Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G

• URL: http://arxiv.org/abs/2206.04967v1
• Date: Fri, 10 Jun 2022 09:45:25 GMT
• Title: Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G
• Authors: Xin Wang and Xiaolin Hou and Lan Chen and Yoshihisa Kishiyama and Takahiro Asai
• Abstract summary: We propose two implementation schemes for artificial intelligence for CSI (AI4CSI) in 5G NR networks. The schemes are evaluated in terms of spectrum efficiency (SE), feedback overhead, and computational complexity. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks.
• Score: 8.731696607553346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. 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. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25\% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6% -- 26% compared with DL-based receivers and 33% -- 58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.

Related papers

• Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks [1.3351610617039973]
  This paper aims to address privacy concerns by examining the representative case study of shared database scenarios in 5G Open Radio Access Network (O-RAN) networks. We focus on securing the data that can be used by machine learning (ML) models for spectrum sharing and interference mitigation applications without compromising the model and network performances.
  arXiv  Detail & Related papers  (2024-02-15T05:06:53Z)
• In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks [61.416494781759326]
  In-situ model downloading aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level. We propose a 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library.
  arXiv  Detail & Related papers  (2022-10-07T13:41:15Z)
• Enhancing Deep Learning Performance of Massive MIMO CSI Feedback [7.63185216082836]
  We propose a jigsaw puzzles aided training strategy (JPTS) to enhance the deep learning-based Massive multiple-input multiple-output (MIMO) CSI feedback approaches. Experimental results show that by adopting this training strategy, the accuracy can be boosted by 12.07% and 7.01% on average in indoor and outdoor environments.
  arXiv  Detail & Related papers  (2022-08-24T07:08:31Z)
• AI for CSI Feedback Enhancement in 5G-Advanced and 6G [51.276468472631976]
  3rd Generation Partnership Project has started the study of Release 18 in 2021. This article provides a comprehensive overview of AI for CSI feedback enhancement in 5G-Advanced and 6G.
  arXiv  Detail & Related papers  (2022-06-30T08:52:43Z)
• Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems [77.0986534024972]
  Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy.
  arXiv  Detail & Related papers  (2022-06-29T03:28:57Z)
• FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
  This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources. It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
  arXiv  Detail & Related papers  (2022-01-18T13:59:22Z)
• Deep Learning-based Implicit CSI Feedback in Massive MIMO [68.81204537021821]
  We propose a DL-based implicit feedback architecture to inherit the low-overhead characteristic, which uses neural networks (NNs) to replace the precoding matrix indicator (PMI) encoding and decoding modules. For a single resource block (RB), the proposed architecture can save 25.0% and 40.0% of overhead compared with Type I codebook under two antenna configurations.
  arXiv  Detail & Related papers  (2021-05-21T02:43:02Z)
• CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback [7.63185216082836]
  This paper presents a novel neural network CLNet tailored for CSI feedback problem based on the intrinsic properties of CSI. The experiment result shows that CLNet outperforms the state-of-the-art method by average accuracy improvement of 5.41% in both outdoor and indoor scenarios.
  arXiv  Detail & Related papers  (2021-02-15T12:16:11Z)
• ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks [63.91384986073851]
  We propose the autoencoder-based low-rank filter-sharing technique technique (ALF) ALF shows a reduction of 70% in network parameters, 61% in operations and 41% in execution time, with minimal loss in accuracy.
  arXiv  Detail & Related papers  (2020-07-27T09:01:22Z)
• Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation [36.364156900974535]
  We propose a deep learning assisted CSI estimation technique in highly mobile vehicular networks. We formulate and solve a dynamic network slicing based resource allocation problem for vehicular user equipments.
  arXiv  Detail & Related papers  (2020-03-12T10:00:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.