Deep CSI Compression and Coordinated Precoding for Multicell Downlink Systems

Lee, An-An; Wang, Yung-Shun; Hong, Y.-W. Peter

doi:10.1109/globecom42002.2020.9322121

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Hence different ML techniques and approaches have been proposed in the literature. Some of the approaches include the use of DL techniques for CSI feedback compression, quantization and recovery [112], [202], [215]- [223], [225], [226], [231], [234], [235], [237], [238], [240], [241], [243]- [245], [247], [248], [255]- [257], use of DL at the edge for CSI feedback [224], DL assisted blind CE [227], DL based CSI for maximal beamforming performance [228] , ML clustering techniques design [229], [239], DL user grouping for joint spatial division and multiplexing (JSDM) [258], DL CSI prediction [21], [230], [232], [233], [246], DL with superimposed coding [236], [259], learning based remote channel inference [196], channel mapping to user position [137], [242], [260] and analog feedback [261], DL symbol level based CE [262].…”

Section: ) Channel Estimationmentioning

confidence: 99%

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

et al. 2022

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Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output MIMO (CM-MIMO) systems. This introduces the concept of intelligent mMIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends.

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Section: ) Channel Estimationmentioning

confidence: 99%

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

et al. 2022

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“…CsiFBnet, [116]: the decoder NNs directly produce a BF vector that maximizes the BF gain; [117]:jointly designing the feedback and hybrid precoding and pointing out that the gain achieved by joint design is large, especially when the feedback is extremely limited; CsiCPreNet [118]: the BSs exchange the feedback codewords with one another when multiple-cell is considered, and the precoding matrix is generated by a coordinated precoder design NN;…”

Section: Joint Channel Feedback and Bf Designmentioning

confidence: 99%

“…As mentioned in [117], the gain achieved by joint design is large, especially when the feedback is extremely limited. A more complicated scenario is considered in [118], where the user receives the signals from all cells instead of only the nearby cells in [116]. The framework in [118] consists of two modules: a CSI compression NN and a coordinated precoder NN.…”

Section: Joint Channel Acquisition and Utilizationmentioning

confidence: 99%

“…A more complicated scenario is considered in [118], where the user receives the signals from all cells instead of only the nearby cells in [116]. The framework in [118] consists of two modules: a CSI compression NN and a coordinated precoder NN. Upon receiving the feedback bitstreams, the BSs exchange the feedback codewords with one another.…”

Section: Joint Channel Acquisition and Utilizationmentioning

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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“…Online transfer learning [17], [18] strategies are introduced to DL-based CSI feedback to mitigate the influence of channel mismatch. Other deployment problems are also widely studied including joint design with other modules [19]- [21], bit-stream generation [22], multiple-rate feedback [23], imperfect feedback [24], etc.. Beside the aforementioned research, the hardware limitations in computation and storage are also considered to facilitate the deployment of DL-based CSI feedback in practical communication systems. Although the computation of DL-based CSI feedback algorithm can be significantly accelerated with the assistance of specific devices such as graphics processing units and tensor processing units, the computational complexity can still be large for resource-limited scenarios.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Neural Network with Knowledge Distillation for CSI Feedback

Cui¹,

Guo²,

Chen³

et al. 2022

Preprint

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Deep learning-based autoencoder has shown considerable potential in channel state information (CSI) feedback. However, the excellent feedback performance achieved by autoencoder is at the expense of a high computational complexity. In this paper, a knowledge distillation-based neural network lightweight strategy is introduced to deep learning-based CSI feedback to reduce the computational requirement. The key idea is to transfer the dark knowledge learned by a complicated teacher network to a lightweight student network, thereby improving the performance of the student network. First, an autoencoder distillation method is proposed by forcing the student autoencoder to mimic the output of the teacher autoencoder. Then, given the more limited computational power at the user equipment, an encoder distillation method is proposed where distillation is only performed to student encoder at the user equipment and the teacher decoder is directly used at the base stataion. The numerical simulation results show that the performance of student autoencoder can be considerably improved after knowledge distillation and encoder distillation can further improve the feedback performance and reduce the complexity.

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Deep CSI Compression and Coordinated Precoding for Multicell Downlink Systems

Cited by 8 publications

References 14 publications

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

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

Lightweight Neural Network with Knowledge Distillation for CSI Feedback

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