Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization

Zappone, Alessio; Renzo, Marco Di; Debbah, Mérouane; Tu, Lam-Thanh; Xing-san, Qian

doi:10.1109/mvt.2019.2921627

Cited by 163 publications

(104 citation statements)

References 10 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…The transmitters are uniformly distributed in the square region [0, 100] × [0, 100] meters. The receivers are uniformly distributed within [2,10] meters away from the transmitter. The adopted channel model is…”

Section: ) Varying User Locationsmentioning

confidence: 99%

See 1 more Smart Citation

A Graph Neural Network Approach for Scalable Wireless Power Control

Shen

Shi

Zhang

et al. 2019

2019 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in K-user interference channels. Specifically, a K-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one-layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE.

show abstract

Section: ) Varying User Locationsmentioning

confidence: 99%

“…We follow [4] to set up the simulation. The link distance is uniformly distributed in [2,10] meters during training. In the test, the link distance is uniformly distributed in [l r , u r ] meters, where l r is uniform in [2,20] meters and u r is uniform in [l r , 20] meters.…”

Section: ) Varying User Locationsmentioning

confidence: 99%

A Graph Neural Network Approach for Scalable Wireless Power Control

Shen

Shi

Zhang

et al. 2019

2019 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

show abstract

“…This is because traditional SP methods are crafted according to the prior expert knowledge, such as solution structures, uplinkdownlink duality, models, and the properties of signals. Such a priori expert knowledge acquired from extensive research in the literature over the past decades, is expected to be highly useful and should be utilized [8].…”

Section: Data-driven Architecturementioning

confidence: 99%

Model-Driven Beamforming Neural Networks

Xia

Zheng

Wong

et al. 2020

IEEE Wireless Commun.

View full text Add to dashboard Cite

Beamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing (ZF) are simpler but at the expense of performance loss. Alternatively, deep learning (DL) is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data-and model-driven beamforming neural networks (BNNs), presents various possible learning strategies, and also discusses complexity reduction for the DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions.

show abstract

“…In this paper, we advocate the use of a hybrid approach for spectrum sharing, in which the model-based part operates on a small timescale, whilst the data-driven part operates on a coarser time scale and refines the models used in the model-based part. The benefit of hybrid approaches has been demonstrated in the context of speech signal processing for the localization and tracking tasks [22] and in these parallel and independent works [23], [24].…”

Section: B Model-based Approaches For Spectrum Sharingmentioning

confidence: 99%

A Hybrid Model-Based and Data-Driven Approach to Spectrum Sharing in mmWave Cellular Networks

Ghadikolaei

Ghauch

Fodor

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

View full text Add to dashboard Cite

Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference. Unfortunately, traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms in terms of latency and protocol overhead, while being sensitive to missing channel state information. In this paper, we propose hybrid model-based and data-driven multi-operator spectrum sharing mechanisms, which incorporate model-based beamforming and user association complemented by data-driven model refinements. Our solution has the same computational complexity as a model-based approach but has the major advantage of having substantially less signaling overhead. We discuss how limited channel state information and quantized codebook-based beamforming affect the learning and the spectrum sharing performance. We show that the proposed hybrid sharing scheme significantly improves spectrum utilization under realistic assumptions on inter-operator coordination and channel state information acquisition.

show abstract

Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization

Cited by 163 publications

References 10 publications

A Graph Neural Network Approach for Scalable Wireless Power Control

A Graph Neural Network Approach for Scalable Wireless Power Control

Model-Driven Beamforming Neural Networks

A Hybrid Model-Based and Data-Driven Approach to Spectrum Sharing in mmWave Cellular Networks

Contact Info

Product

Resources

About