CSI Feedback Model Based on Multi-Source Characterization in FDD Systems
Abstract
:1. Introduction
1.1. Background and Motivations
1.2. Contributions
- (1)
- Use dual-path neural networks. Path1 and Path2 learn the channel matrix’s spatial features and temporal properties, respectively. Path1 adopts multiple sensory fields to understand the channel matrix’s spatial characteristics comprehensively, and Path2 combines the Transformer Attention Mechanism with Convolutional Neural Networks to thoroughly learn the temporal properties of the channel information.
- (2)
- Path1 uses multiple sensory fields; Path2 uses the Transformer attention mechanism combined with a convolutional neural network to ensure the learning of temporal relationships while reducing model parameters. The two path feature representations are eventually fused to improve the model’s performance, robustness, and generalization ability.
1.3. Paper Organization
2. System Model
3. Mix_Multi_TransNet Design
3.1. Network Modeling Processes
3.2. Path1
3.3. Path2
3.4. Mix_Multi_TransNet Network Outputs
3.5. Mix_Multi_TransNet Steps
Algorithm 1: Mix_Multi_TransNet Steps | |
1 | Input: |
2 | Output: |
3 | Initialize: |
4 | |
5 | |
6 | |
7 | |
8 | Path1: Path1_E, Path1_D |
9 | Path1_E: ; Path1_D: |
10 | Path2: Path2_E, Path2_D |
11 | Path1_E: ; Path1_D: |
12 | |
13 | End |
4. Simulation Results and Analysis
4.1. Data Sets, Training Programs, and Assessment Indicators
4.2. Mix_Multi_TransNet Network Performance
4.3. Ablation Experiment
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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1/4 | 1/8 | 1/16 | 1/32 | 1/64 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Methods | NMSE | |||||||||
In | Out | In | Out | In | Out | In | Out | In | Out | |
Mix_Multi_TransNet | −20.48 | −14.05 | −18.49 | −13.48 | −15.29 | −9.57 | −13.30 | −7.75 | −12.23 | −5.01 |
CsiNet | −16.83 | −10.28 | −14.43 | −7.08 | −9.59 | −2.37 | −8.41 | −1.84 | −5.43 | −1.93 |
CsiNet+ | −16.22 | −7.40 | −9.45 | −2.95 | −7.34 | −2.10 | −7.08 | −1.70 | −5.04 | −1.43 |
CRNet | −18.51 | −9.74 | −14.12 | −7.24 | −10.37 | −4.86 | −8.48 | −3.15 | −5.71 | −2.08 |
CLNet | −21.88 | −10.42 | −13.54 | −6.95 | −10.03 | −4.53 | −8.08 | −3.10 | −5.76 | −1.56 |
STNet | −1.46 | −0.25 | −2.65 | −0.57 | −4.21 | −1.18 | −7.01 | −2.08 | −10.27 | −0.30 |
Training Time(minutes) | ||||||||||
Mix_Multi_TransNet | 259.16 | 264.07 | 257.14 | 264.41 | 256.26 | 263.81 | 257.50 | 265.93 | 260.86 | 264.80 |
Batch’s Response Time (milliseconds) | ||||||||||
12.27 | 12.24 | 12.23 | 12.21 | 12.24 | 12.23 | 12.24 | 12.25 | 12.19 | 12.18 |
Path1 | Path2 | Path1 + Path2 without Convolution | Path1 + Path2 | |||||
---|---|---|---|---|---|---|---|---|
In | Out | In | Out | In | Out | In | Out | |
1/4 | −13.34 | −8.77 | −8.32 | −2.90 | −15.67 | −9.66 | −20.48 | −14.05 |
1/8 | −10.39 | −5.92 | −10.70 | −2.62 | −13.40 | −6.84 | −18.49 | −13.48 |
1/16 | −8.33 | −3.46 | −8.58 | −1.29 | −10.35 | −4.30 | −15.29 | −9.57 |
1/32 | −5.45 | −2.21 | −6.16 | −2.92 | −7.08 | −2.72 | −13.30 | −7.75 |
1/64 | −4.25 | −1.95 | −6.95 | −4.30 | −5.76 | −2.48 | −12.23 | −5.01 |
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Pan, F.; Zhao, X.; Zhang, B.; Xiang, P.; Hu, M.; Gao, X. CSI Feedback Model Based on Multi-Source Characterization in FDD Systems. Sensors 2023, 23, 8139. https://doi.org/10.3390/s23198139
Pan F, Zhao X, Zhang B, Xiang P, Hu M, Gao X. CSI Feedback Model Based on Multi-Source Characterization in FDD Systems. Sensors. 2023; 23(19):8139. https://doi.org/10.3390/s23198139
Chicago/Turabian StylePan, Fei, Xiaoyu Zhao, Boda Zhang, Pengjun Xiang, Mengdie Hu, and Xuesong Gao. 2023. "CSI Feedback Model Based on Multi-Source Characterization in FDD Systems" Sensors 23, no. 19: 8139. https://doi.org/10.3390/s23198139
APA StylePan, F., Zhao, X., Zhang, B., Xiang, P., Hu, M., & Gao, X. (2023). CSI Feedback Model Based on Multi-Source Characterization in FDD Systems. Sensors, 23(19), 8139. https://doi.org/10.3390/s23198139