Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems

Ma, Xisuo; Gao, Zhen; Gao, Feifei; Renzo, Marco Di

doi:10.1109/jsac.2021.3087269

Cited by 86 publications

(30 citation statements)

References 41 publications

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“…One approach is to use a machine-learning algorithm as a replacement to one or more components of the system. Examples include [28] for channel equalization, [15] for channel encoder and decoder, [7,8,14] for channel decoder, and [29][30][31] for channel estimation. A second approach is to modify an existing algorithm by incorporating DNNs.…”

Section: Other Related Workmentioning

confidence: 99%

Learning Maximum Margin Channel Decoders

Tsvieli¹,

Weinberger²

2022

Preprint

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The problem of learning a channel decoder is considered for two channel models. The first model is an additive noise channel whose noise distribution is unknown and nonparametric. The learner is provided with a fixed codebook and a dataset comprised of independent samples of the noise, and is required to select a precision matrix for a nearest neighbor decoder in terms of the Mahalanobis distance. The second model is a non-linear channel with additive white Gaussian noise and unknown channel transformation. The learner is provided with a fixed codebook and a dataset comprised of independent input-output samples of the channel, and is required to select a matrix for a nearest neighbor decoder with a linear kernel. For both models, the objective of maximizing the margin of the decoder is addressed. Accordingly, for each channel model, a regularized loss minimization problem with a codebook-related regularization term and hinge-like loss function is developed, which is inspired by the support vector machine paradigm for classification problems. Expected generalization error bounds for the error probability loss function are provided for both models, under optimal choice of the regularization parameter. For the additive noise channel, a theoretical guidance for choosing the training signal-to-noise ratio is proposed based on this bound. In addition, for the non-linear channel, a high probability uniform generalization error bound is provided for the hypothesis class.For each channel, a stochastic sub-gradient descent algorithm for solving the regularized loss minimization problem is proposed, and an optimization error bound is stated. The performance of the proposed algorithms is demonstrated through several examples.

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Section: Other Related Workmentioning

confidence: 99%

Learning Maximum Margin Channel Decoders

Tsvieli¹,

Weinberger²

2022

Preprint

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“…Q. Wan et al proposed an unrolled deep learning architecture based on inverse-free variational Bayesian learning framework for MIMO detection [28]. X. Ma et al proposed a model-driven channel estimation and feedback learning scheme for wideband millimeter-wave massive hybrid MIMO systems [29]. Although these works achieve a better performance with lower computational complexity than that of the traditional model-driven methods, they are still lack of the ability of solving the optimization problems with complex constraints.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks

He¹,

Xiong²,

An³

et al. 2022

Preprint

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In wireless network, the optimization problems generally have complex constraints, and are usually solved via utilizing the traditional optimization methods that have high computational complexity and need to be executed repeatedly with the change of network environments. In this paper, to overcome these shortcomings, an unsupervised deep unrolling framework based on projection gradient descent, i.e., unrolled PGD network (UPGDNet), is designed to solve a family of constrained optimization problems. The set of constraints is divided into two categories according to the coupling relations among optimization variables and the convexity of constraints. One category of constraints includes convex constraints with decoupling among optimization variables, and the other category of constraints includes non-convex or convex constraints with coupling among optimization variables. Then, the first category of constraints is directly projected onto the feasible region, while the second category of constraints is projected onto the feasible region using neural network. Finally, an unrolled sum rate maximization network (USRMNet) is designed based on UPGDNet to solve the weighted SR maximization problem for the multiuser ultra-reliable low latency communication system. Numerical results show that USRMNet has a comparable performance with low computational complexity and an acceptable generalization ability in terms of the user distribution.

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“…The reliable channel state information (CSI) acquisition is indispensable for the quality-of-service (QoS) of this spaceto-air scenario [5]- [7]. However, the high-speed mobility of UAVs and satellites makes accurate CSI acquisition rather challenging.…”

Section: Introductionmentioning

confidence: 99%

Angle Estimation for Terahertz Ultra-Massive MIMO-Based Space-to-Air Communications

Liao

Gao

Yang³

et al. 2021

Preprint

Self Cite

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This paper investigates terahertz ultra-massive (UM)-MIMO-based angle estimation for space-to-air communications, which can solve the performance degradation problem caused by the dual delay-beam squint effects of terahertz UM-MIMO channels. Specifically, we first design a grouping truetime delay unit module that can significantly mitigate the impact of delay-beam squint effects to establish the space-to-air THz link. Based on the subarray selection scheme, the UM hybrid array can be equivalently considered as a low-dimensional fullydigital array, and then the fine estimates of azimuth/elevation angles at both UAVs and satellite can be separately acquired using the proposed prior-aided iterative angle estimation algorithm. The simulation results that close to Cramér-Rao lower bounds verify the effectiveness of our solution.

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Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems

Cited by 86 publications

References 41 publications

Learning Maximum Margin Channel Decoders

Learning Maximum Margin Channel Decoders

An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks

Angle Estimation for Terahertz Ultra-Massive MIMO-Based Space-to-Air Communications

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