Deep Learning Based CSI Compression and Quantization With High Compression Ratios in FDD Massive MIMO Systems

Zhang, Yangyang; Zhang, Xichang; Liu, Yi

doi:10.1109/lwc.2021.3092947

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…The NMSE of ENet [41] is −11.20 dB in indoor scenarios when η equals 1/32, which has been surpassed by our model. In outdoor scenarios, a neural network named CsiNet+DNN proposed in [42] gets remarkable results at the expense of extremely high computational overhead. Compared to TransNet+ and other attention models, the deployment resource overhead of CsiNet+DNN may be significantly larger.…”

Section: Discussionmentioning

confidence: 99%

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

Chen,

Guo,

Cui

2024

Applied Sciences

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To achieve the anticipated performance of massive multiple input multiple output (MIMO) systems in wireless communication, it is imperative that the user equipment (UE) accurately feeds the channel state information (CSI) back to the base station (BS) along the uplink. To reduce the feedback overhead, an increasing number of deep learning (DL)-based networks have emerged, aimed at compressing and subsequently recovering CSI. Various novel structures are introduced, among which Transformer architecture has enabled a new level of precision in CSI feedback. In this paper, we propose a new method named TransNet+ built upon the Transformer-based TransNet by updating the multi-head attention layer and implementing an improved training scheme. The simulation results demonstrate that TransNet+ outperforms existing methods in terms of recovery accuracy and achieves state-of-the-art.

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Section: Discussionmentioning

confidence: 99%

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

Chen,

Guo,

Cui

2024

Applied Sciences

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“…For example, NMSE is reduced from −17.36 dB to −20.80 dB when CR is 1/4. Based on [52], CsiNet+ does not work well when CR is low, such as 1/32 for outdoor channels. Therefore, two FC layers are embedded after the second convolutional layer in the RefineNet block, and more RefineNet blocks are employed.…”

Section: A Novel Nn Architecture Designmentioning

confidence: 99%

“…CsiNet+DNN [52] Embedding two FC layers after the second convolutional layer in the RefineNet block; Employing more RefineNet block; MRNet [59] Setting the convolutional sizes of the encoder and the decoder as 5 × 5 and 8 × 8; CS-ReNet [60] Stacking seven convolutional layers with 3 × 3 filters at the decoder; BCsiNet [61] Stacking three 3 × 3 convolutional layers at the encoder to improve CSI feature quality;…”

Section: Increasing Receptive Fieldmentioning

confidence: 99%

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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“…It also designs an offset network to compensate for the quantization errors. [27] equips the µ-law quantizer into its system and improves the feedback performance through end-to-end training. Focusing on the non-differentiable problem brought by the quantization module, [28] designs a differentiable function to approximate the gradients.…”

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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Deep Learning Based CSI Compression and Quantization With High Compression Ratios in FDD Massive MIMO Systems

Cited by 14 publications

References 18 publications

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

Multi-Head Transformer Architecture with Higher Dimensional Feature Representation for Massive MIMO CSI Feedback

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

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

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