Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer
Abstract
:1. Introduction
2. Related Work
2.1. Non-Coherent Approach
2.2. Channel Prediction in Coherent Approach
3. System Model
Communication Model
4. SO Improved GRU Model
4.1. Gated Recurrent Neural Network
4.2. SO Improved GRU Prediction Model
4.3. GRU Algorithm Improved by SO
- Step 1: Build a GRU network model and determine the boundary values of the number of hidden layer units and learning rate according to historical data and inherent model requirements.
- Step 2: Import the training set and preprocess the data.
- Step 3: Determine the number of iterations according to historical experience, initialize the population with boundary values, and divide the female and male populations.
- Step 4: The fitness of everyone is obtained according to the model and training set. The loss value of the network model is used as the fitness function value.
- Step 5: Use the SO algorithm to find the best individual iteration.
- Step 6: Judge whether the SO algorithm reaches the upper limit of iteration times. If yes, retain the final optimal fitness individual and return the optimal number of hidden layer units and learning rate; otherwise, the operation of Step 5 is repeated.
- Step 7: Establish the GRU model with the best parameters, input the pre-processed training set data, and train the network model.
- Step 8: Judge whether it reaches the end of the training set data. If yes, proceed to Step 9; otherwise, continue the training.
- Step 9: Use the trained network model to conduct online prediction and online training.
- Step 10: Output the predicted channel status information.
4.4. Model Training and Prediction
Algorithm 1: Proposed channel predictor |
1. Initialize parameters in the SO, such as population M, iteration times T, etc. |
2. Use SO to find the optimal parameters. |
3. The two-channel GRU model was constructed using the optimal parameters. |
4. Start offline Training. |
5. While True do |
6. Data preprocessing |
7. if Total forecast data length > Total data length then |
8. end while |
9. else |
10. i ← 1 |
11. while True do |
12. if Forecast data length > D then |
13. end while |
14. end if |
15. Single prediction. |
16. Store experience in the experience pool. |
17. if i % 20 = 0 then |
18. Complete learning. |
19. else |
20. if i % 20 = 0 then |
21. Fast learning |
22. end if |
23. i ← i + 1 |
24. end if |
5. Analysis of Simulation Results
5.1. Data Analysis
5.2. Model Parameter Setting and Evaluation Index
5.3. ERSO Improved GRU Algorithm Performance Analysis
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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ERSO-GRU | Value Range |
---|---|
Snake populations M | 10 |
Maximum iterations T | 100 |
Hidden layer units one | [1, 50] |
Hidden layer units two | [1, 50] |
Learning rate | [0.00001, 0.5] |
Known CSI length K | 30 |
Predicted CSI length P | 10 |
Data block size | 200 |
Experience pool size | 200 |
Evaluation Criteria | Definition | Formula |
---|---|---|
MAE | Mean absolute error | |
MAPE | Mean absolute percentage error | |
MSE | Mean square error | |
RMSE | Root mean square error |
Locations | Model | MAE | MAPE | MSE | RMSE |
---|---|---|---|---|---|
Lab139 | LSTM | 5.9804 × 10−4 | 1.3983 | 6.6456 × 10−7 | 8.1437 × 10−4 |
BiLSTM | 6.8622 × 10−4 | 1.6185 | 8.1333 × 10−7 | 8.9885 × 10−4 | |
BiGRU | 4.7453 × 10−4 | 1.1571 | 4.1525 × 10−7 | 6.4409 × 10−4 | |
ERSO-GRU | 3.4329 × 10−4 | 0.7859 | 2.5012 × 10−7 | 4.9732 × 10−4 | |
Corridor_rm155 | LSTM | 6.8724 × 10−4 | 1.4415 | 7.1536 × 10−7 | 8.4559 × 10−4 |
BiLSTM | 7.0936 × 10−4 | 1.5649 | 7.5727 × 10−7 | 8.6966 × 10−4 | |
BiGRU | 4.7949 × 10−4 | 0.7473 | 3.8292 × 10−7 | 6.1840 × 10−4 | |
ERSO-GRU | 4.0665 × 10−4 | 0.6846 | 3.1759 × 10−7 | 5.6068 × 10−4 | |
Main_Lobby | LSTM | 6.1129 × 10−4 | 4.0924 | 6.4957 × 10−7 | 8.0544 × 10−4 |
BiLSTM | 6.8232 × 10−4 | 5.7196 | 8.1423 × 10−7 | 9.0118 × 10−4 | |
BiGRU | 4.3570 × 10−4 | 2.8235 | 3.5520 × 10−7 | 5.9579 × 10−4 | |
ERSO-GRU | 3.6049 × 10−4 | 2.1537 | 2.5163 × 10−7 | 4.9942 × 10−4 | |
Sports_Hall | LSTM | 6.5955 × 10−4 | 1.2397 | 8.5151 × 10−7 | 9.2237 × 10−4 |
BiLSTM | 6.7640 × 10−4 | 1.2836 | 8.5660 × 10−7 | 9.2524 × 10−4 | |
BiGRU | 4.6952 × 10−4 | 0.9228 | 4.4041 × 10−7 | 6.6353 × 10−4 | |
ERSO-GRU | 3.7218 × 10−4 | 0.6650 | 2.7051 × 10−7 | 5.1950 × 10−4 |
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Liu, Q.; Wang, P.; Sun, J.; Li, R.; Li, Y. Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer. Sensors 2023, 23, 6270. https://doi.org/10.3390/s23146270
Liu Q, Wang P, Sun J, Li R, Li Y. Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer. Sensors. 2023; 23(14):6270. https://doi.org/10.3390/s23146270
Chicago/Turabian StyleLiu, Qingli, Peiling Wang, Jiaxu Sun, Rui Li, and Yangyang Li. 2023. "Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer" Sensors 23, no. 14: 6270. https://doi.org/10.3390/s23146270
APA StyleLiu, Q., Wang, P., Sun, J., Li, R., & Li, Y. (2023). Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer. Sensors, 23(14), 6270. https://doi.org/10.3390/s23146270