Wireless Channel Estimation Research Articles

A fast-converging Bayesian tensor inference method for wireless channel estimation

In variational inference-based tensor channel estimation, high order singular value decomposition (HOSVD) initialization effectively captures the latent features of factor matrices, and accelerates convergence speed. However, HOSVD-based initialization further exacerbates the overfitting issue of the tensor variation Bayesian (TVB) method on each factor matrix element, leading to inaccurate rank estimation, and then significantly degrading channel parameter estimation performance. To prevent overfitting, we propose a new TVB method based on array spatial prior (ASP), which incorporates space correlations in tensor data, without introducing additional hierarchical probabilistic models. By analyzing the inferred posterior distribution and the non-decreasing property of the evidence lower bound (ELBO), we confirm the favorable convergence characteristics and global search capability of the proposed algorithm. Through simulations and experiments, we observe that compared to traditional TVB, the proposed algorithm achieves accurate automatic rank determination (ARD) in just a few iterations, significantly reducing convergence time. Meanwhile, it demonstrates superior parameter estimation accuracy with fewer iterations than the compared method.

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.

Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip

Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip

Channelformer: Attention Based Neural Solution for Wireless Channel Estimation and Effective Online Training

In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms in downlink scenarios. The self-attention mechanism is employed to achieve input precoding for the input features before processing them in the decoder. In particular, we implement multi-head attention in the encoder and a residual convolutional neural architecture as the decoder, respectively. We also employ a customized weight-level pruning to slim the trained neural network with a fine-tuning process, which reduces the computational complexity significantly to realize a low complexity and low latency solution. This enables reductions of up to 70% in the parameters, while maintaining an almost identical performance compared with the complete Channelformer. We also propose an effective online training method based on the fifth generation (5G) new radio (NR) configuration for the modern communication systems, which only needs the available information at the receiver for online training. Using industrial standard channel models, the simulations of attention-based solutions show superior estimation performance compared with other candidate neural network methods for channel estimation.

Secrecy Performance Analysis of Heterogeneous Networks With Unreliable Wireless Backhaul and Imperfect Channel Estimation

Wireless backhaul is an economical and flexible alternative to wired backhaul, however, it experiences impairments. This research proposes a new secure heterogeneous system model with unreliable wireless backhaul and imperfect channel estimation over Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading channel. To improve the system secrecy performance, we propose three small-cell transmitter selection schemes under channel estimation imperfections and wireless backhaul impairments, namely Optimum Selection (OS), Sub-optimum Selection (SS) and Minimum-Eavesdropping Selection (MES). Novel closed-form expressions of secrecy outage probability (SOP) are derived for these three selection schemes in both practical and ideal scenarios. The novel theoretical analysis and simulation results show the impact of wireless backhaul uncertainties and channel estimation errors on system secrecy performance. In addition, we investigate how the number of small-cell transmitters affects the system secrecy performance. The asymptotic behaviour is provided to obtain insights into the system performance. The analytical derivations and asymptotic expressions are validated by Monte Carlo simulations. Our novel theoretical analysis can guide different physical layer security (PLS) designs.

Deep Neural Network Augmented Wireless Channel Estimation for Preamble-Based OFDM PHY on Zynq System on Chip

Deep Neural Network Augmented Wireless Channel Estimation for Preamble-Based OFDM PHY on Zynq System on Chip

Towards deep learning-aided wireless channel estimation and channel state information feedback for 6G

Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success of DL has stimulated increasing interest in applying this paradigm to wireless channel estimation in recent years. Since DL principles are inductive in nature and distinct from the conventional rule-based algorithms, when one tries to use DL technique to the channel estimation, one might easily get stuck and confused by so many knobs to control and small details to be aware of. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition, and neural network design for 6G. Specifically, we present several case studies together with the numerical experiments to demonstrate the effectiveness of the DL-based wireless channel estimation framework.

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

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

A Kriging-Based Radio Environment Map Construction and Channel Estimation System in Threatening Environments

A Kriging-Based Radio Environment Map Construction and Channel Estimation System in Threatening Environments

Iterative Joint Estimation Procedure of Channel and PDP for OFDM Systems.

The power-delay profile (PDP) estimation of wireless channels is an important step to generate a channel correlation matrix for channel linear minimum mean square error (LMMSE) estimation. Estimated channel frequency response can be used to obtain time dispersion characteristics that can be exploited by adaptive orthogonal frequency division multiplexing (OFDM) systems. In this paper, a joint estimator for PDP and LMMSE channel estimation is proposed. For LMMSE channel estimation, we apply a candidate set of frequency-domain channel correlation functions (CCF) and select the one that best matches the current channel to construct the channel correlation matrix. The initial candidate set is generated based on the traditional CCF calculation method for different scenarios. Then, the result of channel estimation is used as an input for the PDP estimation whereas the estimated PDP is further used to update the candidate channel correlation matrix. The enhancement of LMMSE channel estimation and PDP estimation can be achieved by the iterative joint estimation procedure. Analysis and simulation results show that in different communication scenarios, the PDP estimation error of the proposed method can approach the Cramér-Rao lower bound (CRLB) after a finite number of iterations. Moreover, the mean square error of channel estimation is close to the performance of accurate PDP-assisted LMMSE.

An embedded pilot power based channel estimation and low-complexity feedback equalization scheme for OTFS system

Orthogonal Time Frequency Space modulation (OTFS) has evolved as an astounding modulation technique for high-speed communication in a doubly dispersive channel. In any wireless communication system, channel estimation and equalization are essential at the receiver to recover the transmitted data. To accomplish this for the emerging OTFS based systems, a modified embedded pilot-based channel estimation technique and low complexity feedback equalization algorithm for integer Doppler shifts in the delay-Doppler domain are proposed in this paper. Our channel estimation scheme exploits embedded-pilot arrangement, and the symbol equalization relies on the Interference calculation and its mitigation iteratively. To achieve this we contemplate a prudent arrangement of symbols in the OTFS frame in such a way that the Guard symbols prevent the interference between data symbols and the pilot symbol at the receiver. Two distinct lumps of received data of the same OTFS frame will be engaged in channel estimation and data detection. An analytical expression of the theoretical Cramer Rao Lower Bound (CRLB) is derived and plotted for the proposed channel estimation scheme. The attained simulation results for Bit-Error-Rate (BER) under the proposed scheme show a significant error rate improvement over the Minimum Mean Squared Error (MMSE) equalization algorithm. Further, a lower computational complexity is also achieved in comparison with modified MMSE detection and MP detection algorithms.

Federation based joint client and server side Machine Learning for 5G and beyond Wireless Channel Estimation

In this paper, we propose a Machine Learning (ML) based approach to address Wireless Channel Estimation (WCE) problem for 5G and beyond wireless networks. Accurate wireless channel estimation plays a crucial role for provision of high quality of service to billions of wireless devices in current and future wireless networks. Our proposed approach uses contemporary ML technique, Federated Learning (FL), in conjunction with the Stochastic Gradient Descent (SGD) algorithm to optimise the WCE problem. Our proposed approach leverages the local user wireless channel information to locally optimise the objective at users and then uses this locally optimised information at the server to optimise the global objective function. The proposed approach is referred to as joint Federated Server Learning and Federated Client Learning (j-FSL-FCL) in the paper. We formulate the WCE problem with a novel loss function to be used for the optimisation problem. To evaluate the performance of our proposed j-FSL-FCL approach (with and without SGD), we consider a Down Link (DL) wireless channel model with Multiple-Input Multiple-Output (MIMO) setting that mimic closely to the wireless channel for 5G and beyond wireless channel models. The performance measuring parameters for j-FSL-FCL are to minimise the difference between actual and estimated wireless channel parameters (channel strength and direction). We compare the results of our proposed approach with the other techniques in the literature based on Least Squares (LS), Linear Regression (LR) and Mean Square Error (MSE). It is shown that the proposed algorithm converges to the optimal solution quickly when used with SGD compared to other existing techniques. It is also shown that the efficiency of the proposed approach for WCE problem is much higher compared to other LS and MSE based techniques. Finally, we present some interesting futuristic applications of FL in the context of 5G and beyond wireless networks.

A review of wireless channel estimation techniques: challenges and solutions

A review of wireless channel estimation techniques: challenges and solutions

Maximum Likelihood Estimation of Time-Varying Sparsity Level for Dynamic Sparse Signals

In the field of Compressed Sensing, estimation of sparsity level is very essential as the sparsity level determines the minimum number of (i) measurements to be obtained of a sparse signal during acquisition and (ii) iterations to be performed for many of the greedy techniques for the perfect recovery of the sparse signal from the obtained measurements. In this paper, we propose a Maximum Likelihood (ML) estimator to estimate the instantaneous sparsity level during acquisition and an ML sequence (MLS) estimator of sparsity levels during recovery. As the sparsity level varies in time due to the continuous birth of newer supporting components and death of existing supporting components, this paper models the sparsity level variation as a stochastic birth-death process. The real-world applications of the proposed estimators are presented on the compression of aircraft vibration signals and the estimation of wireless channels. The simulation results on real-world and model-generated data demonstrate the performance merits of the proposed estimators compared to other existing methods.

Adaptive Pilot Pattern for Massive MIMO Systems

This work proposes a new adaptive pilot pattern used for pilot arrangement at the mobile station and a new channel estimation at the base station. It addresses the bit error rate (BER) performance optimization of wireless channel estimation in massive multiple-input multiple-output systems. First, we propose a new adaptive pilot pattern (APP) that offers a lower BER than the conventional pilot patterns. Then, we suggest a new channel estimation algorithm based on the APP. It is called the shifted APP-channel estimation (SACE). As a result, APP guarantees an optimal BER performance for the different system configurations, channel models, and carrier frequencies. It offers better BER performance than conventional pilot patterns, such as the long-term evolution (LTE) pilot pattern. Moreover, the shifted APP-based channel estimation algorithm solves the error floor caused by the usage of multiple subcarriers. It also offers a signal-to-noise ratio (SNR) improvement that reaches 17 dB at BER $= 10^{-2}$ compared to the conventional minimum mean square error (MMSE) channel estimation.

Design and Experimental Validation of an Augmented Reality System With Wireless Integration for Context Aware Enhanced Show Experience in Auditoriums

The development of multiple cultural and social related activities, such as shows related with the performing arts, conferences or presentations rely on facilities such as auditoriums, theatres and conference sites, which are progressively including multiple technological features in order to enhance user experience. There are still however situations in which user experience is limited owing to lack of environment adaption, such as people with disabilities. In this sense, the adoption of Context Aware paradigms within auditoriums can provide adequate functionalities in order to comply with specific needs. This work is aimed at demonstrating the feasibility in enhancing user experience (e.g., improving the autonomy of disabled people) within auditorium and theatre environments, by means of an Augmented Reality (AR) device (HoloLens smart glasses) with wireless system integration. To carry out the demonstration, different elements to build AR applications are described and tested. First, an intensive measurement campaign was performed in a real auditorium in the city of Pamplona (Baluarte Congress Center) in order to evaluate the feasibility of using Wi-Fi enabled AR devices in a complex wireless propagation scenario. The results show that these environments exhibit high levels of interference, owing to the co-existence and non-coordinated operation of multiple wireless communication systems, such as on site and temporary Wi-Fi access points, wireless microphones or communications systems used by performers, staff and users. Deterministic wireless channel estimation based in volumetric 3D Ray Launching have been obtained for the complete scenario volume, in order to assess quality of service metrics. For illustration purposes, a user-friendly application to help hearing impaired people was developed and its main features were tested in the auditorium. Such an application provides users with a 3D virtual space to visualize useful multimedia content like subtitles or additional information about the show, as well as an integrated call button.

High Dimensional Channel Estimation Using Deep Generative Networks

This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. Channel reconstruction using generative priors outperforms conventional CS techniques and requires fewer pilots. It also eliminates the need of a priori knowledge of the sparsifying basis, instead using the structure captured by the deep generative model as a prior. Using this prior, we also perform channel estimation from one-bit quantized pilot measurements, and propose a novel optimization objective function that attempts to maximize the correlation between the received signal and the generator's channel estimate while minimizing the rank of the channel estimate. Our approach significantly outperforms sparse signal recovery methods such as Orthogonal Matching Pursuit (OMP) and Approximate Message Passing (AMP) algorithms such as EM-GM-AMP for narrowband mmWave channel reconstruction, and its execution time is not noticeably affected by the increase in the number of received pilot symbols.

Wireless channel estimation and beamforming by using block sparse adaptive filtering

Channel estimation normally provides information about indoor and outdoor fading channel statistics. The adaptive channel estimation models play an important role to generate the required channel state information (CSI) using the estimated channel coefficient vector. The CSI can be utilized to generate an angle vector that controls the steering mechanism of a beamformer. The beamformer provides better directive gain for linear antenna array and helps to improve the signal to noise ratio of the wireless receiver. The proposed estimation model process the transmitted quadrature amplitude modulation (QAM) data samples in the frequency domain. The adaptive design incorporates norm-based sparsity through block recursive least square (BRLS) algorithm to develop a computationally efficient model. The proposed sparse-FBRLS (Fast BRLS) model has simultaneously addressed the problems of channel estimation and beamforming in case of indoor and outdoor communication. The performance of the model is tested by different performance measures under practical mobility conditions.

Deterministic 3D Ray-Launching Millimeter Wave Channel Characterization for Vehicular Communications in Urban Environments.

The increasing demand for more sensors inside vehicles pursues the intention of making vehicles more “intelligent”. In this context, the vision of fully connected and autonomous cars is becoming more tangible and will turn into a reality in the coming years. The use of these intelligent transport systems will allow the integration of efficient performance in terms of route control, fuel consumption, and traffic administration, among others. Future vehicle-to-everything (V2X) communication will require a wider bandwidth as well as lower latencies than current technologies can offer, to support high-constraint safety applications and data exhaustive information exchanges. To this end, recent investigations have proposed the adoption of the millimeter wave (mmWave) bands to achieve high throughput and low latencies. However, mmWave communications come with high constraints for implementation due to higher free-space losses, poor diffraction, poor signal penetration, among other channel impairments for these high-frequency bands. In this work, a V2X communication channel in the mmWave (28 GHz) band is analyzed by a combination of an empirical study and a deterministic simulation with an in-house 3D ray-launching algorithm. Multiple mmWave V2X links has been modeled for a complex heterogeneous urban scenario in order to capture and analyze different propagation phenomena, providing full volumetric estimation of frequency/power as well as time domain parameters. Large- and small-scale propagation parameters are obtained for a combination of different situations, taking into account the obstruction between the transceivers of vehicles of distinct sizes. These results can aid in the development of modeling techniques for the implementation of mmWave frequency bands in the vehicular context, with the capability of adapting to different scenario requirements in terms of network topology, user density, or transceiver location. The proposed methodology provides accurate wireless channel estimation within the complete volume of the scenario under analysis, considering detailed topological characteristics.

Efficient Optical MIMO–OFDM Channel Estimation Based on Correntropy Compressive Sensing

The compressive sensing (CS) technique is widely used in the field of radio-frequency channel estimation. However, in optical wireless channel (OWC) estimation, CS needed to be adapted to cope with the nonlinear distortion resultant from the electrical-to-optical and optical-to-electrical conversion process of the OWC. Therefore, a robust CS channel estimation technique is proposed for the optical MIMO–OFDM system. The proposed CS channel estimation technique is based on higher-order statistics (HOS). In which, the correntropy is exploited as a HOS similarity measurement instead of the second-order statistics euclidean distance (ED) used with the conventional CS to handle the OWC nonlinear distortion. Herein, the ED is the most popular iterative CS algorithms such as orthogonal matching pursuits, sparsity adaptive matching pursuit, and self-aware step size sparsity adaptive matching pursuit is efficiently replaced by the HOS correntropy based CS to show the effectiveness of the proposed algorithm. Moreover, the most recent visible light communication system GLIM-OFDM is considered here to test the proposed technique. However, the pre-estimated channel state information (CSI) obtained from the previous OFDM frame is exploited as initial information in estimating the CSI of the current OFDM frame, which improves overall system performance. A comparison with the conventional CS reconstruction algorithms is conducted via extensive simulation analysis. The simulation results show the superiority of the bit error rate of the proposed correntropy based CS channel estimation scheme compared to the conventional CS-based channel estimation algorithms.