Machine learning-based adaptive CSI feedback interval

Hong, Seunghui; Jo, Sanguk; So, Jaewoo

doi:10.1016/j.icte.2021.11.007

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Multi-domain correlations have been adopted to leverage the geometrical wireless propagation information embedded within the CSI. For instance, the prediction neural network in [24] generates the current CSI based on the previous CSI sequence capitalizing on time correlation, which is shared by both the UE and the BS. Furthermore, the DualNet-MAG architecture in [25] exploits the correlation between the magnitudes of the bidirectional channels, with the uplink channel serving as a feedback channel for the quantized CSI phase and a neural network-based encoder compressing the CSI magnitude.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…The other 𝑇 − 1 CSI "frames" are compressed by low-CR encoders; LSTM refines the reconstructed CSI with the information extracted from the former CSI; RecCsiNet [86] The LSTM at the encoder compresses the CSI based on the current and previous CSI matrices; [87] Feedback overhead is reduced by dynamically adjusting feedback interval of time varying channel; Feedback is not needed if prediction errors are tolerable;…”

Section: ) Gan and Vaementioning

confidence: 99%

“…Unlike [85], [86], [97], the feedback overhead is reduced in [87] by dynamically adjusting the feedback interval of the time-varying channel instead of reducing the overhead of each CSI feedback. A prediction NN in [87], which produces the current CSI based on the knowledge of the past CSI sequence, is shared by the user and the BS. If prediction errors are tolerable, the user does not need to feed back the current CSI, and the BS directly uses the CSI produced by the shared prediction NN.…”

Section: ……mentioning

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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“…Moreover, the complexity of ML is too high to be implemented in the lightweight IoT device. Recent work of [ 33 ] proposed aperiodic CSI feedback based on the deep neural network (DNN)-based channel prediction, where the terminal decides whether or not to feed back its CSI relying on the DNN-based channel prediction. However, the work of [ 33 ] did not take into account the feedback delay, and moreover, applied a classical DNN rather than an LSTM model in time-series forecasting.…”

Section: Introductionmentioning

confidence: 99%

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

Han

Kim

2023

Sensors

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As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance.

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Machine learning-based adaptive CSI feedback interval

Cited by 4 publications

References 11 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

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

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