10 accepted papers
We consider the problem of power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium. To achieve both a high total rate and a level of fairness across users, we formulate a policy optimization problem with…
We consider the problem of resource allocation in large scale wireless networks. When contextualizing wireless network structures as graphs, we can model the limits of very large wireless systems as manifolds. To solve the problem in the machine learning framework, we propose the use of Manifold Neu…
We consider optimal resource allocation problems under asynchronous wireless network setting. Without explicit model knowledge, we design an unsupervised learning method based on Aggregation Graph Neural Networks (Agg-GNNs). Depending on the localized aggregated information structure on each network…
Optimal resource allocation in real-world wireless systems is rather challenging, not only due to the unavailability of accurate statistical channel models, but also because expressions of maximal or achievable information rates are most often unknown, or not adequately precise. Under a modular stoc…
We consider the problem of finding optimal power allocations subject to system constraints in ad-hoc wireless networks. The resulting optimization problem has the form of a constrained learning problem, motivating the use of a function parameterization such as a neural network. While such a policy c…
We consider the problem of allocating radio resources over wireless communication links to control a series of independent low-latency wireless control systems common in industrial settings. Supporting wireless control in time sensitive settings requires fast data rates over wireless links, which co…
We consider the problem of finding optimal resource allocations subject to system constraints in a generic class of problems in wireless communications. These problems are inherently challenging due to functional optimization and potential non-convexities. However, these problems can be observed to…
Most second order methods are inapplicable to large scale empirical risk minimization (ERM) problems because both, the number of samples N and number of parameters p are large. Large N makes it costly to evaluate Hessians and large p makes it costly to invert Hessians. This paper propose a novel ada…
This paper considers the resource allocation problem in wireless systems over an unknown time-varying non-stationary channel. The goal is to maximize a utility function, such as a capacity function, over a set of wireless nodes while satisfying a set of resource constraints. To bypass the need for a…
We present an incremental Broyden-Fletcher-Goldfarb-Shanno (BFGS) method as a quasi-Newton algorithm with a cyclically iterative update scheme for solving large-scale optimization problems. The proposed incremental quasi-Newton (IQN) algorithm reduces computational cost relative to traditional quasi…