Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input-multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre- and post-processing of the sensor observations and received signal are proposed for the minimum mean square error (MMSE) estimation of a parameter vector. The proposed techniques exploit the limited scattering nature of the mmWave MIMO channel for formulating the hybrid transceiver design framework as a multiple measurement vectors (MMVs)-based sparse signal recovery problem. This is then solved using the iterative appealingly low-complexity simultaneous orthogonal matching pursuit (SOMP). Tailor-made designs are presented for WSNs operating under both total and per-sensor power constraints, while considering ideal noiseless as well as realistic noisy sensors. Furthermore, both the Bayesian Cramer-Rao lower bound and the centralized MMSE bound are derived for benchmarking the proposed decentralized estimation schemes. Our simulation results demonstrate the efficiency of the designs advocated and verify the analytical findings.

Hybrid precoders and combiners are designed for cooperative cell-free multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular networks for low complexity interference mitigation. Initially, we derive an optimal hybrid transmit beamformer (HTBF) for a broadcast scenario considering both total and per access point (AP) power constraints. Next, an optimal successive hybrid beamformer technique is proposed for unicast and multicast scenarios which relies on the optimal minimum variance distortionless response (MVDR). We demonstrate that it mitigates both the interuser and intergroup interference, while successively ensuring orthogonality to the previously scheduled users/user groups. Furthermore, it is shown theoretically that the proposed schemes are capable of supporting a large number of users. Subsequently, a Bayesian learning (BL) based method is conceived for jointly designing the RF and baseband precoders/combiners for the various scenarios considered. Furthermore, we also conceive the uplink counterpart of our HTBF scheme, which is based on maximizing the signal-to-interference-plus noise ratio (SINR) of each individual user. Finally, the efficacy of the proposed schemes is characterized by our extensive simulation results in terms of cancelling the interuser/intergroup interference, which improves the spectral efficiency.

This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization. Although the current versions of ADMM algorithm provide promising numerical results in producing solutions that are close to optimal for many convex and non-convex optimization problems, it remains unclear if they can converge to a stationary point for weakly convex and locally non-smooth functions. Through our analysis using the Moreau envelope function, we demonstrate that MADM can indeed converge to a stationary point under mild conditions. Our analysis also includes computing the bounds on the amount of change in the dual variable update step by relating the gradient of the Moreau envelope function to the proximal function. Furthermore, the results of our numerical experiments indicate that our method is faster and more robust than widely-used approaches.

This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices. Due to the decentralized nature of federated environments, distributed learning becomes essential for scalability and adaptability. Moreover, these environments are often plagued by outlier data, which presents substantial challenges to conventional methods, particularly in maintaining estimation accuracy and ensuring algorithm convergence. To mitigate these challenges, we propose a method that adopts an L1-norm robust formulation within a distributed sub-gradient framework, explicitly designed to handle these obstacles. Our approach addresses the problem in its original form, without resorting to iterative simplifications or approximations, resulting in enhanced computational efficiency and improved estimation accuracy. We demonstrate that our method converges to a stationary point, highlighting its effectiveness and reliability. Through numerical simulations, we confirm the superior performance of our approach, notably in outlier-rich environments, which surpasses existing state-of-the-art localization methods.

This paper studies quantile regression with non-convex and non-smooth sparse-penalties, such as minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). Although iterative coordinate descent and local linear approximation techniques can solve quantile regression problem, convergence is slow for MCP and SCAD penalties. However, alternating direction method of multipliers (ADMM) can be exploited to enhance the convergence speed. Hence, this paper proposes a new ADMM algorithm with an increasing penalty parameter, called IAD, to handle sparse-penalized quantile regression. We first investigate the convergence of the proposed algorithm and establish the conditions for convergence. Then, we present numerical results to demonstrate the efficacy of the proposed algorithm. Our results show that the proposed IAD algorithm can handle sparse-penalized quantile regression more effectively than the state-of-the-art methods.

This paper presents a private-partial distributed least mean square (PP-DLMS) algorithm that offers energy efficiency while preserving privacy and is suitable for applications with limited resources and strict security requirements. The proposed PP-DLMS allows every agent to exchange only a fraction of their perturbed data with neighbors during the collaboration process to minimize communication costs and guarantee privacy simultaneously. In order to understand how partial-sharing of perturbed data affects the learning performance, we conduct mean convergence analysis. Moreover, to investigate the privacy-preserving properties of the proposed algorithm, we characterize agent privacy in the presence of an honest-but-curious (HBC) adversary. Analytical results show that the proposed PP-DLMS is resilient against an HBC adversary by providing a fair energy-privacy trade-off compared to the conventional LMS algorithm. Numerical simulations corroborate the analytical findings.

This paper presents a majorization-minimization-based framework for learning time-varying graphs from spatial-temporal measurements with non-convex penalties. The proposed approach infers time-varying graphs by using the log-likelihood function in conjunction with two non-convex regularizers. Using the log-likelihood function under a total positivity constraint, we can construct the Laplacian matrix from the off-diagonal elements of the precision matrix. Furthermore, we employ non-convex regularizer functions to constrain the changes in graph topology and associated weight evolution to be sparse. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods in sparse and non-sparse situations.

This paper proposes an optimal scheduling policy for broadcasting spatio-temporally dependent observations to two remote estimators over a finite time horizon. The system comprises a scheduler that can broadcast one observation from one out of two spatio-temporally dependent processes at each time instant. Since the number of broadcasting instants for each sensor is constrained, the scheduler must plan the broadcasting that minimizes the time-averaged estimation error. As the scheduler cannot observe the measurements, it determines the expected estimation error based on the age-of information (AoI). Using AoI as a state variable, we derive a set of optimal scheduling policies that minimizes the average mean squared error (MSE) for any given time horizon. The policies provide the optimal number of transmission instances for each sensor and time-varying AoI thresholds for when to be scheduled. By studying how the MSE evolves with respect to the AoI generated by a given scheduling sequence, we can obtain an optimal policy using a low-complexity numerical method. Numerical results validate the theory and demonstrate how utilizing spatio-temporal dependencies together with AoI can enhance the estimation accuracy in a communication-constrained sensor network.

Distributed Kalman filtering techniques enable agents of a multiagent network to enhance their ability to track a system and learn from local cooperation with neighbors. Enabling this cooperation, however, requires agents to share information, which raises the question of privacy. This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) that protects local agent information by restricting and obfuscating the information exchanged. The derived PP-DKF embeds two state-of-the-art average consensus techniques that guarantee agent privacy. The resulting PP-DKF utilizes noise injection-based and decomposition-based privacy-preserving techniques to implement a robust distributed Kalman filtering solution against perturbation. We characterize the performance and convergence of the proposed PP-DKF and demonstrate its robustness against the injected noise variance. We also assess the privacy-preserving properties of the proposed algorithm for two types of adversaries, namely, an external eavesdropper and an honest-but-curious (HBC) agent, by providing bounds on the privacy leakage for both adversaries. Finally, several simulation examples illustrate that the proposed PP-DKF achieves better performance and higher privacy levels than the distributed Kalman filtering solutions employing contemporary privacy-preserving techniques.

This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) to protect the private information of individual network agents from being acquired by honest-but-curious (HBC) adversaries. The proposed approach endows privacy by incorporating noise perturbation and state decomposition. In particular, the PP-DKF provides privacy by restricting the amount of information exchanged with decomposition and concealing private information from adversaries through perturbation. We characterize the performance and convergence of the proposed PP-DKF and demonstrate its robustness against perturbation. The resulting PP-DKF improves agent privacy, defined as the mean squared estimation error of private data at the HBC adversary, without significantly affecting the overall filtering performance. Several simulation examples corroborate the theoretical results.