Deep Learning-Based Bitstream Error Correction for CSI Feedback

Chang, Haoran; Liang, Xin; Li, Haozhen; Shen, Jinghan; Xu, Gu; Zhang, Lin

doi:10.1109/lwc.2021.3118923

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…Imperfect Feedback Link DNNet [130] A plug-and-play denoise NN is added before the decoder to reduce transmission errors; ECBlock [131] The error correcting NN embedded before the decoder is trained with the autoencoder; AnalogDeepCMC [132] The downlink CSI is directly mapped to the input of the uplink channel;…”

Section: E Practical Considerationmentioning

confidence: 99%

“…For example, the NMSE gap is up to 10 dB when uplink SNR is 5 dB. Unlike [130], for the digital CSI feedback in [131], the codeword is first quantized by the method proposed by [145] and fed back in the form of the bitstream. Bit errors are inevitable due to imperfect transmission.…”

Section: Data Collection and Online Trainingmentioning

confidence: 99%

“…. Illustration of ECBlock-aided digital CSI feedback framework[131], in which quantization errors and feedback bit errors are considered. At the BS, the codeword is refined by the ECBlock before being sent to the decoder.…”

mentioning

confidence: 99%

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Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems

Guo¹,

Wen²,

Jin³

et al. 2022

Preprint

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Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user terminal) and feeding back to the transmitter. The overhead of CSI feedback occupies substantial uplink bandwidth resources, especially when the number of the transmit antennas is large. Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. In this paper, a comprehensive overview of state-of-the-art research on this topic is provided, beginning with basic DL concepts widely used in CSI feedback and then categorizing and describing some existing DL-based feedback works. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy. Works on bitlevel CSI feedback and joint design of CSI feedback with other communication modules are also introduced, and some practical issues, including training dataset collection, online training, complexity, generalization, and standardization effect, are discussed. At the end of the paper, some challenges and potential research directions associated with DL-based CSI feedback in future wireless communication systems are identified.

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Section: E Practical Considerationmentioning

confidence: 99%

Section: Data Collection and Online Trainingmentioning

confidence: 99%

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Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems

Guo¹,

Wen²,

Jin³

et al. 2022

Preprint

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“…In the meanwhile, codeword quantization is also critical to deploying the DL-based system with acceptable transmission overhead. Uniform quantization is utilized in [23]- [25] to quantize the float numbers in codewords into bit streams. [26] introduces the A-law quantizer and tests the influence of different values of hyper-parameter A on the quantization performance.…”

Section: Related Workmentioning

confidence: 99%

Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback

Zhang¹,

Lu²,

Zhou³

et al. 2022

Preprint

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In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following beamforming. But the large scale of antennas in massive MIMO systems causes huge feedback overhead. Deep learning (DL) based methods can compress the CSI at the UE and recover it at the BS, which reduces the feedback cost significantly. But the compressed CSI must be quantized into bit streams for transmission. In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback. First, we design a network-aided adaptor and an advanced training scheme to adaptively improve the quantization and reconstruction accuracy. Moreover, for easy practical employment, we introduce the expert knowledge of data distribution and propose a pluggable and costfree adaptor scheme. Experiments show that compared with the state-of-the-art feedback quantization method, this adaptor-aided quantization strategy can achieve better quantization accuracy and reconstruction performance with less or no additional cost. The open-source codes are available at https://github.com/zhang-xd18/QCRNet.

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“…CsiNet [3] proposes the autoencoder-based structure to accomplish the CSI compression and reconstruction, which provides a basic approach for the DL-based CSI feedback research. Some works including DualNet [4], ATNet [5] and CRNet [6] focus on the improvement of feedback accuracy through the novel designs of network structures, while some other researches such as CVLNet [7] and BCsiNet [8] take the complexity and feasibility of the model into consideration.…”

Section: Introductionmentioning

confidence: 99%

Multi-task Deep Neural Networks for Massive MIMO CSI Feedback

Zhang¹,

Li²,

Liang³

et al. 2022

Preprint

Self Cite

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Deep learning has been widely applied for the channel state information (CSI) feedback in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. For the typical supervised training of the feedback model, the requirements of large amounts of taskspecific labeled data can hardly be satisfied, and the huge training costs and storage usage of the model in multiple scenarios are hindrance for model application. In this letter, a multi-task learning-based approach is proposed to improve the feasibility of the feedback network. An encoder-shared feedback architecture and the corresponding training scheme are further proposed to facilitate the implementation of the multi-task learning approach. The experimental results indicate that the proposed multi-task learning approach can achieve comprehensive feedback performance with considerable reduction of training cost and storage usage of the feedback model.

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Deep Learning-Based Bitstream Error Correction for CSI Feedback

Cited by 10 publications

References 8 publications

Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems

Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems

Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback

Multi-task Deep Neural Networks for Massive MIMO CSI Feedback

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