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Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems
[article]
2019
arXiv
pre-print
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This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular ...
We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research. ...
Index Terms-Massive MIMO, channel estimation, FDD, 5G cellular, deep learning.
I. ...
arXiv:1912.10573v1
fatcat:w2dn4os3yngjjkrzx66utzacga
Deep Learning Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction
[article]
2020
arXiv
pre-print
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This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. ...
Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. ...
CONCLUSION This study considered the FDD non-stationary massive MIMO system and proposed a deep learning-based scheme to reconstruct the downlink channel. ...
arXiv:2002.09858v1
fatcat:g7vqp5fpu5funlwmkeb3ggbgju
Scanning the Literature
2021
IEEE wireless communications
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The core of the data processing is a deep-learning based multivariate long short term memory model that captures and predicts the spatiotemporal patterns and mobility. ...
Cellular operators are facing fast growing mobile subscribers and surging traffic demand. More base stations and more powerful processing units are deployed to increase the network capacity. ...
In this paper, the authors explored low rate CSI feedback to conserve feedback bandwidth and improve downlink CSI reconstruction accuracy for massive MIMO downlink in FDD systems. ...
doi:10.1109/mwc.2021.9535469
fatcat:wno2m5nibbbshb7o2mlmkxeutm
Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency
[article]
2019
arXiv
pre-print
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For example, in FDD massive MIMO, the uplink channels can be mapped to the downlink channels or the downlink channels at one subset of antennas can be mapped to the downlink channels at all the other antennas ...
If this channel-to-channel mapping is possible, we can expect dramatic gains for massive MIMO systems. ...
DEEP LEARNING BASED CHANNEL MAPPING IN CELL-FREE MASSIVE MIMO SYSTEMS In this section, we investigate the developed deep-learning based channel mapping approach on a cell-free (distributed) massive MIMO ...
arXiv:1905.03761v2
fatcat:jaxpqvihhfb5vdqdahd43ufnvy
Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels
[article]
2018
arXiv
pre-print
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Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. ...
samples of time-varying massive MIMO channels. ...
SYSTEM MODEL An FDD downlink massive MIMO-orthogonal frequency division multiplexing (OFDM) system with N c subcarriers is considered. ...
arXiv:1807.11673v1
fatcat:6iuytdgkivcktlrtqx6j5j7pqe
CSI Feedback with Model-Driven Deep Learning of Massive MIMO Systems
[article]
2021
arXiv
pre-print
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In this paper, a two stages low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO systems is proposed to reduce the feedback overhead based on model-driven deep learning. ...
In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information ...
There is a prerequisite for using massive MIMO, which is the base station (BS) needs to acquire channel state information (CSI) of the downlink channel. ...
arXiv:2112.06405v1
fatcat:kjlstv37mnektma2o6ka7ntqxm
Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems
2021
Sensors
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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. ...
This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. ...
Similarly, a model-driven DL-enabled downlink channel reconstruction design is proposed in [227] for FDD massive MIMO systems. ...
doi:10.3390/s22010309
pmid:35009848
pmcid:PMC8749942
fatcat:2w4th63dtrdyboa6rmhr5rcvja
Deep Learning for Massive MIMO CSI Feedback
[article]
2018
arXiv
pre-print
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In frequency division duplex mode, the downlink channel state information (CSI) should be sent to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output ...
In this letter, we use deep learning technology to develop CsiNet, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples. ...
The methodology can be used in FDD MIMO systems as a feedback protocol. ...
arXiv:1712.08919v4
fatcat:zuzhr2ccpbhkroc3zobuh4c2ye
2019 Index IEEE Wireless Communications Letters Vol. 8
2019
IEEE Wireless Communications Letters
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., +, LWC June 2019 881-884
Deep Learning-Based Channel Estimation for Massive MIMO Systems. ...
., +, LWC Aug. 2019 1077-1081
Low-Complexity Downlink Channel Estimation for Millimeter-Wave FDD
Massive MIMO Systems. ...
doi:10.1109/lwc.2019.2961756
fatcat:bwxehcl4ejew7a6m66prb6s4z4
Aggregated Network for Massive MIMO CSI Feedback
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2101.06618v1 fatcat:erzoapjcandf5p3xu3htead2wu
The downlink CSI is essential for the massive multiple-input multiple-output (MIMO) system to acquire the potential gain. ...
Recently, deep learning is widely adopted to massive MIMO CSI feedback task and proved to be effective compared with traditional compressed sensing methods. ...
The authors are with the Department of Electronic Engineering, Tsinghua University, and Beijing National Research Center for Information Science and Technology (BNRist), Beijing 100084, China. ...
arXiv:2101.06618v2
fatcat:lb27nn2ycva33kgo2gxccxcg2q
AI Empowered Channel Semantic Acquisition for 6G Integrated Sensing and Communication Networks
[article]
2024
arXiv
pre-print
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In the proposed framework, we introduce a two-stage frame structure and resort artificial intelligence (AI) to achieve the synergistic gain by designing a joint C&S channel semantic extraction and reconstruction ...
Although the millimeter wave (mmWave) communication and mmWave radar share similar multiple-input multiple-output (MIMO) architecture for integration, the full potential of dual-function synergy remains ...
Case Study To demonstrate the superiority of the proposed scheme, we study a representative case in the massive MIMO- based ISAC systems. ...
arXiv:2401.09127v1
fatcat:a4jowem5bre7fb2q3saqeoacey
Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions
[article]
2021
arXiv
pre-print
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(MIMO), reconfigurable intelligent surface assisted communications and cell-free massive MIMO. ...
Since the substance of channel extrapolation is a mapping from one parameter subspace to another, we can resort to deep learning (DL), a powerful learning architecture, to approximate such mapping function ...
15 communications and cell-free massive MIMO, and deserves a full investigation and exploitation for such communications systems, which will incorporate intelligence in the future. ...
arXiv:2102.12859v1
fatcat:emmqvrchgne3pej6auxpv6hm7q
Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges
2020
Journal of Cloud Computing: Advances, Systems and Applications
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As a classic model of deep learning, autoencoder is widely used in the design paradigms of communication system models. ...
We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing ...
Acknowledgements The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions. ...
doi:10.1186/s13677-020-00168-9
fatcat:7n6r2pozgfb5rgfwyxoxpqxq3q
CSI Feedback Based on Deep Learning for Massive MIMO Systems
2019
IEEE Access
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(MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. ...
INDEX TERMS FDD, massive MIMO, channel state information, compressed feedback, deep learning. This work is licensed under a Creative Commons Attribution 3.0 License. ...
In the frequency-division duplexing (FDD) massive MIMO systems, the base station (BS) needs to obtain the downlink CSI through the feedback of the receiver [5] , [6] . ...
doi:10.1109/access.2019.2924673
fatcat:abdsyekr35cdvic2lbriyg4vdq
Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G
[article]
2022
arXiv
pre-print
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Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. ...
To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. ...
Fig. 2 2 Fig. 2 An example mapping pattern of NR CSI-RS.
3. 2 2 Deep Learning based CSI Schemes 3.2.1 DL-based CSI Reconstruction at Receiver (AI4CSI Rx)
Fig. 3 3 Fig. 3 System structure for CSI reconstruction ...
arXiv:2206.04967v2
fatcat:jdvf74d3fzanroiziljapmakyy
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