Robust noise-power estimation via primal–dual algorithms with measurements from a subset of network nodes

Iscar, 2018, Efficient noise variance estimation under pilot contamination for massive MIMO systems, IEEE Trans. Veh. Technol., 67, 2982, 10.1109/TVT.2017.2766226 Rakovic, 2015, Capacity-aware cooperative spectrum sensing based on noise power estimation, IEEE Trans. Commun., 63, 2428, 10.1109/TCOMM.2015.2433297 Mariani, 2011, Effects of noise power estimation on energy detection for cognitive radio applications, IEEE Trans. Commun., 59, 3410, 10.1109/TCOMM.2011.102011.100708 Gävert, 2022, Estimation of phase noise based on in-band and out-of-band frequency domain pilots, IEEE Trans. Commun., 70, 4780, 10.1109/TCOMM.2022.3171809 Wu, 2021, Maximum-likelihood, magnitude-based, amplitude and noise variance estimation, IEEE Signal Process. Lett., 28, 414, 10.1109/LSP.2021.3055464 Yuan, 2021, Optimally displaced threshold detection for discriminating binary coherent states using imperfect devices, IEEE Trans. Commun., 69, 2546, 10.1109/TCOMM.2020.3048341 Ebrahimzadeh, 2015, Sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing, IEEE Trans. Veh. Technol., 64, 1565, 10.1109/TVT.2014.2331681 Xu, 2020, RSSI-based direction of arrival estimation using maximum likelihood estimator and UHF antenna array, 1 Blatt, 2006, Energy-based sensor network source localization via projection onto convex sets, IEEE Trans. Signal Process., 54, 3614, 10.1109/TSP.2006.879312 Alves, 2020, Effective acoustic energy sensing exploitation for target sources localization in urban acoustic scenes, IEEE Sens. Lett., 4, 1, 10.1109/LSENS.2019.2960888 Guo, 2022, RSS-based localization for wireless underground battery-free sensor networks, IEEE Sens. Lett., 6, 1, 10.1109/LSENS.2022.3212689 Lin, 2022, Learning to identify malfunctioning sensors in a large-scale sensor network, IEEE Sens. J., 22, 2582, 10.1109/JSEN.2021.3138250 Ang, 2018, Optimizing energy consumption for big data collection in large-scale wireless sensor networks with mobile collectors, IEEE Syst. J., 12, 616, 10.1109/JSYST.2016.2630691 Zeng, 2018, Outlier-robust matrix completion via ℓp -minimization, IEEE Trans. Signal Process., 66, 1125, 10.1109/TSP.2017.2784361 Kim, 2021, Deep learning based low-rank matrix completion for IoT network localization, IEEE Wirel. Commun. Lett., 10, 2115, 10.1109/LWC.2021.3093983 Philip, 2017, Missing observations - method of analysis, J. Complement. Med. Altern. Healthc., 4 Mukhopadhyay, 2020, Imdlms: An imputation based LMS algorithm for linear system identification with missing input data, IEEE Trans. Signal Process., 68, 2370, 10.1109/TSP.2020.2983162 Farhangfar, 2007, A novel framework for imputation of missing values in databases, IEEE Trans. Syst. Man Cybern. A, 37, 692, 10.1109/TSMCA.2007.902631 Liu, 2015, Distributed censored regression over networks, IEEE Trans. Signal Process., 63, 5437, 10.1109/TSP.2015.2455519 Liu, 2017, Recursive least squares for censored regression, IEEE Trans. Signal Process., 65, 1565, 10.1109/TSP.2016.2646660 Afonso, 2015, Blind inpainting using ℓ0 and total variation regularization, IEEE Trans. Image Process., 24, 2239, 10.1109/TIP.2015.2417505 Mao, 2018, Spatio-temporal signal recovery based on low rank and differential smoothness, IEEE Trans. Signal Process., 66, 6281, 10.1109/TSP.2018.2875886 Romero, 2017, Kernel-based reconstruction of graph signals, IEEE Trans. Signal Process., 65, 764, 10.1109/TSP.2016.2620116 Girault, 2017, Grasp: A matlab toolbox for graph signal processing Jiang, 2020, Recovery of time-varying graph signals via distributed algorithms on regularized problems, IEEE Trans. Signal Inf. Process. Netw., 6, 540 Li, 2022, Acoustic mode measuring approach developed on generalized minimax-concave regularization and tikhonov regularization, IEEE Trans. Instrum. Meas., 71, 1, 10.1109/TIM.2022.3218574 Lashgari, 2018, Missing surface estimation based on modified tikhonov regularization: Application for destructed dental tissue, IEEE Trans. Image Process., 27, 2433, 10.1109/TIP.2018.2800289 Chen, 2015, Signal recovery on graphs: Variation minimization, IEEE Trans. Signal Process., 63, 4609, 10.1109/TSP.2015.2441042 Chen, 2018, Tensor nuclear norm-based low-rank approximation with total variation regularization, IEEE J. Sel. Top. Sign. Proces., 12, 1364, 10.1109/JSTSP.2018.2873148 Sant, 2022, Block-sparse signal recovery via general total variation regularized sparse Bayesian learning, IEEE Trans. Signal Process., 70, 1056, 10.1109/TSP.2022.3144948 Bertsekas, 1999 Nedic, 2001, Incremental subgradient methods for nondifferentiable optimization, SIAM J. Optim., 12, 109, 10.1137/S1052623499362111 Chambolle, 2011, A first-order primal-dual algorithm for convex problems with applications to imaging, J. Math. Imaging Vision, 40, 10.1007/s10851-010-0251-1 Mansoori, 2021, FlexPD: A flexible framework of first-order primal-dual algorithms for distributed optimization, IEEE Trans. Signal Process., 69, 3500, 10.1109/TSP.2021.3083981 Bismor, 2016, Review and comparison of variable step-size LMS algorithms, Int. J. Acoust. Vib., 21 Malitsky, 2018, A first-order primal-dual algorithm with linesearch, SIAM J. Optim., 28, 411, 10.1137/16M1092015 Antonante, 2022, Outlier-robust estimation: Hardness, minimally tuned algorithms, and applications, IEEE Trans. Robot., 38, 281, 10.1109/TRO.2021.3094984 Morales-Jimenez, 2015, Large dimensional analysis of robust M-estimators of covariance with outliers, IEEE Trans. Signal Process., 63, 5784, 10.1109/TSP.2015.2460225 Farahmand, 2012, Robust RLS in the presence of correlated noise using outlier sparsity, IEEE Trans. Signal Process., 60, 3308, 10.1109/TSP.2012.2189766 2019 Chen, 2015, Discrete signal processing on graphs: Sampling theory, IEEE Trans. Signal Process., 63, 6510, 10.1109/TSP.2015.2469645