Bayesian Channel Research Articles

Joint Bayesian Channel Estimation and Data Detection for Underwater Acoustic Communications

Joint Bayesian Channel Estimation and Data Detection for Underwater Acoustic Communications

Bayesian Channel Estimation for Intelligent Reflecting Surface-Aided mmWave Massive MIMO Systems With Semi-Passive Elements

In this paper, we propose a Bayesian channel estimator for intelligent reflecting surface-aided (IRS-aided) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with semi-passive elements that can receive the signal in the active sensing mode. Ultimately, our goal is to minimize the channel estimation error using the received signal at the base station and additional information acquired from a small number of active sensors at the IRS. Unlike recent works on channel estimation with semi-passive elements that require both uplink and downlink training signals to estimate the UE-IRS and IRS-BS links, we only use uplink training signals to estimate all the links. To compute the minimum mean squared error (MMSE) estimates of all the links, we propose a novel variational inference-sparse Bayesian learning (VI-SBL) channel estimator that performs approximate posterior inference on the channel using VI with the mean-field approximation under the SBL framework. The simulation results show that VI-SBL outperforms the state-of-the-art baselines for IRS with passive reflecting elements in terms of the channel estimation accuracy and training overhead. Furthermore, VI-SBL with semi-passive elements is shown to be more spectral- and energy-efficient than the baselines with passive reflecting elements.

A Fully Bayesian Approach for Massive MIMO Unsourced Random Access

In this paper, we propose a novel fully Bayesian approach for the massive multiple-input multiple-output (MIMO) massive unsourced random access (URA). The payload of each user device is coded by the sparse regression codes (SPARCs) without redundant parity bits. A Bayesian model is established to capture the probabilistic characteristics of the overall system. Particularly, we adopt the core idea of the model-based learning approach to establish a flexible Bayesian channel model to adapt the complex environments. Different from the traditional divide-and-conquer or pilot-based massive MIMO URA strategies, we propose a three-layer message passing (TLMP) algorithm to jointly decode all the information blocks, as well as acquire the massive MIMO channel, which adopts the core idea of the variational message passing and approximate message passing. We verify that our proposed TLMP significantly enhances the spectral efficiency compared with the state-of-the-arts baselines, and is more robust to the possible codeword collisions.

Multitask Sparse Bayesian Channel Estimation for Turbo Equalization in Underwater Acoustic Communications

In this article, a multitask sparse Bayesian learning channel estimation based on factor graphs (MT-SBL-FG) for turbo equalization (TEQ) in underwater acoustic (UWA) communications is proposed. Based on the framework of multitask sparse Bayesian learning, the received data are divided into subblocks and the channel estimation (CE) of each subblock is regarded as a task. Considering the time-varying channel in UWA communications and the CE error in turbo equalization, a factor graph is proposed for the multitask CE of the subblocks. Based on the factor graph, a message-passing algorithm with weighting factor is derived. The performance of channel estimation is improved by utilizing the temporal correlation between the subblocks and the sparsity of the UWA channel. The proposed algorithm has been tested by the underwater trial data collected in an experiment conducted in Qiandao Lake, Hangzhou, China, in May 2016. We have verified that the proposed algorithm outperforms other CE algorithms in terms of the bit error rate (BER).

A Structured Sparse Bayesian Channel Estimation Approach for Orthogonal Time-Frequency Space Modulation.

Orthogonal time-frequency space (OTFS) modulation has been advocated as a promising waveform for achieving integrated sensing and communication (ISAC) due to its superiority in high-mobility adaptability and spectral efficiency. In OTFS modulation-based ISAC systems, accurate channel acquisition is critical for both communication reception and sensing parameter estimation. However, the existence of the fractional Doppler frequency shift spreads the effective channels of the OTFS signal significantly, making efficient channel acquisition very challenging. In this paper, we first derive the sparse structure of the channel in the delay Doppler (DD) domain according to the input and output relationship of OTFS signals. On this basis, a new structured Bayesian learning approach is proposed for accurate channel estimation, which includes a novel structured prior model for the delay-Doppler channel and a successive majorization-minimization (SMM) algorithm for efficient posterior channel estimate computation. Simulation results show that the proposed approach significantly outperforms the reference schemes, especially in the low signal-to-noise ratio (SNR) region.

Low-complex Bayesian estimator for imperfect channels in massive muti-input multi-output system

<span lang="EN-US">Motivated by the fact that the complexity of the computations is one of the main challenges in large multiple input multiple output systems, known as massive multiple-input multiple-output (MIMO) systems, this article proposes a low-complex minimum mean squared error (MMSE) Bayesian channel estimator for uplink channels of such systems. First, we have discussed the necessity of the covariance information for the MMSE estimator and how their imperfection knowledge can affect its accuracy. Then, two reduction phases in dimension and floating-point operations have been suggested to reduce its complexity: in phase 1, eigenstructure reduction for channel covariance matrices is implemented based on some truncation rules, while in phase 2, arithmetic operations reduction for matrix multiplications in the MMSE equation is followed. The proposed procedure has significantly reduced the complexity of the MMSE estimator to the first order O(M), which is less than that required for the conventional MMSE with O(M<sup>3</sup>) in terms of matrix dimension. It has been shown that the estimated channels using our proposed procedure are asymptotically aligned and serve the same quality as the full-rank estimated channels. Our results are validated by averaging the normalized mean squared error (NMSE) over a length of 500 sample realizations through a Monte Carlo simulation using MATLAB R2020a.</span>

Is Channel Estimation Necessary to Select Phase-Shifts for RIS-Assisted Massive MIMO?

Reconfigurable intelligent surfaces (RISs) consist of many passive elements of metamaterials whose impedance can be controllable to change the characteristics of wireless signals impinging on them. Channel estimation is a critical task when it comes to the control of a large RIS when having a channel with a large number of multipath components. In this paper, we derive Bayesian channel estimators for two RIS-assisted massive multiple-input multiple-output (MIMO) configurations: i) the short-term RIS configuration based on the instantaneous channel estimates; ii) the long-term RIS configuration based on the channel statistics. The proposed methods exploit spatial correlation characteristics at both the base station and the planar RISs, and other statistical characteristics of multi-specular fading in a mobile environment. Moreover, a novel heuristic for phase-shift selection at the RISs is developed. A computationally efficient fixed-point algorithm, which solves the max-min fairness power control optimally, is proposed. Simulation results demonstrate that the proposed uplink RIS-aided framework improves the spectral efficiency of the cell-edge mobile user equipments substantially in comparison to a conventional single-cell massive MIMO system. The impact of several channel effects are studied to gain insight about when the channel estimation, i.e., the short-term configuration, is preferable in comparison to the long-term RIS configuration to boost the spectral efficiency.

Fused LISS IV Image Classification using Deep Convolution Neural Networks

These days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively.

Joint Bayesian Channel Estimation and Data Detection for OTFS Systems in LEO Satellite Communications

Lower earth orbit (LEO) satellites play an important role in the integration of space and terrestrial communication networks, which typically encounter high-mobility scenarios. It has been shown that orthogonal time frequency space (OTFS) modulation performs well in such high-mobility scenarios by transforming the time-varying channels into the delay-Doppler domain. In this paper, we develop a joint channel estimation and data detection algorithm for OTFS-based LEO satellite communications. Firstly, we adopt the powerful variational Bayesian inference (VBI) method for estimating the delay-Doppler channel vector, which contains the channel gain, the delay and the Doppler. Secondly, we exploit the unknown data symbols in an OTFS frame as ‘virtual pilots’ for improving the accuracy of channel estimation and detect them simultaneously. Our simulation results demonstrate that the proposed algorithm achieves improved channel estimation mean square error and bit error rate performance than its conventional counterparts.

Bayesian Learning-Based Clustered-Sparse Channel Estimation for Time-Varying Underwater Acoustic OFDM Communication.

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in underwater acoustic (UWA) communication due to its good anti-multipath performance and high spectral efficiency. For UWA-OFDM systems, channel state information (CSI) is essential for channel equalization and adaptive transmission, which can significantly affect the reliability and throughput. However, the time-varying UWA channel is difficult to estimate because of excessive delay spread and complex noise distribution. To this end, a novel Bayesian learning-based channel estimation architecture is proposed for UWA-OFDM systems. A clustered-sparse channel distribution model and a noise-resistant channel measurement model are constructed, and the model hyperparameters are iteratively optimized to obtain accurate Bayesian channel estimation. Accordingly, to obtain the clustered-sparse distribution, a partition-based clustered-sparse Bayesian learning (PB-CSBL) algorithm was designed. In order to lessen the effect of strong colored noise, a noise-corrected clustered-sparse channel estimation (NC-CSCE) algorithm was proposed to improve the estimation accuracy. Numerical simulations and lake trials are conducted to verify the effectiveness of the algorithms. Results show that the proposed algorithms achieve higher channel estimation accuracy and lower bit error rate (BER).

A Vector Processor for Mean Field Bayesian Channel Estimation

Physical layer signal processing algorithms in the wireless domain are seeing increased use of machine learning algorithms, especially Bayesian methods. This work presents the hardware architecture and implementation of a vector processor for one such application, Bayesian channel estimation (CE) (BCE). The BCE vector processor supports a generic instruction set with a supplement of specialized instructions to realize Bayesian algorithms in the signal processing context. The vector processor is designed to work as an accelerator in a system-on-chip (SoC) with an AHB/AXI bus interface or as stand-alone unit. The vector processor achieves more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> improvement in performance when compared with a traditional CE algorithm running on a commercial vector processor. To the best of authors knowledge, this is a first known hardware implementation of a variational Bayesian inference algorithm for a wireless communication application.

Bayesian Channel Estimation and Data Detection in Oversampled OFDM Receiver With Low-Resolution ADC

This paper focuses on a novel orthogonal frequency division multiplexing (OFDM) receiver architecture, which uses a low-resolution analog-to-digital converter (ADC) to oversample the received signal in time domain. Although attractive in terms of power consumption and hardware cost, low-resolution ADC incurs severe nonlinear quantization distortion and thus destroys the orthogonality between the OFDM subcarriers. The loss of orthogonality, together with the oversampling-caused correlation in noise samples, poses a great challenge to the OFDM receiver design. This paper aims to tackle this challenge. For the proposed receiver, we consider an often-used two-phase transmission protocol. In the first phase, training OFDM symbols are transmitted for channel estimation. In the second phase, information-conveying OFDM symbols are transmitted and detected using the previously obtained channel estimate. Therefore, the proposed receiver consists of two key components: channel estimator and data detector. These components are elaborately derived in the framework of Bayesian inference. Due to the diversity gain resulting from the oversampling operation, the proposed receiver can remarkably outperform the counterparts, including the conventional OFDM receiver with perfect infinite-precision quantization.

Bayesian Channel Estimation in Multi-User Massive MIMO With Extremely Large Antenna Array

We investigate wideband uplink channel estimation for a multi-user (MU) multiple-input single-output (MISO) OFDM system, in which the base station (BS) is equipped with an extremely large antenna array (ELAA). The existing compressive sensing massive multiple-input multiple-output (MIMO) channel estimation approach with a traditional sparsity promoting prior model becomes invalid in the ELAA scenario due to the spatial non-stationary effects caused by the spherical wavefront and visibility region (VR) issue. We therefore propose a new structured prior with the Hidden Markov Model (HMM) to promote the structured sparsity of the spatial non-stationary ELAA channel. Based on this, a Bayesian inference problem on the posterior of the ELAA channel coefficients is formulated. In addition, we propose the turbo orthogonal approximate message passing (Turbo-OAMP) algorithm to achieve a low-complexity channel estimation. Comprehensive simulations verify that the proposed algorithm has supreme performance under spatial non-stationary ELAA channels compared to various state-of-the-art baselines.

Bayesian Iterative Channel Estimation and Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

This article investigates a robust receiver scheme for a single carrier, multiple-input-multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation-maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation.

Joint Antenna Detection and Bayesian Channel Estimation for Non-Coherent User Terminals

In this paper, we propose a method of improving the channel estimates for non-coherent multi-antenna terminals, which are terminals that cannot control the relative phase between its antenna ports, with channels that can be considered constant over multiple time slots. The terminals have multiple antennas and are free to choose whichever antenna they want to use in each time slot. An unknown phase shift is introduced in each time slot as we cannot guarantee that the terminals are phase coherent across time slots. We compare three different clustering techniques that we use to detect the active antenna. We also compare a set of different statistical and heuristic estimators for the channels and the phase shifts. We evaluate the methods by using correlated Rayleigh fading and three different bounds on the uplink capacity. The accuracy of the capacity bounds are verified with bit-error-rate simulations. With our proposed methods we can have an SNR improvement of approximately 2 dB at 1 bit/s/Hz.

Channel Estimation Performance Analysis of FBMC/OQAM Systems with Bayesian Approach for 5G-Enabled IoT Applications

A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.

Grid-Less Variational Bayesian Channel Estimation for Antenna Array Systems With Low Resolution ADCs

Employing low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays at the receivers has drawn considerable interests in the millimeter wave (mm-wave) system. Since mm-wave channels are sparse in angular dimensions, exploiting the structure could reduce the number of measurements while achieving acceptable performance at the same time. Motivated by the variational Bayesian line spectral estimation (VALSE) algorithm which treats the angles as random parameters, in contrast to previous works which confine the estimate to the set of grid angle points and induce grid mismatch, this paper proposes the grid-less quantized variational Bayesian channel estimation (GL-QVBCE) algorithm for antenna array systems with low resolution ADCs. Numerical results show the near optimal performance of GL-QVBCE by comparing with the Cramer Rao bound (CRB) and the state-of-art methods.

An Efficient Object Sensor Movement using SMAC Algorithm

The hearty following of the sudden movement is a difficult assignment in the ongoing field of PC vision. For visual following different following techniques, for example, molecule channels and by utilizing Markov-Chain Monte Carlo strategy have been proposed , however these strategies lament from the neighborhood trap issue and sudden movement un certainity. In this paper, we present the Stochastic Approximation Monte Carlo testing technique into the Bayesian channel following structure for taking care of the nearby trap issue. What's more for improving the testing productivity, and propose another MCMC sampler with concentrated adjustment. This is finished by joining the SAMC examining with a thickness matrix based prescient model. The proposed technique is exceptionally viable and computationally proficient in tending to the sudden movement issue.

Massive MIMO multi-pair two-way half-duplex AF FDD relaying: channel estimation

We consider two-way amplify-and-forward FDD massive multi-input-multi-output (MIMO) relaying system, where multiple half-duplex (HD) user-pairs exchange information via a shared HD relay equipped with massive MIMO. To design and study the performance of these systems, we require channel state information in uplink as well as in downlink. For the uplink of the system model, we use conventional low-complexity MMSE estimator for the channel estimation, wherein the number of orthogonal pilots required for uplink channel estimation are of the order of the number of users. However, in the massive MIMO relay systems, the downlink channel estimation becomes challenging, because of increased pilot overhead in proportion to the number of antennas at the relay and employing conventional channel estimation approaches will result in poor spectral efficiency. The massive MIMO channels exhibits sparse structure in the angular domain, due to limited scattering environment. We exploit this channel sparsity to reduce the pilot overhead by performing sparse Bayesian learning-based downlink channel estimation. Furthermore, practical massive MIMO systems are built with low-cost hardware components which makes the massive MIMO system prone to hardware impairments like phase and quantization errors. In this paper, we numerically analyze the effect of hardware impairments on the two-way multi-pair half-duplex massive MIMO relay systems.

Bayesian Channel Estimation Algorithms for Massive MIMO Systems With Hybrid Analog-Digital Processing and Low-Resolution ADCs

We address the problem of channel estimation in massive multiple-input multiple-output (Massive MIMO) systems where both hybrid analog-digital processing and low-resolution analog-to-digital converters (ADCs) are utilized. The hardware-efficient architecture is attractive from a power and cost point of view, but poses two significant channel estimation challenges. One is due to the smaller dimension of the measurement signal obtained from the limited number of radio frequency chains, and the other is the coarser measurements from the low-resolution ADCs. We address this problem by utilizing two sources of information. First, by exploiting the sparse nature of the channel in the angular domain, the channel estimate is enhanced and the required number of pilots is reduced. Second, by utilizing the transmitted data symbols as the “virtual pilots,” the channel estimate is further improved without adding more pilot symbols. The constraints imposed by the architecture, the sparsity of the channel and the data aided channel estimation are treated in a unified manner by employing a Bayesian formulation. The quantized sparse channel estimation is formulated into a sparse Bayesian learning framework, and solved using the variational Bayesian method. Simulation results show that the proposed algorithm can efficiently estimate the channel even with the architectural constraints, and that significant improvements are enabled by leveraging the transmitted data symbols.