Adaptive Split Bregman for sparse and low rank massive MIMO channel estimation

Nasser, Ahmed; Elsabrouty, Maha

doi:10.1109/ict.2016.7500453

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This scheme is studied in different literature works, e.g. [6], [7], where different CS recovery algorithms are proposed as structured subspace pursuit in [6], and split Bregman in [7]. These CS algorithms are proposed to recover single measurement vector (SMV) channels, so they are called SMV CS algorithms.…”

Section: A Existing Research Workmentioning

confidence: 99%

FDD Cooperative Channel Estimation and Feedback for 3D Massive MIMO System

2019

Self Cite

View full text Add to dashboard Cite

In this paper, a downlink cooperative channel estimation scheme is proposed for the threedimensional massive multiple inputs multiple outputs (3D-mMIMO) system operating in the frequency division duplexing (FDD) mode. In the proposed cooperative scheme, users have to cooperate with each other via device-to-device (D2D) communication protocol to jointly exploit the sparsity structure property of the channel. Motivated by the sparsity property of the mMIMO channel in the angle-time domain, a parametric feedback scheme is proposed, where the feedback overhead is decreased by sending a limited version of the estimated coefficients rather than all the coefficients back to the BS. Then, a compressive sensing (CS) algorithm is proposed, which we named weighted fast iterative shrinkage thresholding (WFISTA). In the WFISTA, we first introduce new weights and threshold function to the original FISTA to enhance its sparsity-undersampling trade-off in the single measurement vector (SMV) case, then we extend the proposed WFISTA to the case of multiple measurement vector (MMV) problem by adopting ReMBo (reduce MMV and boost) strategy. The proposed WFISTA has the ability to estimate the mMIMO system's channel coefficients by exploiting the joint sparsity structure through cooperation among users' equipment (UEs). Complexity analysis and the probability of decreasing the feedback overhead are provided for the proposed cooperative estimation scheme. The simulation results verify the efficiency of the proposed cooperative algorithm scheme compared to several joint channel estimations. INDEX TERMS Channel estimation, device to device (D2D) communication, compressive sensing, fast iterative shrinkage thresholding algorithm (FISTA), massive multiple input multiple output (mMIMO), multiple measurement vectors (MMV), frequency division duplexing (FDD).

show abstract

Section: A Existing Research Workmentioning

confidence: 99%

FDD Cooperative Channel Estimation and Feedback for 3D Massive MIMO System

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Split Bregman method (SBM) proposed in [28] is a universal convex optimization algorithm for both l 1 -norm and TV-norm regularization problems. By the idea of decomposing the original problem into several subproblems worked out by Bregman Iteration (BI) [29,30], SBM has been widely utilized in the complex domain through the complex-to-real converting technique [31,32], e.g., MRI imaging [33], SAR imaging [34], forward-looking scanning radar imaging [35], SAR image super-resolution [36], and massive MIMO channel estimation [37]. However, SBM still has great potential in terms of both reconstruction performance and time cost considering the following two points: The original BI defined in the real domain may not make good use of the phase information for complex variables, which degrades the recovery accuracy; secondly, the converting technique quadruples the elements of the sensing matrix A to 2 m × 2 n , which consumes more memory and time within the iteration process.…”

Section: Introductionmentioning

confidence: 99%

A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method

Xiong

Zhao

Shi

et al. 2019

Sensors

View full text Add to dashboard Cite

The Split Bregman method (SBM), a popular and universal CS reconstruction algorithm for inverse problems with both l1-norm and TV-norm regularization, has been extensively applied in complex domains through the complex-to-real transforming technique, e.g., MRI imaging and radar. However, SBM still has great potential in complex applications due to the following two points; Bregman Iteration (BI), employed in SBM, may not make good use of the phase information for complex variables. In addition, the converting technique may consume more time. To address that, this paper presents the complex-valued Split Bregman method (CV-SBM), which theoretically generalizes the original SBM into the complex domain. The complex-valued Bregman distance (CV-BD) is first defined by replacing the corresponding regularization in the inverse problem. Then, we propose the complex-valued Bregman Iteration (CV-BI) to solve this new problem. How well-defined and the convergence of CV-BI are analyzed in detail according to the complex-valued calculation rules and optimization theory. These properties prove that CV-BI is able to solve inverse problems if the regularization is convex. Nevertheless, CV-BI needs the help of other algorithms for various kinds of regularization. To avoid the dependence on extra algorithms and simplify the iteration process simultaneously, we adopt the variable separation technique and propose CV-SBM for resolving convex inverse problems. Simulation results on complex-valued l1-norm problems illustrate the effectiveness of the proposed CV-SBM. CV-SBM exhibits remarkable superiority compared with SBM in the complex-to-real transforming technique. Specifically, in the case of large signal scale n = 512, CV-SBM yields 18.2%, 17.6%, and 26.7% lower mean square error (MSE) as well as takes 28.8%, 25.6%, and 23.6% less time cost than the original SBM in 10 dB, 15 dB, and 20 dB SNR situations, respectively.

show abstract

Adaptive Split Bregman for sparse and low rank massive MIMO channel estimation

Cited by 2 publications

References 17 publications

FDD Cooperative Channel Estimation and Feedback for 3D Massive MIMO System

FDD Cooperative Channel Estimation and Feedback for 3D Massive MIMO System

A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method

Contact Info

Product

Resources

About