Search Results for author:
Found 19 papers, 2 papers with code
Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks
Herein, a novel ensemble Q-learning algorithm that addresses the performance and complexity challenges of the traditional Q-learning algorithm for optimizing wireless networks is presented.
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy Optimization
Reinforcement learning (RL) is a classical tool to solve network control or policy optimization problems in unknown environments.
Optimized Parameter Design for Channel State Information-Free Location Spoofing
Numerical results are provided, validating the theoretical analysis and showing that the root-mean-square error for eavesdropper's localization can be more than 150 m with the optimized delay-angle shifts for DAIS.
Interference-Constrained Scheduling of a Cognitive Multi-hop Underwater Acoustic Network
The network consists of primary and secondary users, with multi-hop transmission adopted for both user types to provide reliable communications.
Channel State Information-Free Location-Privacy Enhancement: Delay-Angle Information Spoofing
In this paper, a delay-angle information spoofing (DAIS) strategy is proposed for location-privacy enhancement.
Channel State Information-Free Location-Privacy Enhancement: Fake Path Injection
Two closed-form, lower bounds on the illegitimate devices' estimation error are derived via the analysis of the Fisher information of the location-relevant channel parameters, thus characterizing the enhanced location-privacy.
Matrix Approximation with Side Information: When Column Sampling is Enough
A theoretical spectral error bound is provided, which captures the possible inaccuracies of the side information.
Channel State Information-Free Artificial Noise-Aided Location-Privacy Enhancement
In this paper, an artificial noise-aided strategy is presented for location-privacy preservation.
UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks
The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments.
Uncertainty-Based Non-Parametric Active Peak Detection
Active, non-parametric peak detection is considered.
Joint Localization and Orientation Estimation in Millimeter-Wave MIMO OFDM Systems via Atomic Norm Minimization
Herein, an atomic norm based method for accurately estimating the location and orientation of a target from millimeter-wave multi-input-multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented.
Atomic Norm Based Localization and Orientation Estimation for Millimeter-Wave MIMO OFDM Systems
Herein, an atomic norm based method for accurately estimating the location and orientation of a target from millimeter-wave multi-input-multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented.
Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization
In particular, we show that the method converges at a rate $O(log_2 k/\sqrt k)$ to the optimal solution, when the underlying objective function is strongly convex and smooth.
Common Information Belief based Dynamic Programs for Stochastic Zero-sum Games with Competing Teams
For this general model, we provide bounds on the upper (min-max) and lower (max-min) values of the game.
Multiagent Systems Systems and Control Systems and Control
Design of false data injection attack on distributed process estimation
Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering.
Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach
A Bayesian variant of the existing upper confidence bound (UCB) based approaches is proposed.
Data-driven sensor scheduling for remote estimation in wireless networks
Sensor scheduling is a well studied problem in signal processing and control with numerous applications.
Sequential Experiment Design for Hypothesis Verification
In the exploration phase, selection of experiments is such that a moderate level of confidence on the true hypothesis is achieved.
Policy Design for Active Sequential Hypothesis Testing using Deep Learning
Deep learning can be used as a tool for designing better heuristics in such problems.
