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

  title={Grid-Less Variational Bayesian Channel Estimation for Antenna Array Systems With Low Resolution ADCs},
  author={Jiang Zhu and Chao-Kai Wen and Jun Tong and Chongbin Xu and Shi Jin},
  journal={IEEE Transactions on Wireless Communications},
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… 

Low-rank and Angular Structures aided mmWave MIMO Channel Estimation with Few-bit ADCs

This paper develops a two stage approach for mmWave channel estimation, namely, a low rank matrix recovery stage and a gridless angle recovery stage, which improves the channel estimation performance.

Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array

A new scenario of direction of arrival estimation using massive multiple-input multiple-output receive array with low-resolution analog-to-digital convertors (ADCs), which can strike a good balance between performance and circuit cost is presented.

Doubly Selective Channel Estimation Algorithms for Millimeter Wave Hybrid MIMO Systems

Three new compressive sensing-based algorithms for channel estimation in millimeter wave hybrid MIMO systems over doubly selective channels can significantly improve the mean squared error performance compared with the state-of-the-art approaches.

Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL

The state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems.

Efficient Majorization-Minimization-Based Channel Estimation for One-Bit Massive MIMO Systems

A computationally efficient majorization-minimization based maximum likelihood (MM) based channel matrix estimator (referred to as 1bMM-ML), which maximizes the one-bit likelihood function iteratively by solving simple linear least squares problems.

MIMO Channel Estimation with Non-Ideal ADCS: Deep Learning Versus GAMP

DL applied to channel estimation of MIMO systems with low resolution analog-to-digital converters (ADCs) indicates that a single neural network trained in a range of practical conditions is more robust to ADC impairments than a GAMP variant.

Performance Analysis of Massive MIMO Relay Systems With Variable-Resolution ADCs/DACs Over Spatially Correlated Channels

This paper investigates the performance of massive MIMO relay systems with a variable-resolution ADC/DAC-based architecture, and finds that the achievable rate is optimal over spatially correlated channels when all ADC and DAC adopt the same quantization bits from the perspective of statistic.

DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency

The numerical results reveal that the HAD structure with mixedADCs can significantly reduce the power consumption and hardware cost and that that architecture is able to achieve a better trade-off between the performance loss and thePower consumption.

On Performance Loss of DOA Measurement Using Massive MIMO Receiver With Mixed-ADCs

Simulation results show that the mixed-ADC architecture can strike a good balance among performance loss, circuit cost and energy efficiency, and just a few bits of low-resolution ADCs can achieve a satisfactory performance for DOA measurement.

A Cramér-Rao Bound Analysis for mmWave PMCW MIMO Radar with Quantized Observations

The Cramér-Rao bound (CRB) is derived for jointly estimating targets’ amplitudes, time delays, Doppler shifts, and directions in mmWave phase modulated continuous wave radar with quantized observations.



Gridless Channel Estimation for Mixed One-Bit Antenna Array Systems

This research considers the channel estimation problem to fill this gap and proposes a two-step channel estimator by utilizing the different features of mixed outputs, which yields significantly lower mean square errors than the conventional maximum likelihood estimator.

Compressive sensing based time-varying channel estimation for millimeter wave systems

This paper proposes an efficient sparse channel estimation scheme based on compressive sensing (CS) theory, considering that the angles of arrival/departure (AoAs/AoDs) vary more slowly than the path gains, and formulate the channel estimation into a block-sparse signal recovery problem.

Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO With Low-Precision ADCs

A Bayes-optimal JCD estimator is developed using a recent technique based on approximate message passing that allows the efficient evaluation of the performance of quantized massive MIMO systems and provides insights into effective system design.

Gridless variational Bayesian inference of line spectral from quantized samples

An efficient grid-less Bayesian algorithm named VALSE-EP is proposed, which is a combination of the high resolution and low complexity gridless variational line spectral estimation (VALSE) and expectation propagation (EP).

Grid-less Variational Bayesian Inference of Line Spectral Estimation from Quantized Samples

The basic idea of VALSE-EP is to iteratively approximate the challenging quantized model of line spectral estimation as a sequence of simple pseudo unquantized models, so that the VALSE algorithm can be applied.

Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Successive Interference Cancellation

The results indicate that for uplink massive MIMO systems with low-resolution ADCs, the radio-frequency circuit power consumption can be significant because a large number of antennas are required to compensate for the loss due to quantization errors while the number of base station antennas needed with the ZF-SIC receiver is significantly smaller than that with theZF receiver.

Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach

Simulation results show that the proposed method renders reliable beam alignment even in the low signal-to-noise ratio regime and presents a clear performance advantage over existing methods.

A Discretization-Free Sparse and Parametric Approach for Linear Array Signal Processing

An exact discretization-free method, named as sparse and parametric approach (SPA), is proposed for uniform and sparse linear arrays that carries out parameter estimation in the continuous range based on well-established covariance fitting criteria and convex optimization and is statistically consistent under uncorrelated sources.

Grid-less variational Bayesian line spectral estimation with multiple measurement vectors

Quantized Spectral Compressed Sensing: Cramer–Rao Bounds and Recovery Algorithms

  • H. FuYuejie Chi
  • Computer Science
    IEEE Transactions on Signal Processing
  • 2018
A new algorithm based on atomic norm soft thresholding for signal recovery, which is equivalent to proximal mapping of properly designed surrogate signals with respect to the atomic norm that motivates spectral sparsity is proposed.