On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

Sapavath, Naveen Naik; Safavat, Sunitha; Rawat, Danda B.

doi:10.1002/ett.3713

Cited by 17 publications

(9 citation statements)

References 20 publications

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“…Typically in some studies that use machine learning for solving channel estimation problems, the required dataset for training the desired network has been made by creating artificial data samples. For creating artificially labeled data, all the parameters of the network should be known 6,7 . In particular, in the channel estimation approach, the channel type, channel distribution, and the range of signal-to-noise ratio (SNR) need to be available.…”

Section: Introductionmentioning

confidence: 99%

RNN-FL-based Channel Estimation Approach in mmWave Massive MIMO Systems

Abouei

Shahabodini

Mansoori

et al. 2023

Preprint

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So far, various data-driven approaches have been presented to obtain channel state information (CSI) in mmWave multiple-input-multiple-output (MIMO) wireless networks. In almost all previous works, training and testing channels were assumed to have the same distribution, which may not be the case in practice. In this paper, we address this challenge, by proposing a learning framework that is a combination of a long short-term memory (LSTM) network and a deep neural network (DNN) for estimating CSI in a dynamic wireless communication environment. Furthermore, we use federated learning (FL) to train the learning-based channel estimation (CE) model. More specifically, we introduce a two-stage downlink pilot transmission procedure, where in the initial stage, long frame length downlink pilot signals are used to train the introduced RNN-DNN model. Following that, users will receive shorter-frame-length pilot signals that can be used for CSI estimation. To speed up the training procedure of the proposed network, we first generate a pre-trained model and then modify it according to the collected data samples. Simulation results demonstrate that, when the channel distribution is unavailable, the proposed approach performs significantly better than the most recent channel estimation algorithms in terms of estimation performance and computational complexity.

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Section: Introductionmentioning

confidence: 99%

RNN-FL-based Channel Estimation Approach in mmWave Massive MIMO Systems

Abouei

Shahabodini

Mansoori

et al. 2023

Preprint

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“…In a similar manner, the estimation of large MIMO channels was facilitated by networks trained on pilot signals in [90], [91]. Arguably, in the most common scenario in the literature, deep learning methods have been tasked with learning optimal digital, analog, or hybrid digital/analog beamforming practices [92], [93], [94], [95].…”

Section: Introductionmentioning

confidence: 99%

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

et al. 2022

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“…In a similar manner, the estimation of large MIMO channels was facilitated by networks trained on pilot signals in [99], [100]. Arguably, in the most common scenario in the literature, deep learning methods have been tasked with learning optimal digital, analog, or hybrid digital/analog beamforming practices [101]- [104].…”

Section: Introductionmentioning

confidence: 99%

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

Alexandropoulos¹,

Stylianopoulos²,

Huang³

et al. 2022

Preprint

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The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardwareefficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces with limited, or even the absence of, computing hardware. In this paper, we consider multi-user and multi-RIS-empowered wireless systems, and present a thorough survey of the online machine learning approaches for the orchestration of their various tunable components. Focusing on the sum-rate maximization as a representative design objective, we present a comprehensive problem formulation based on Deep Reinforcement Learning (DRL). We detail the correspondences among the parameters of the wireless system and the DRL terminology, and devise generic algorithmic steps for the artificial neural network training and deployment, while discussing their implementation details. Further practical considerations for multi-RIS-empowered wireless communications in the sixth Generation (6G) era are presented along with some key open research challenges. Differently from the DRL-based status quo, we leverage the independence between the configuration of the system design parameters and the future states of the wireless environment, and present efficient multi-armed bandits approaches, whose resulting sum-rate performances are numerically shown to outperform random configurations, while being sufficiently close to the conventional Deep Q-Network (DQN) algorithm, but with lower implementation complexity.

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On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

Cited by 17 publications

References 20 publications

RNN-FL-based Channel Estimation Approach in mmWave Massive MIMO Systems

RNN-FL-based Channel Estimation Approach in mmWave Massive MIMO Systems

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

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