Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO

Sohrabi, Foad; Attiah, Kareem M.; Yu, Wei

doi:10.1109/twc.2021.3055202

Cited by 131 publications

(107 citation statements)

References 35 publications

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“…For the proposed model-driven solution, when calculating the sum rate in the loss function, both the original algorithm that uses the beamforming solution in (7), and the one that uses the simplified ZF beamforming in (24) are included. For comparison, we consider the following benchmark algorithms:…”

Section: Simulation Resultsmentioning

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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Section: Simulation Resultsmentioning

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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“…The performance of the proposed scheme is validated through simulation, as compared to the theoretical channel model. Similarly, in [82], deep learning is used to enable distributed quantization, feedback, channel estimation, and downlink multi-user precoding for massive MIMO. The authors proposed a joint design of pilots and a deep neural network, to transform the received pilots into feedback bits at UE level, while mapping the UEs' feedback bits into the precoding matrix at the base stations side.…”

Section: ) Literature Reviewmentioning

confidence: 99%

Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges

Brik

Boutiba

Ksentini

2022

IEEE Open J. Commun. Soc.

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Open Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN architecture featuring open, softwaredriven, virtual, and intelligent radio access architecture. O-RAN architecture is based on (1) disaggregated RAN functions that run as Virtual Network Function (VNF) and Physical Network Function (PNF); (2) the notion of RAN controller that runs centrally RAN applications such as mobility management, users' scheduling, radio resources allocation, etc. The RAN controller is in charge of enforcing the application decisions by using open interfaces with the RAN functions. One important feature introduced by O-RAN is the heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent RAN applications that are able to fulfill the Quality of Service (QoS) requirements of the envisioned 5G and beyond network services. In this work, we first give an overview of the evolution of RAN architectures toward 5G and beyond, namely C-RAN, vRAN, and O-RAN. We also compare them based on various perspectives, such as edge support, virtualization, control and management, energy consumption, and AI support. Then, we review existing DL-based solutions addressing the RAN part. We also show how they can be integrated/mapped to the O-RAN architecture since these works were not initially adapted to the O-RAN architecture. In addition, we present two case studies for DL techniques deployment in O-RAN. Furthermore, we describe how the main steps of deployed DL models in O-RAN can be automated, to ensure stable performance of these models, introducing ML system operations (MLOps) concept in O-RAN. Finally, we identify key technical challenges, open issues, and future research directions related to the Artificial Intelligence (AI)-enabled O-RAN architecture.

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“…High-dimensional channels are reconstructed using DL from underdetermined measurements by exploiting the MIMO channel’s angular-domain compressibility. The DNN architecture is employed for downlink multiuser precoding, feedback, quantization, and distributed channel estimation for a FDD Ma-MIMO system in [ 158 ], where a BS serves many mobile users. However, the feedback from the users to the BS is rate-limited.…”

Section: Rl and DL Application In Mimomentioning

confidence: 99%

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Naeem

Pietro

Coronato

2021

Sensors

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The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

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Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO

Cited by 131 publications

References 35 publications

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

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

Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

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