Matlab Code for "Robust one-shot estimation over shared networks in the presence of denial-of-service attacks"
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OptimalJammingProbability.m: Optimal jamming probability
$\varphi^\star$ for the proactive jammer. -
OptimalJammingProbability_VS_d.m: Optimal jamming probability
$\varphi^\star$ for the proactive jammer as a function of d. -
OptimalJammingProbability_VS_var.m: Optimal jamming probabilities
$\varphi^\star$ as a function of$\sigma^2$ . -
OptimalJammingProbability_VS_cd.m: Optimal jamming probability
$\varphi^\star$ for the proactive jammer as a function of$c$ and$d$ .
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OptimalTransmission_Jamming_Policy.m: Optimal Optimal transmission policy and jamming probability
$\varphi^\star$ for the proactive jammer over large-scale network. -
OptimalJammingProbability_VS_d.m: Optimal jamming probability
$\varphi^\star$ for the proactive jammer over large-scale network as a function of d. -
OptimalJammingProbability_VS_kappa.m: Optimal jamming probability
$\varphi^\star$ for the proactive jammer over large-scale network as a function of$\bar{\kappa}$ . -
OptimalJammingProbability_VS_cd.m: Optimal jamming probability
$\varphi^\star$ for the proactive jammer over large-scale network as a function of c and d. Here, X ~ N(0,1). -
OptimalJammingProbability_VS_ckappa.m: Optimal jamming probability
$\varphi^\star$ for the proactive jammer over large-scale network as a function of c and$\bar{\kappa}$ . Here, X ~ N(0,1). -
OptimalJammingProbability_VS_dkappa.m: Optimal jamming probability
$\varphi^\star$ for the proactive jammer over large-scale network as a function of d and$\bar{\kappa}$ . Here, X ~ N(0,1).
- MonteCarlo_AlgorithmComparison.m: Monte Carlo simulations for PGA-CCP and GDA in 1 dimension
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Optimalalphabeta_VS_var: Optimal jamming probabilities
$\alpha^\star$ and$\beta^\star$ as a function of$\sigma^2$ . - PGA_CCP_Algorithm: Convergence of PGA-CCP
- GDA_Algorithm.m: Convergence of GDA
- FirstNashEqulibiumChecker.m: Check whether approximate First Nash Equilibrium is satisfied
- grad_PGA.m Gradients for PGA
- grad_CCP.m: Gradients for CCP
- grad_GD.m: Gradients for GD
Description: the algorithm is accelerated by replacing iterations with matrix computation.
- MonteCarlo_MultiDim_AlgorithmComparison.m: Monte Carlo simulations for PGA-CCP and GDA for multidimensional state, the expectation is estimated by using the average of random samples
- MultiDim_PGA_CCP_Algorithm Convergence of PGA-CCP for multidimensional state
- MultiDim_GDA_Algorithm.m: Convergence of GDA for multidimensional state
- batch_grad_PGA.m Estimation of Gradients for PGA
- batch_grad_CCP.m: Estimation of Gradients for CCP
- batch_grad_GD.m: Estimation of Gradients for GD
- batch_FirstNashEqulibiumChecker.m: Check whether the estimated approximate First Nash Equilibrium is satisfied