Channel Estimation Method Based on Transformer in High Dynamic Environment

Chen, Zhuolin; Gu, Fengshou; Jiang, Rui

doi:10.1109/wcsp49889.2020.9299821

Cited by 18 publications

(9 citation statements)

References 16 publications

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“…We have also implemented a transformer-based method similar to [9] with 31,829 parameters for comparison. The output of TR is interpolated by the bilinear method to estimate the channel matrix of the whole slot.…”

Section: Transformer-based Methods (Tr) For Comparisonmentioning

confidence: 99%

“…A graph attention network has also been utilized for channel estimation [8]. The papers [9] [10] [11] also deploy the attention mechanism to improve the performance.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed solution should also be robust to the extended channel conditions, such as signal-to-noise ratio (SNR) and Doppler shift. We also implement a transformer architecture from [9] for comparison, whose performance is found to be inferior to HA02.…”

Section: Introductionmentioning

confidence: 99%

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Attention Based Neural Networks for Wireless Channel Estimation

Luan¹,

Thompson²

2022

Preprint

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In this paper, we deploy the self-attention mechanism to achieve improved channel estimation for orthogonal frequency-division multiplexing waveforms in the downlink. Specifically, we propose a new hybrid encoder-decoder structure (called HA02) for the first time which exploits the attention mechanism to focus on the most important input information.In particular, we implement a transformer encoder block as the encoder to achieve the sparsity in the input features and a residual neural network as the decoder respectively, inspired by the success of the attention mechanism. Using 3GPP channel models, our simulations show superior estimation performance compared with other candidate neural network methods for channel estimation.

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Section: Transformer-based Methods (Tr) For Comparisonmentioning

confidence: 99%

“…A graph attention network has also been utilized for channel estimation [8]. The papers [9] [10] [11] also deploy the attention mechanism to improve the performance.…”

Section: Introductionmentioning

confidence: 99%

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Attention Based Neural Networks for Wireless Channel Estimation

Luan¹,

Thompson²

2022

Preprint

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“…Its remarkable success in the fields of NLP and CV has inspired communication researchers to investigate its applications to various communication problems. Not surprisingly transformers have also shown remarkable success in certain communication tasks, specifically, for channel estimation and semantic communication [7], [8].…”

Section: B Self-attention and Transformermentioning

confidence: 99%

Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication

Wang¹,

Gao²,

Zheng³

et al. 2022

Preprint

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6G wireless networks are foreseen to speed up the convergence of the physical and cyber worlds and to enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, in particular deep learning (DL), is going to be one of the key technological enablers of 6G by offering a new paradigm for the design and optimization of networks with a high level of intelligence. In this article, we introduce an emerging DL architecture, known as the transformer, and discuss its potential impact on 6G network design. We first discuss the differences between the transformer and classical DL architectures, and emphasize the transformer's self-attention mechanism and strong representation capabilities, which make it particularly appealing in tackling various challenges in wireless network design. Specifically, we propose transformer-based solutions for massive multiple-input multiple-output (MIMO) systems and various semantic communication problems in 6G networks. Finally, we discuss key challenges and open issues in transformerbased solutions, and identify future research directions for their deployment in intelligent 6G networks.

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“…Self-attention mechanism and transformer network [16] have great successes in various NLP applications, for example, Devlin and others [17], by its powerful feature extractions, and also for various image processing applications [18]. Several works [19][20][21][22] presented channel estimations based on transformer network or attention mechanisms. A transformer network can be designed to process variable lengths of sequences as inputs and outputs, which is beneficial for designing scalable channel estimators.…”

Section: Related Workmentioning

confidence: 99%

Deep learning‐based scalable and robust channel estimator for wireless cellular networks

et al. 2022

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In this paper, we present a two‐stage scalable channel estimator (TSCE), a deep learning (DL)‐based scalable, and robust channel estimator for wireless cellular networks, which is made up of two DL networks to efficiently support different resource allocation sizes and reference signal configurations. Both networks use the transformer, one of cutting‐edge neural network architecture, as a backbone for accurate estimation. For computation‐efficient global feature extractions, we propose using window and window averaging‐based self‐attentions. Our results show that TSCE learns wireless propagation channels correctly and outperforms both traditional estimators and baseline DL‐based estimators. Additionally, scalability and robustness evaluations are performed, revealing that TSCE is more robust in various environments than the baseline DL‐based estimators.

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Channel Estimation Method Based on Transformer in High Dynamic Environment

Cited by 18 publications

References 16 publications

Attention Based Neural Networks for Wireless Channel Estimation

Attention Based Neural Networks for Wireless Channel Estimation

Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication

Deep learning‐based scalable and robust channel estimator for wireless cellular networks

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