Rate Maximization

Rate maximization focuses on optimizing the efficiency of resource allocation in various systems, aiming to achieve the highest possible data transmission rates or performance levels. Current research emphasizes developing efficient algorithms and model architectures, such as deep reinforcement learning (including off-policy methods), graph neural networks, and heuristic approaches combined with optimization techniques like semi-definite programming, to solve complex rate maximization problems in communication networks and other domains. These advancements are crucial for improving the performance of wireless communication systems, enhancing machine learning model fairness, and achieving optimal estimation in challenging statistical settings like off-policy evaluation. The resulting improvements in efficiency and performance have significant implications for both theoretical understanding and practical applications across diverse fields.

Papers

April 14, 2024

Heuristic Solution to Joint Deployment and Beamforming Design for STAR-RIS Aided Networks
Bai Yan, Qi Zhao, Jin Zhang, J. Andrew Zhang
Specific Heuristic Reconfigurable Intelligent Surface Real Time Deployment Concurrent Transmission Hybrid Beamforming Beamforming Design STAR Ri Optimal Deployment Rate Maximization

October 12, 2023

Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach
Heasung Kim, Sravan Kumar Ankireddy
Deep Reinforcement Learning Environment Exploration Reinforcement Learning Policy Joint Beamforming Parameterized Network Rate Maximization

June 14, 2023

Nonparametric regression using over-parameterized shallow ReLU neural networks
Yunfei Yang, Ding-Xuan Zhou
Neural Network Shallow Neural Network Wide Neural Network Nonparametric Regression Shallow ReLU Rate Maximization

January 20, 2023

Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks
Tharaka Perera, Saman Atapattu, Yuting Fang, Prathapasinghe Dharmawansa, Jamie Evans
Graph Neural Network Resource Allocation Resource Management Full Duplex Pairwise Alignment Rate Maximization

June 27, 2022

Benign overfitting and adaptive nonparametric regression
Julien Chhor, Suzanne Sigalla, Alexandre B. Tsybakov
Linear Estimator Benign Overfitting Nonparametric Regression Average Smoothness Square Loss Rate Maximization

January 31, 2022

Learning Fair Representations via Rate-Distortion Maximization
Somnath Basu Roy Chowdhury, Snigdha Chaturvedi
Text Representation Fair Representation Demographic Attribute Biased Decision Rate Distortion Function Rate Distortion Optimization Rate Maximization

January 17, 2022

On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation
Xiaohong Chen, Zhengling Qi
Policy Evaluation Infinite Horizon Function Estimation Smoothness Constraint Nonparametric Instrumental Variable Rate Maximization

Rate Maximization

Papers

Heuristic Solution to Joint Deployment and Beamforming Design for STAR-RIS Aided Networks

Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach

Nonparametric regression using over-parameterized shallow ReLU neural networks

Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks

Benign overfitting and adaptive nonparametric regression

Learning Fair Representations via Rate-Distortion Maximization

On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation