Compressed Sensing Based Channel Estimation and Open-loop Training Design for Hybrid Analog-digital Massive MIMO Systems

Ardah, Khaled; Sokal, Bruno; Almeida, André L. F. de; Haardt, Martin

doi:10.1109/icassp40776.2020.9054443

Cited by 8 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By applying the vec{Adiag{b}C} = (C T A)b property, where denotes the Khatri-Rao product, the vectorized form of Y can be expressed as (12) where n = vec{N}. Therefore, (11) can be rewritten as…”

Section: Proposed Gomp For Doa Estimation a The Single-source Casementioning

confidence: 99%

“…From the above, it is clear that the estimation accuracy and the complexity of the proposed GOMP method (i.e., Algorithm 2) is highly dependent on the initial on-grid estimates ν (0) and X (0) . Specifically, we have observed that if the OMP method correctly approximates the true frequencies ν to its nearest on-grid points ν (0) (i.e., ν [1] Note that (3) can be written in a sparse-form as [11]: Y ≈ Φ Å(ν) X + N ∈ C N ×L , where Å(ν) = [a(ν 1 ), . .…”

Section: Proposed Egd For Projection Matrix Designmentioning

confidence: 99%

“…In CS-based methods, the sensing matrix should be designed carefully so that it satisfies a certain property, e.g., the mutual coherence to enhance recoverability of desired signal [4]- [6], [11]. In general, the sensing matrix comprises a tunable N × M projection matrix and a fixed M × P dictionary matrix, where N , M , and P , with N ≤ M ≤ P , denote the number of dedicated radio-frequency (RF) chains, the number of array elements, and the number of sampling (grid) points, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compressed Sensing Constant Modulus Constrained Projection Matrix Design and High-Resolution DoA Estimation Methods

Ardah¹,

Haardt²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

This paper proposes a compressed sensing-based high-resolution direction-of-arrival estimation method called gradient orthogonal matching pursuit (GOMP). It contains two main steps: a sparse coding approximation step using the wellknown OMP method and a sequential iterative refinement step using a newly proposed gradient-descent method. To enhance the recoverability, we further propose an efficient projection matrix design method, which considers the constant modulus constraints imposed by the projection matrix hardware components. Simulation results show the effectiveness of the proposed methods as compared to benchmark algorithms.

show abstract

Section: Proposed Gomp For Doa Estimation a The Single-source Casementioning

confidence: 99%

Section: Proposed Egd For Projection Matrix Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Compressed Sensing Constant Modulus Constrained Projection Matrix Design and High-Resolution DoA Estimation Methods

Ardah¹,

Haardt²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…, 𝝍 𝑃 }. Due to its convexity, Problem ( 27) can be efficiently solved using existing techniques, e.g., using the proposed method in [38], as we have shown in [5,6]. Alternatively, we can resort to the relaxed semidefinite programming (SDP) approach, by dropping the rank-one constraint, and write Problem (27) as max…”

Section: [𝑘 𝑗 ]mentioning

confidence: 99%

Recovery under Side Constraints

Ardah¹,

Haardt²,

Liu³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

This chapter addresses sparse signal reconstruction under various types of structural side constraints with applications in multi-antenna systems. Side constraints may result from prior information on the measurement system and the sparse signal structure. They may involve the structure of the sensing matrix, the structure of the non-zero support values, the temporal structure of the sparse representation vector, and the nonlinear measurement structure. First, we demonstrate how a priori information in form of structural side constraints influence recovery guarantees (null space properties) using ℓ 1 -minimization. Furthermore, for constant modulus signals, signals with row-, block-and rank-sparsity, as well as non-circular signals, we illustrate how structural prior information can be used to devise efficient algorithms with improved recovery performance and reduced computational complexity. Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals. Moreover, we discuss the linear mixing matrix design based on coherence minimization. Then we extend our focus to nonlinear measurement systems where we design parallel optimization algorithms to efficiently compute stationary points in the sparse phase retrieval problem with and without dictionary learning.

show abstract

“…K. Ardah is with Lenovo (Deutschland) GmbH (e-mail: kardah@lenovo.com). low-rank (sparse) nature of mm-wave MIMO channels [4], where several techniques, e.g., compressed sensing [9], [10] and ESPRIT [11] can be readily employed to obtain a high CSI estimation accuracy while requiring a small number of training overhead. Differently, in sub-6 GHz-based systems, the MIMO channels often experience a high-rank nature, which makes most, if not all, mm-wave-based MIMO channel estimation methods unfeasible.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Channel Estimation and Reflection Design for RIS-Aided Millimeter-Wave MIMO Communication Systems

Gherekhloo¹,

Ardah²,

Almeida³

et al. 2021

2021 55th Asilomar Conference on Signals, Systems, and Computers

Self Cite

View full text Add to dashboard Cite

In this paper, we consider the channel estimation problem in sub-6 GHz uplink wideband MIMO-OFDM communication systems, where a user equipment with a fully-digital beamforming structure is communicating with a base station having a hybrid analog-digital beamforming structure. A novel channel estimation method called Sequential Alternating Least Squares Approximation (SALSA) is proposed by exploiting a hidden tensor structure in the uplink measurement matrix. Specifically, by showing that any MIMO channel matrix can be approximately decomposed into a summation of R factor matrices having a Kronecker structure, the uplink measurement matrix can be reshaped into a 3-way tensor admitting a Tucker decomposition. Exploiting the tensor structure, the MIMO channel matrix is estimated sequentially using an alternating least squares method. Detailed simulation results are provided showing the effectiveness of the proposed SALSA method as compared to the classical least squares method.

show abstract

Compressed Sensing Based Channel Estimation and Open-loop Training Design for Hybrid Analog-digital Massive MIMO Systems

Cited by 8 publications

References 19 publications

Compressed Sensing Constant Modulus Constrained Projection Matrix Design and High-Resolution DoA Estimation Methods

Compressed Sensing Constant Modulus Constrained Projection Matrix Design and High-Resolution DoA Estimation Methods

Recovery under Side Constraints

Tensor-Based Channel Estimation and Reflection Design for RIS-Aided Millimeter-Wave MIMO Communication Systems

Contact Info

Product

Resources

About