Binarized Aggregated Network with Quantization: Flexible Deep Learning
Deployment for CSI Feedback in Massive MIMO System
- URL: http://arxiv.org/abs/2105.00354v1
- Date: Sat, 1 May 2021 22:50:25 GMT
- Title: Binarized Aggregated Network with Quantization: Flexible Deep Learning
Deployment for CSI Feedback in Massive MIMO System
- Authors: Zhilin Lu, Xudong Zhang, Hongyi He, Jintao Wang and Jian Song
- Abstract summary: A novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance.
The elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations.
Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks.
- Score: 22.068682756598914
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Massive multiple-input multiple-output (MIMO) is one of the key techniques to
achieve better spectrum and energy efficiency in 5G system. The channel state
information (CSI) needs to be fed back from the user equipment to the base
station in frequency division duplexing (FDD) mode. However, the overhead of
the direct feedback is unacceptable due to the large antenna array in massive
MIMO system. Recently, deep learning is widely adopted to the compressed CSI
feedback task and proved to be effective. In this paper, a novel network named
aggregated channel reconstruction network (ACRNet) is designed to boost the
feedback performance with network aggregation and parametric rectified linear
unit (PReLU) activation. The practical deployment of the feedback network in
the communication system is also considered. Specifically, the elastic feedback
scheme is proposed to flexibly adapt the network to meet different resource
limitations. Besides, the network binarization technique is combined with the
feature quantization for lightweight and practical deployment. Experiments show
that the proposed ACRNet outperforms loads of previous state-of-the-art
networks, providing a neat feedback solution with high performance, low cost
and impressive flexibility.
Related papers
- Joint Channel Estimation and Feedback with Masked Token Transformers in
Massive MIMO Systems [74.52117784544758]
This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix.
The entire encoder-decoder network is utilized for channel compression.
Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
arXiv Detail & Related papers (2023-06-08T06:15:17Z) - Towards Efficient Subarray Hybrid Beamforming: Attention Network-based
Practical Feedback in FDD Massive MU-MIMO Systems [9.320559153486885]
This paper introduces a jointly optimized network for channel estimation and feedback.
Experiments show that the proposed network is over 10 times lighter at the resource-sensitive user equipment.
arXiv Detail & Related papers (2023-02-05T15:12:07Z) - Better Lightweight Network for Free: Codeword Mimic Learning for Massive
MIMO CSI feedback [9.320559153486885]
lightweight feedback networks attract special attention due to their practicality of deployment.
A cost free distillation technique named codeword mimic (CM) is proposed to train better feedback networks.
Experiments show that the proposed CM learning outperforms the previous state-of-the-art feedback distillation method.
arXiv Detail & Related papers (2022-10-29T09:35:14Z) - Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable
Intelligent Surface-Aided Tera-Hertz Massive MIMO [56.022764337221325]
Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems.
However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging.
This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
arXiv Detail & Related papers (2022-09-18T03:07:37Z) - 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) - CSI Feedback with Model-Driven Deep Learning of Massive MIMO Systems [0.0]
We propose a two stages low rank (TSLR) CSI feedback scheme to reduce the feedback overhead based on model-driven deep learning.
Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback.
arXiv Detail & Related papers (2021-12-13T03:50:43Z) - 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) - Aggregated Network for Massive MIMO CSI Feedback [18.04633171156304]
ACRNet is designed to boost the feedback performance with network aggregation and parametric RuLU activation.
Experiments show that ACRNet outperforms loads of previous state-of-the-art feedback networks without any extra information.
arXiv Detail & Related papers (2021-01-17T08:19:40Z) - A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI
Feedback [32.442094263278605]
Forward channel state information (CSI) plays a vital role in transmission optimization for massive multiple-input multiple-output (MIMO) communication systems.
Recent studies on the use of recurrent neural networks (RNNs) have demonstrated strong promises, though the cost of computation and memory remains high.
In this work, we exploit channel coherence in time to substantially improve the feedback efficiency.
arXiv Detail & Related papers (2020-09-20T16:26:12Z) - Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral
Super-Resolution [79.97180849505294]
We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI.
Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
arXiv Detail & Related papers (2020-07-10T08:08:20Z) - Resolution Adaptive Networks for Efficient Inference [53.04907454606711]
We propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs.
In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations.
High-resolution paths in the network maintain the capability to recognize the "hard" samples.
arXiv Detail & Related papers (2020-03-16T16:54:36Z)
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.