Optimization Inspired Learning Network for Multiuser Robust Beamforming

Zhu, Minghe; Chang, Tsung-Hui

doi:10.1109/sam48682.2020.9104277

Cited by 5 publications

(9 citation statements)

References 16 publications

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“…We demonstrate the performance of the proposed CL framework using problem (1). Our approaches can be extended to many other related problems, such as the beamforming problem [13], please refer to our online version due to the space limitation [30].…”

Section: Resultsmentioning

confidence: 99%

“…Deep learning has been successful in many applications such as computer vision [1], natural language processing [2], among others [3]. Recent works have also demonstrated that deep learning can be applied in communication systems, either by replacing an individual component in the system (such as signal detection [4,5], channel estimation [6,7], power allocation [8][9][10][11][12] and beamforming [13]), or by jointly optimizing the entire system [14,15], for achieving state-of-the-art performance. Specifically, deep learning is a datadriven method in which a large amount of training data is used to train a deep neural network (DNN) for a specific task (such as power control).…”

Section: Introductionmentioning

confidence: 99%

“…Once trained, such a DNN model will replace conventional algorithms to process data in real time. Existing works have shown that when the real-time data follows similar distribution as the training data, then such an approach can generate high-quality solutions for non-trivial wireless tasks [4][5][6][7][8][9][10][11][12][13], while significantly reducing real-time computation, and/or requiring only a subset of channel state information (CSI). Dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

Sun

Zhu

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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There has been a growing interest in developing data-driven, in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-ofthe-art performance while requiring less computational efforts, less channel state information (CSI), etc. However, it is often challenging for these approaches to learn in a dynamic environment where parameters such as CSIs keep changing.This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. Specifically, we consider an "episodically dynamic" setting where the environment changes in "episodes", and in each episode the environment is stationary. We propose a continual learning (CL) framework for wireless systems, which can incrementally adapt the learning models to the new episodes, without forgetting models learned from the previous episodes. Our design is based on a novel min-max formulation which ensures certain "fairness" across different episodes. Finally, we demonstrate the effectiveness of the CL approach by customizing it to a popular DNN based model for power control, and testing using both synthetic and real data.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

Sun

Zhu

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…There are several successful examples [33], such as channel prediction, signal detection, resource allocation etc. In the field of beamforming, there are also many DL-based works, including multiuser robust beamforming [34], constant envelope beamforming [35], and auction-driven multiuser beamforming [36], which verify the feasibility of applying DL in beamforming. However, in most existing DL-based beamforming methods, the DL structures are limited to specific scenes, which brings great limitations.…”

Section: Introductionmentioning

confidence: 93%

A deep learning‐based low complexity approach for joint transceiver beamforming

et al. 2021

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In this paper, massive multiple-input-multiple-output (MIMO) wireless communication systems are considered to investigate joint transceiver beamforming. A base station (BS) equipped with a uniform planar array (UPA) serves several multi-antennas users in a single cell. Based on the channel state information (CSI), the low complexity design of transceiver beamforming to minimize the transmit power subject to some quality of service (QoS) constraints is investigated. As the upper bound of the transmit power performance, the existing iteration-based algorithms are leveraged as a reference. A general deep learning (DL)-based framework and deep neural network (DNN) structure are proposed to reduce the complexity of the existing algorithms, where the properly trained DNN structure can learn directly from CSI. Consider the complexity of the DNN structure itself, a heuristic algorithm is proposed to replace the DNN structure, which takes the max-eigenvalueeigenvector of the CSI as the direction of receive beamforming directly. The DNN structure is trained in the offline stage, therefore, only the complexity in the online stage is taken into consideration. Based on the numerical simulation, the complexity of the proposed DL-based framework and the transceiver beamforming algorithms is reduced significantly while maintaining nearly the optimal performance compared with the existing iterative algorithms.

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“…Deep learning has been successful in many applications such as computer vision [2], natural language processing [3], and recommender systems [4]; see [5] for an overview. Recent works have also demonstrated that deep learning can be applied in communication systems, either by replacing an individual component in the system (such as signal detection [6,7], channel decoding [8], channel estimation [9,10], power allocation [11][12][13][14][15], beamforming [16,17] and wireless scheduling [18]), or by jointly optimizing the entire system [19,20], for achieving state-of-the-art performance. Specifically, deep learning is a data-driven method in which a large amount of training data is used to train a deep neural network (DNN) for a specific task (such as power control).…”

Section: Introductionmentioning

confidence: 99%

Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment

Sun,

Pu,

Zhu

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

There has been a growing interest in developing data-driven and in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-of-the-art performance while requiring less computational efforts, less channel state information (CSI), etc. However, it is often challenging for these approaches to learn in a dynamic environment where parameters such as CSIs keep changing.This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. Specifically, we consider an "episodically dynamic" setting where the environment changes in "episodes", and in each episode the environment is stationary. We propose to build the notion of continual learning (CL) into the modeling process of learning wireless systems, so that the learning model can incrementally adapt to the new episodes, without forgetting knowledge learned from the previous episodes. Our design is based on a novel min-max formulation which ensures certain "fairness" across different data samples. We demonstrate the effectiveness of the CL approach by customizing it to two popular DNN based models (one for power control and one for beamforming), and testing using both synthetic and real data sets. These numerical results show that the proposed CL approach is not only able to adapt to the new scenarios quickly and seamlessly, but importantly, it maintains high performance over the previously encountered scenarios as well.

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Optimization Inspired Learning Network for Multiuser Robust Beamforming

Cited by 5 publications

References 16 publications

Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

A deep learning‐based low complexity approach for joint transceiver beamforming

Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment

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