Model-Driven Beamforming Neural Networks

Xia, Wenchao; Zheng, Gan; Wong, Kai-Kit; Zhu, Hongbo

doi:10.1109/mwc.001.1900239

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…To address this issue, TL can be a potential solution. Particularly, a DL model consisting of a conventional CNN and a signal processing module is proposed in [170]. The signal processing module can be placed before the input or after the output of the CNN to convert key features from expert knowledge to the target beamforming matrix.…”

Section: B Future Research Directionsmentioning

confidence: 99%

“…Since the SINR load balancing problem has some common features across different wireless environments, a transductive TL process is proposed to utilize the knowledge from different environments. Different from the TL approach in [170], [169] proposes to transfer a pre-trained model to the target task. Specifically, all the layers except the fully connected layer, of the pre-trained network are frozen, and the target data is only used to train the fully connected layer.…”

Section: B Future Research Directionsmentioning

confidence: 99%

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Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

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With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, will impede the effectiveness and applicability of ML in future wireless networks. To address these problems, Transfer Learning (TL) has recently emerged to be a very promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks as well as from valuable experiences accumulated from the past to facilitate the learning of new problems. Doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on applications of TL in wireless networks. Particularly, we first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, localization, signal recognition, security, human activity recognition and caching, which are all important to nextgeneration networks such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

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Section: B Future Research Directionsmentioning

confidence: 99%

Section: B Future Research Directionsmentioning

confidence: 99%

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

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“…Recent advancements in model-driven deep learning approaches in physical layer communications were discussed in [19]. In our previous works [7], [20], we have proposed the model-driven neural network design for beamforming optimization by exploiting the problem structure. Deep neural networks that adopt the algorithmic structure and constraints of adaptive signal processing techniques were proposed in [21] that can efficiently learn to perform fast high-quality ultrasound beamforming by using very few training data.…”

Section: Introductionmentioning

confidence: 99%

Model-Driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information

Zhang

You

Zheng

et al. 2022

IEEE Trans. Wireless Commun.

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Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.

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“…The weights of the ANNs are trained, mainly by means of gradient descent-based algorithms, to learn and later generalize the actual correspondence between the input and output sets. Several works have proposed the use of supervised DL approaches to solve different challenging problems in IRS-assisted MIMO communications like channel estimation [27]- [33], beamforming [34], [35] and IRS phase-shift matrix optimization [36]. However, the performance of supervised DL approaches is highly dependent on the data sets available for the training process.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Approach to IRS-assisted MU-MIMO Communication Systems

Pereira-Ruisánchez¹,

Fresnedo²,

Pérez-Adán³

et al. 2022

Preprint

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<div>The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) framework is proposed to solve the joint optimization of the IRS phase-shift matrix and the precoding matrix in an IRS-assisted multi-stream multi-user MIMO communication.<br></div><div><br></div><div>The combination of multiple-input multiple-output(MIMO) communications and intelligent reflecting surfaces(IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6Gsystems. In this work, we develop an innovative deep reinforcement learning (DRL)-based approach to the joint optimization of the MIMO precoders and the IRS phase-shift matrices that is proved to be efficient in high dimensional systems. The proposed approach is termed deep deterministic policy gradient (DDPG)and maximizes the sum rate of an IRS-assisted multi-stream(MS) multi-user MIMO (MU-MIMO) system by learning the best matrix configuration through online trial-and-error interactions. The proposed approach is formulated in terms of continuous state and action spaces, and a sum-rate-based reward function. The computational complexity is reduced by using artificial neural networks (ANNs) for function approximations and it is shown that the proposed solution scales better than other state-of-the-art methods, while reaching a competitive performance.<br></div>

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Model-Driven Beamforming Neural Networks

Cited by 17 publications

References 16 publications

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Model-Driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information

A Deep Reinforcement Learning Approach to IRS-assisted MU-MIMO Communication Systems

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