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pro vyhledávání: '"Alizadeh, Mehrazin"'
This paper develops a unified framework to maximize the network sum-rate in a multi-user, multi-BS downlink terahertz (THz) network by optimizing user associations, number and bandwidth of sub-bands in a THz transmission window (TW), bandwidth of lea
Externí odkaz:
http://arxiv.org/abs/2408.03451
Autor:
Tomut, Andrei, Jahromi, Saeed S., Sarkar, Abhijoy, Kurt, Uygar, Singh, Sukhbinder, Ishtiaq, Faysal, Muñoz, Cesar, Bajaj, Prabdeep Singh, Elborady, Ali, del Bimbo, Gianni, Alizadeh, Mehrazin, Montero, David, Martin-Ramiro, Pablo, Ibrahim, Muhammad, Alaoui, Oussama Tahiri, Malcolm, John, Mugel, Samuel, Orus, Roman
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and l
Externí odkaz:
http://arxiv.org/abs/2401.14109
Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework
Autor:
Alizadeh, Mehrazin, Tabassum, Hina
Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users' quality-of-service (QoS) constraints, guaranteeing constraint sat
Externí odkaz:
http://arxiv.org/abs/2306.01787
There exists many resource allocation problems in the field of wireless communications which can be formulated as the generalized assignment problems (GAP). GAP is a generic form of linear sum assignment problem (LSAP) and is more challenging to solv
Externí odkaz:
http://arxiv.org/abs/2103.14548
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