposed RIS-assisted protocol is able to increase the expected throughput by approximately 60% in comparison to the slotted ALOHA (S-ALOHA) protocol.

An Improved Orthogonal Matching Pursuit Algorithm for CS-Based Channel Estimation

Wireless broadband transmission channels usually have time-domain-sparse properties, and the reconstruction of these channels using a greedy search-based orthogonal matching pursuit (OMP) algorithm can effectively improve channel estimation performance while decreasing the length of the reference signal. In this research, the improved OMP and SOMP algorithms for compressed-sensing (CS)-based channel estimation are proposed for single-carrier frequency domain equalization (SC-FDE) systems, which, in comparison with conventional algorithms, calculate the path gain after obtaining the path delay and updating the observation matrices. The reliability of the communication system is further enhanced because the channel path gain is calculated using longer observation vectors, which lowers the Cramér–Rao lower bound (CRLB) and results in better channel estimation performance. The developed method can also be applied to time-domain-synchronous OFDM (TDS-OFDM) systems, and it is applicable to the improvement of other matching pursuit algorithms.

Channel Estimation in MIMO OFDM Systems with Tapped Delay Line Model

The continuous increase in the user demands fornew-generation communication systems, is making the wireless channel more complex and challenging for estimation, developing a simulation model for the channel,and evaluating the performance of different MIMO systems. In this work, a simulation model for multipath fading channels in wireless communication is performed. The model includes a selection of typical Tapped-Delay-Line channel models that can be implemented to reproduce the effects of representative channel distortion and interference. Based on the simulation results, the proposed method exhibits accurate channel estimation performance for frequency-selective fading channels. The proposed work employed LS, MMSE, and ML methods for channel estimation, using 16 and 32 pilots and fixed pilot locations in each frame. Results are obtained for 4x4, 8x8, 16x16, 16x8, and 16x4 MIMO systems and tapped delay line systems.

Channel Estimation for LEO Satellite Massive MIMO OFDM Communications

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.

Accurate channel estimation and hybrid beamforming using Artificial Intelligence for massive MIMO 5G systems

In a large-scale massive Multi User-Multiple Input Multiple Output (MU-MIMO) environment channel estimation and beamforming is a breathtaking task for enhancing the array gain without utilizing the many Radio Frequency (RF) chains. Somehow, several state-of-the-art works perform channel estimation and Hybrid Beamforming (HB) using Artificial Intelligence (AI) Algorithms but the massive computation intricacy and power consumption hindered the performance of the existing system. By considering the existing issues, we designed a DL-based Hybrid Beamformer for the MIMO environment with 5G communication technology (DLHB-MIMO 5G). The proposed HB design centers on three progressive processes such as accurate channel estimation, hybrid beamformer design, and hybrid beamforming. In the accurate channel estimation phase, the noise and interference-free channels are estimated using the Improved Extreme Learning Machine-Adaptive Orthogonal Matching Pursuit (IELM-AOMP) algorithm based on channel parameters and user feedback. In the HB design stage, the shortcoming of prior DL models is resolved by adopting Transfer Learning Lite Convolutional Neural Network (TL-LiteCNN) for designing a hybrid beamformer. Beforehand, we select the appropriate antenna numbers using Stackelberg Game Theory (StGT) using adequate parameters. In the hybrid beamforming stage, the problem of less Spectral Efficiency (SE) during low SNR conditions is fixed by adopting the Improved Proximal Policy Optimization (IPPO) algorithm with several beamforming parameters to generate highly resourceful hybrid beams. The realization of the proposed research is carried out using the MATLAB R2020a simulation tool and the performance of the proposed work is compared with the major state-of-the-art works in terms of useful performance metrics. The comparative results show that the proposed work beats the existing works.

Channel Estimation and Turbo Equalization for Coded OTFS and OFDM: A Comparison

In this letter, we study joint channel estimation and turbo equalization for coded orthogonal time frequency space (OTFS) and orthogonal frequency division multiplexing (OFDM). To the best of our knowledge, this is the first study that compares performance of OTFS and OFDM coded systems using the state-of-the-art message passing (MP) and soft minimum mean square error (MMSE) equalizers, under imperfect channel estimation. We show that the commonly used threshold-based channel estimator incurs noticeable performance loss for OTFS. Hence, we propose a basis expansion model (BEM) channel estimation technique for OTFS, which improves performance of the threshold-based channel estimator and yields a superior performance to OFDM when using small modulations of BPSK and QPSK. Our work also reveals that OTFS can perform inferior to OFDM as the modulation order increases. This is due to the increased 2D detection complexity of OTFS.

Joint CFO and Channel Estimation for RIS-Aided Multi-User Massive MIMO Systems

Accurate channel estimation is essential to achieve the performance gains promised by the use of reconfigurable intelligent surfaces (RISs) in wireless communications. In the uplink of multi-user orthogonal frequency division multiple access (OFDMA) systems, synchronization errors such as carrier frequency offsets (CFOs) can significantly degrade the channel estimation performance. This becomes more critical in RIS-aided communications, as the RIS phases are adjusted based on the channel estimates and even a small channel estimation error leads to a significant performance loss. Motivated by this, we propose a joint CFO and channel estimation method for RIS-aided multi-user massive multiple-input multiple-output (MIMO) systems. Our proposed pilot structure makes it possible to accurately estimate the CFOs without multi-user interference (MUI), using the same pilot resources for both CFO estimation and channel estimation. For optimization of the RIS phase shifts at the data transmission stage, we propose a projected gradient method (PGM) which achieves the same performance as a more computationally demanding grid search technique while having a significantly lower computational complexity. Simulation results demonstrate that the proposed method provides an improvement in the normalized mean-square error (NMSE) of channel es-timation as well as in the bit error rate (BER) performance. Moreover, analyzing the computational complexity and the pilot resource efficiency of the proposed method, we demonstrate that the proposed estimation approach requires low computational load and no extra cost in pilot overhead.

Cluster-aware channel estimation with deep learning method in deep-water acoustic communications.

In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.

Joint Channel Estimation and Data Detection for IRS-Assisted OFDM Systems

Intelligent reconfigurable surface (IRS) becomes a promising technology for improving spectrum efficiency and reducing the energy consumption in the next-generation networks. To reap the passive beamforming gain of IRS, accurate channel estimation is essentially required. However, the existing channel estimation methods for the IRS-assisted orthogonal frequency division multiplexing (OFDM) systems suffer from serious performance loss, due to using the least-squares (LS) estimator and ignoring the channel sparsity in the time domain. Another drawback of the existing methods is that the channel estimation and data detection are separated from each other so that any inaccurate channel estimation would cause an error accumulation problem for the data detection. To overcome these drawbacks, we propose a novel joint sparse channel estimation and data detection method for the IRS-assisted OFDM systems. The novelties of the proposed method are twofold: (i) present a new common sparse representation model for the joint channel estimation and data detection, which can utilize the common channel sparsity between the pilots and data streams to significantly improve the channel estimation performance and reduce the training overhead; and (ii) propose an efficient variational Bayesian inference (VBI) framework to simultaneously perform the Bayesian inference for the sparse channel recovery and data detection, where the coupling effect caused by the reflection pattern is trickly evaded by adopting an additional column-independent factorization. Besides exploiting the common sparsity for the channel estimation and data detection, the proposed method also avoids using the LS estimator, and thus its performance can be greatly enhanced. Simulation results verify the superiority of the proposed method.

Channel estimation and PAPR reduction in OFDM based on dual layers-superimposed training

Superimposed Training (ST) is one of the most appealing channel estimation techniques for Orthogonal Frequency Division Multiplexing (OFDM), to be possibly exploited in 6G. The data and pilot symbols are sharing the same time and frequency resources, and hence, the overhead is significantly reduced. Moreover, the superimposed pilots can be also used for the reduction of the Peak-to-Average Power Ratio (PAPR). However, a joint channel estimation and PAPR reduction procedure has not been addressed yet. In this work, a novel scheme denoted as Dual Layers-Superimposed Training (DL-ST) is proposed for this joint purpose. The Training Sequence (TS) of the first layer is targeted to perform channel estimation, while the TS of a second layer is designed for PAPR reduction and it is made transparent to the first one. Both layers can be independently processed, which implies a reduced complexity. To verify the performance of the proposed technique, the analytical expression of the channel estimation Mean Squared Error (MSE) is derived. Finally, several numerical results further illustrate the performance of the proposal, showing how the MSE and achievable rate are improved while significant PAPR reductions are attained with negligible complexity.

Performance analysis of channel estimation techniques for IRS assisted MIMO

The need for low latency and high data rates is increasing rapidly since the advent of wireless communication. The current fifth-generation (5G) networks are unable to fulfill the requirements of upcoming technologies. So, researchers are commencing their research beyond 5G. Terahertz (THz) frequency is one candidate to satisfy the large bandwidth requirement and intelligent reflecting surface (IRS) is incorporated to mitigate signal blockage which is the main problem for communication at high frequencies. Channel estimation is a process of identifying coefficients of the channel matrix. The compressive sensing technique is of great importance as it decreases the number of pilot symbols required for channel estimation. As mmWave and THz signals are naturally sparse applying a compressive sensing technique is reasonable. Unlike other papers, this paper considers the imperfect IRS elements, which is the real case, by varying the value of beta (amplitude perturbations). The channel estimation performance of the conventional least squares (LS), orthogonal matching pursuit (OMP) and Oracle is analyzed with respect to signal-to-noise ratio (SNR) and pilot length (T). Normalized mean square error (NMSE) and spectral efficiency (SE) are used as performance metrics and the OMP algorithm is found to perform better than LS even at a fewer number of pilot symbols.

Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems.

Orthogonal time-frequency space (OTFS) modulation outperforms orthogonal frequency-division multiplexing in high-mobility scenarios through better channel estimation. Current superimposed pilot (SP)-based channel estimation improves the spectral efficiency (SE) when compared to that of the traditional embedded pilot (EP) method. However, it requires an additional non-superimposed EP delay-Doppler frame to estimate the delay-Doppler taps for the following SP-aided frames. To handle this problem, we propose a channel estimation method with high SE, which superimposes the perfect binary array (PBA) on data symbols as the pilot. Utilizing the perfect autocorrelation of PBA, channel estimation is performed based on a linear search to find the correlation peaks, which include both delay-Doppler tap information and complex channel gain in the same superimposed PBA frame. Furthermore, the optimal power ratio of the PBA is then derived by maximizing the signal-to-interference-plus-noise ratio (SINR) to optimize the SE of the proposed system. The simulation results demonstrate that the proposed method can achieve a similar channel estimation performance to the existing EP method while significantly improving the SE.