Zobrazeno 1 - 10
of 87
pro vyhledávání: '"NaderiAlizadeh, Navid"'
Network slicing is a key feature in 5G/NG cellular networks that creates customized slices for different service types with various quality-of-service (QoS) requirements, which can achieve service differentiation and guarantee service-level agreement
Externí odkaz:
http://arxiv.org/abs/2405.05748
We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a multi-hop wireless network with statistically-identical agents. Agents cache the most recent samples from others and communicate over wireless coll
Externí odkaz:
http://arxiv.org/abs/2404.03227
Deep unrolling, or unfolding, is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. However, the convergence guarantees and generalizability of the unrolled networks are s
Externí odkaz:
http://arxiv.org/abs/2312.15788
This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access to only local information. We delineate a novel State Augmentation (SA)
Externí odkaz:
http://arxiv.org/abs/2310.00248
Continual learning is inherently a constrained learning problem. The goal is to learn a predictor under a no-forgetting requirement. Although several prior studies formulate it as such, they do not solve the constrained problem explicitly. In this wo
Externí odkaz:
http://arxiv.org/abs/2310.00154
Algorithm unrolling has emerged as a learning-based optimization paradigm that unfolds truncated iterative algorithms in trainable neural-network optimizers. We introduce Stochastic UnRolled Federated learning (SURF), a method that expands algorithm
Externí odkaz:
http://arxiv.org/abs/2305.15371
We consider a radio resource management (RRM) problem in a multi-user wireless network, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We propose a state-augmented par
Externí odkaz:
http://arxiv.org/abs/2210.16412
We propose a federated methodology to learn low-dimensional representations from a dataset that is distributed among several clients. In particular, we move away from the commonly-used cross-entropy loss in federated learning, and seek to learn share
Externí odkaz:
http://arxiv.org/abs/2210.00299
We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-aug
Externí odkaz:
http://arxiv.org/abs/2207.02242
We consider the problems of user selection and power control in wireless interference networks, comprising multiple access points (APs) communicating with a group of user equipment devices (UEs) over a shared wireless medium. To achieve a high aggreg
Externí odkaz:
http://arxiv.org/abs/2203.11012