Collaborative Networked Estimation and Inference through Graph Neural Networks Graph neural networks (GNNs) are deep learning architectures that learn powerful representations of graphs and graph signals. GNNs have shown remarkable performance across various domains, such as biology and drug discovery, quantum chemistry, robotics, social networks, and recommender systems. Their success can be attributed to their […]
Rethinking Deep Learning Compression Using Information Theoretic Structures Neural compression has brought tremendous progress in designing lossy compressors with good rate-distortion (RD) performance at low complexity. Thus far, neural compression design involves transforming the source to a latent vector, which is then rounded to integers and entropy coded. While this approach has been shown to […]
Data-driven Adaptive Network Models: Quantitative Group Testing The quantitative group testing (QGT) problem aims at learning an underlying binary vector x of length n with a sparsity parameter k. The information acquisition process about x involves conducting pooled measurements, also known as group tests, where the outcome reveals the number of ones within the pool. […]
Empirical Network Optimization via Non-uniform Sampling Large-scale distributed optimization is a key component of the design of multi-scale network protocols. Traditionally, architecture-level hyperparameters and protocol parameters are optimized manually at run-time, which requires a human fine-tuning step to produce an optimized network performance metric. We will capitalize on the abundance of data and past design […]
Empirical Network Optimization via Distributed Zeroth-order Optimization Distributed zeroth-order optimization forms the backbone of empirical network optimization, and has wide applications in federated learning, where multiple agents use their local data and computation to collectively train a model. There are two main challenges in practical network optimization tasks. The first is the unavailable gradient of […]
Sequential Learning and Decision Making in Networks Millimeter-wave (mmWave) with phase array systems are utilized to increase the data rate in wireless communication systems. In order to perform beamforming, however, one must find the Angle of Arrival (AoA). To estimate the AoA for the mmWave systems, one must also consider the cost of the receiver […]
Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach In enterprise settings, it is vital to manage network operations to support multiple use cases with different requirements. Additionally, 3GPP includes architectures to integrate Wi-Fi in converged connectivity (5G + Wi-Fi) in enterprise. Network slicing allows an access point (AP) to allocate the network resources across […]
