Compressive Channel Estimation and Multi-User Detection in C-RAN With Low-Complexity Methods

He, Qi; Quek, Tony Q. S.; Chen, Zhi; Zhang, Qi; Li, Shaoqian

doi:10.1109/twc.2018.2818125

Cited by 34 publications

(20 citation statements)

References 30 publications

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“…However, such large scale optimization easily converged to a bad local minimum [40]. The layer-wise training strategy is used to separate (16) into the following two parts, i.e., (17) and (18), where (17) is to find a good initialization of (18) to avoid bad local minima of (16). In particular, for training a k-layer network, we denote Θ 0:k−1 as the trainable parameters from layer 0 to layer k − 1, and Θ k−1 as the trainable parameters in layer k − 1.…”

Section: B Deep Neural Network Training and Testingmentioning

confidence: 99%

Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee

Shi¹,

Choi²,

Shi³

et al. 2021

Preprint

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Massive access is a critical design challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multiple-antenna base station (BS) and a large number of single-antenna IoT devices. Taking into account the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. This problem can be solved by applying the existing compressed sensing techniques, which however either suffer from high computational complexities or lack of algorithm robustness. To this end, we propose a novel algorithm unrolling framework based on the deep neural network to simultaneously achieve low computational complexity and high robustness for solving the JADCE problem. Specifically, we map the original iterative shrinkage thresholding algorithm (ISTA) into an unrolled recurrent neural network (RNN), thereby improving the convergence rate and computational efficiency through end-to-end training. Moreover, the proposed algorithm unrolling approach inherits the structure and domain knowledge of the ISTA, thereby maintaining the algorithm robustness, which can handle non-Gaussian preamble sequence matrix in massive access. With rigorous theoretical analysis, we further simplify the unrolled network structure by reducing the redundant training parameters. Furthermore, we prove that the simplified unrolled deep neural network structures enjoy a linear convergence rate. Extensive simulations based on various preamble signatures show that the proposed unrolled networks outperform the existing methods in terms of the convergence rate, robustness and estimation accuracy.

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Section: B Deep Neural Network Training and Testingmentioning

confidence: 99%

Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee

Shi¹,

Choi²,

Shi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Problem ( 2) is a non-differentiable optimization problem for which classical sub-gradient methods often exhibit a rather slow convergence. As discussed in Section I, several first-order methods have been applied to solve (2) such as FISTA [7], ProxGradient [8] and ADMM [9], [5]. However, in order to achieve fast convergence, these methods require either a centralized step such as the line search routine in the Prox-Gradient method or a pre-conditioner that scales all variables in advance.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…Algorithm 1 outlines a tailored version of ALADIN [10] for solving (5). The algorithm also has two main steps, a parallelizable step and a consensus step.…”

Section: A Augmented Lagrangian Based Alternating Direction Inexact N...mentioning

confidence: 99%

“…Another option is to apply Proximal Gradient (ProxGradient) methods [8], which can distribute the main iteration but require a centralized line search procedure. Moreover, [5] applied the Alternating Direction Method of Multipliers (ADMM) [9] to solve the Joint Activity Detection and Channel Estimation (JADCE) problem in cloud radio access network. By exploiting the distributed structure, the numerical simulations show that ADMM can reduce the computational cost significantly.…”

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confidence: 99%

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Distributed Optimization for Massive Connectivity

Jiang¹,

Su²,

Shi³

et al. 2020

Preprint

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Massive device connectivity in Internet of Thing (IoT) networks with sporadic traffic poses significant communication challenges. To overcome this challenge, the serving base station is required to detect the active devices and estimate the corresponding channel state information during each coherence block. The corresponding joint activity detection and channel estimation problem can be formulated as a group sparse estimation problem, also known under the name "Group Lasso". This letter presents a fast and efficient distributed algorithm to solve such Group Lasso problems, which alternates between solving smallscaled problems in parallel and dealing with a linear equation for consensus. Numerical results demonstrate the speedup of this algorithm compared with the state-of-the-art methods in terms of convergence speed and computation time.

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“…However, AMP algorithms often fail to converge when the preamble signature matrix is mildly ill-conditioned or non-Gaussian [11]. The authors in [12], [13] introduced a mixed 𝑙 1 /𝑙 2 -norm convex relaxation method to reformulate the problem as a form of group LASSO. Many methods can be used to solve the LASSO problem, such as interior-point [12] method and iterative shrinkage thresholding algorithm (ISTA) [14] method.…”

Section: Introductionmentioning

confidence: 99%

Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming

Liang¹,

Shi²,

Zhou³

2021

Preprint

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Massive connectivity is a critical challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multi-antenna base station (BS) and a large number of singleantenna IoT devices. Due to the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. Although many algorithms have been proposed to solve the JADCE problem, most of them are developed based on compressive sensing technique, yielding suboptimal solutions. In this paper, we first develop an efficient weighted 𝑙 1 -norm minimization algorithm to better approximate the group sparsity than the existing mixed 𝑙 1 /𝑙 2 -norm minimization. Although an enhanced estimation performance in terms of the mean squared error (MSE) can be achieved, the weighted 𝑙 1 -norm minimization algorithm is still a convex relaxation of the original group-sparse matrix estimation problem, yielding a suboptimal solution. To this end, we further reformulate the JADCE problem as a mixed integer programming (MIP) problem, which can be solved by using the branch-and-bound method. As a result, we are able to obtain an optimal solution of the JADCE problem, which can be adopted as an upper bound to evaluate the effectiveness of the existing algorithms. Moreover, we also derive the minimum pilot sequence length required to fully recover the estimated matrix in the noiseless scenario. Simulation results show the performance gains of the proposed optimal algorithm over the proposed weighted 𝑙 1 -norm algorithm and the conventional mixed 𝑙 1 /𝑙 2norm algorithm. Results also show that the proposed algorithms require a short pilot sequence than the conventional algorithm to achieve the same estimation performance.

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Compressive Channel Estimation and Multi-User Detection in C-RAN With Low-Complexity Methods

Cited by 34 publications

References 30 publications

Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee

Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee

Distributed Optimization for Massive Connectivity

Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming

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