Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming

Kang, Kai; Hu, Qiyu; Yu, Guanding; Hoydis, Jakob; Eldar, Yonina C.

doi:10.1109/jsac.2022.3191124

Cited by 10 publications

(3 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The modules in the transceiver are usually highly correlated with each other, and thus a joint design can achieve better performance than a separate design. To fulfill the global optimization, several DL-based end-to-end communication systems have been proposed [27][28][29][30][31][32], where all the trainable parameters are updated based on an end-to-end loss. A DNNbased based end-to-end wireless communication system has been proposed in ref.…”

Section: End-to-end Learningmentioning

confidence: 99%

“…[28], the authors of ref. [31] designed a model-driven based endto-end framework for joint transceiver design in time division duplexing (TDD) systems, which consists of a channel estimation deep-unfolding NN (CEDUN) and a hybrid beamforming deep-unfolding NN (HBDUN). As shown in Figure 5, the CEDUN is comprised of a pilot training NN and a recursive least squares (RLS) algorithm-induced deep-unfolding NN, where a set of trainable parameters are introduced to increase the degrees of freedom.…”

Section: End-to-end Learningmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for MIMO Communications

Cai,

Hu,

Zhang

et al. 2023

MIMO Communications - Fundamental Theory, Propagation Channels, and Antenna Systems

Self Cite

View full text Add to dashboard Cite

Recently, deep learning (DL) is becoming a key feature of next-generation multiple-input multiple-output (MIMO) transceiver design with learning and inference capabilities embedded in the network, which achieves greatly enhanced system performance. Popular topics include end-to-end (E2E) learning for transceiver design, deep reinforcement learning (DRL) for communications, and model-driven deep unfolding techniques. In particular, E2E learning treats the communication system design as an E2E data reconstruction task that seeks to jointly optimize transceiver components, so that encoding and decoding are fostered by the learned weights of deep neural networks (DNN). E2E learning can be employed to solve various problems in MIMO communications, such as channel state information (CSI) feedback, beamforming, signal detection, and channel estimation. Moreover, DRL has been widely applied to solve high-dimensional non-convex optimization problems in designing the transceiver. However, these DNNs generally suffer from an inability to be interpreted or generalized, and they often lack performance guarantees. To overcome such drawbacks, substantial researches have proposed to unfold the iterations of an iterative optimization algorithm into a layer-wise structure analogous to a DNN. Inspired by the great potential of these DL methods, it is important to investigate AI-empowered transceivers for future MIMO systems.

show abstract

Section: End-to-end Learningmentioning

confidence: 99%

Section: End-to-end Learningmentioning

confidence: 99%

Deep Learning for MIMO Communications

Cai,

Hu,

Zhang

et al. 2023

MIMO Communications - Fundamental Theory, Propagation Channels, and Antenna Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…ISTA-based deep unfolded networks are developed for high running efficiency and excellent estimation performance [19,20], while the same operation is adopted for ADMM and AMP. Moreover, RIS and SSCA algorithms are introduced for constructing deep unfolded networks [21] so that the computation complexity and signaling overhead are decreased when compared with the original algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Deep Unfolded Network for the Off-Grid Direction-of-Arrival Estimation via a Minimum Hole Array

Li,

Zhang,

et al. 2024

Electronics

View full text Add to dashboard Cite

As an important research focus in radar detection and localization, direction-of-arrival (DOA) estimation has advanced significantly owing to deep learning techniques with powerful fitting and classifying abilities in recent years. However, deep learning inevitably requires substantial data to ensure learning and generalization abilities and lacks reasonable interpretability. Recently, a deep unfolding technique has attracted widespread concern due to the more explainable perspective and weaker data dependency. More importantly, it has been proven that deep unfolding enables convergence acceleration when applied to iterative algorithms. On this basis, we rigorously deduce an iterative sparse Bayesian learning (SBL) algorithm and construct a Bayesian deep unfolded network in a one-to-one correspondence. Moreover, the common but intractable off-grid errors, caused by grid mismatch, are directly considered in the signal model and computed in the iterative process. In addition, minimum hole array, little considered in deep unfolding, is adopted to further improve estimation performance owing to the maximized array degrees of freedom (DOFs). Extensive simulation results are presented to illustrate the superiority of the proposed method beyond other state-of-the-art methods.

show abstract