Table 2 Simulation hyperparameters.
From: A sparse wavelength aware learning framework for robust FSO channel estimation
S.No | Method/algorithm | Parameter | Value |
|---|---|---|---|
1 | Proposed SWALNet | Number of Hidden Units (h) | 64 |
2 | Batch Size | 128 | |
3 | Learning Rate | 0.001 | |
4 | Epochs | 200 | |
5 | Sparsity Regularization Coeff | 0.005 | |
6 | Activation Function | ReLU | |
7 | Optimizer | Adam | |
8 | Loss Function | MSE | |
9 | Input Feature Dimension | 8 | |
10 | LMS | Step Size (μ) | 0.01 |
11 | Initial Weight Vector | Zero | |
12 | Update Rule | Gradient Descent | |
13 | RLS | Forgetting Factor (λ) | 0.99 |
14 | Initial Covariance Matrix | Identity Matrix | |
15 | Regularization Parameter (δ) | 0.001 | |
16 | Kalman Filter | Process Noise Covariance (Q) | 1e−4 |
17 | Measurement Noise Covariance (R) | 1e−2 | |
18 | Initial State Estimate | Zero | |
19 | State Transition Matrix (A) | Identity Matrix | |
20 | Fully Connected DNN | Number of Layers | 3 |
21 | Neurons per Layer | 64 | |
22 | Activation Function | Tanh | |
23 | Learning Rate | 0.0005 | |
24 | Epochs | 150 | |
25 | Optimizer | RMSprop |
