Limited Feedback Double Directional Massive MIMO Channel Estimation: From Low-Rank Modeling to Deep Learning

Sun, Haoran; Zhao, Ziping; Fu, Xiao; Mei, Hong

doi:10.1109/spawc.2018.8446005

Cited by 31 publications

(34 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…with associated step sizes γ λ,k , γ µ,k > 0. The gradient primaldual updates in (16)- (19) successively move the primal and dual variables towards maximum and minimum points of the Lagrangian function, respectively.…”

Section: B Primal-dual Learningmentioning

confidence: 99%

“…In this section, we consider that often in practice, we do not have access to explicit knowledge of the functions g 0 , g, and f , along with the distribution m(h), but rather observe noisy estimates of their values at given operating points. While this renders the direct implementation of the standard primal-dual updates in (16)- (19) impossible, given their reliance on gradients that cannot be evaluated, we can use these updates to develop a model-free approximation. Consider that given any set of iterates and channel realization {θ,x,h}, we can observe stochastic function valuesĝ 0 (x),ĝ(x), and f (h, φ(h,θ)).…”

Section: Model-free Learningmentioning

confidence: 99%

“…As in (30), the backpropogation is performed with respect to the given policy constraint function f , rather than with respect to a Euclidean loss function over a set of given training data. Furthermore, due to the constraints, the backpropogation step in (16) is performed in sequence with the more standard primal and dual variable updates in (17)- (19). In this way, the DNN is trained indirectly within the broader optimization algorithm used to solve (7).…”

Section: Deep Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

Learning Optimal Resource Allocations in Wireless Systems

Eisen

Zhang

Chamon

et al. 2019

IEEE Trans. Signal Process.

231

205

View full text Add to dashboard Cite

This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constraint, motivating the development of learning methodologies to attempt their solution. To handle stochastic constraints, training is undertaken in the dual domain. It is shown that this can be done with small loss of optimality when using near-universal learning parameterizations. In particular, since deep neural networks (DNN) are near-universal their use is advocated and explored. DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parametrization of the resource allocation policy and optimizes the primal and dual variables. Numerical simulations demonstrate the strong performance of the proposed approach on a number of common wireless resource allocation problems.

show abstract

Section: B Primal-dual Learningmentioning

confidence: 99%

Section: Model-free Learningmentioning

confidence: 99%

Section: Deep Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Learning Optimal Resource Allocations in Wireless Systems

Eisen

Zhang

Chamon

et al. 2019

IEEE Trans. Signal Process.

231

205

View full text Add to dashboard Cite

show abstract

“…Deep learning has been successful in many applications such as computer vision [1], natural language processing [2], among others [3]. Recent works have also demonstrated that deep learning can be applied in communication systems, either by replacing an individual component in the system (such as signal detection [4,5], channel estimation [6,7], power allocation [8][9][10][11][12] and beamforming [13]), or by jointly optimizing the entire system [14,15], for achieving state-of-the-art performance. Specifically, deep learning is a datadriven method in which a large amount of training data is used to train a deep neural network (DNN) for a specific task (such as power control).…”

Section: Introductionmentioning

confidence: 99%

“…Once trained, such a DNN model will replace conventional algorithms to process data in real time. Existing works have shown that when the real-time data follows similar distribution as the training data, then such an approach can generate high-quality solutions for non-trivial wireless tasks [4][5][6][7][8][9][10][11][12][13], while significantly reducing real-time computation, and/or requiring only a subset of channel state information (CSI). Dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

Sun

Zhu

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

There has been a growing interest in developing data-driven, in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-ofthe-art performance while requiring less computational efforts, less channel state information (CSI), etc. However, it is often challenging for these approaches to learn in a dynamic environment where parameters such as CSIs keep changing.This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. Specifically, we consider an "episodically dynamic" setting where the environment changes in "episodes", and in each episode the environment is stationary. We propose a continual learning (CL) framework for wireless systems, which can incrementally adapt the learning models to the new episodes, without forgetting models learned from the previous episodes. Our design is based on a novel min-max formulation which ensures certain "fairness" across different episodes. Finally, we demonstrate the effectiveness of the CL approach by customizing it to a popular DNN based model for power control, and testing using both synthetic and real data.

show abstract

Downlink channel estimation for millimeter wave communication combining low-rank and sparse structure characteristics

Jin

2020

Ann. Telecommun.

View full text Add to dashboard Cite

The acquisition of channel state information (CSI) is essential in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The mmWave channel exhibits sparse scattering characteristics and a meaningful low-rank structure, which can be simultaneously employed to reduce the complexity of channel estimation. Most existing works recover the low-rank structure of channels using nuclear norm theory. However, solving the nuclear norm-based convex problem often leads to a suboptimal solution of the rank minimization problem, thus degrading the accuracy of channel estimation. Previous contributions recover the channel using over-complete dictionary with the assumption that the mmWave channel can be sparsely represented under some dictionary. While over-complete dictionary may increase the computational complexity. To address these problems, we propose a channel estimation framework based on non-convex low-rank approximation and dictionary learning by exploring the joint low-rank and sparse representations of wireless channels. We surrogate the widely used nuclear norm theory with non-convex low-rank approximation method and design a dictionary learning algorithm based on channel feature classification employing deep neural network (DNN). Our simulation results reveal the proposed scheme outperform the conventional dictionary learning algorithm, Bayesian framework algorithm, and compressed sensing-based algorithms.

show abstract

Limited Feedback Double Directional Massive MIMO Channel Estimation: From Low-Rank Modeling to Deep Learning

Cited by 31 publications

References 13 publications

Learning Optimal Resource Allocations in Wireless Systems

Learning Optimal Resource Allocations in Wireless Systems

Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

Downlink channel estimation for millimeter wave communication combining low-rank and sparse structure characteristics

Contact Info

Product

Resources

About