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Chinese hackers allegedly stole data of more than 100,000 US Navy personnel
来源于:MIT Technology
Effective resource allocation plays a pivotal role for performance
optimization in wireless networks. Unfortunately, typical resource allocation
problems are mixed-integer nonlinear programming (MINLP) problems, which are
NP-hard in general. Machine learning-based methods recently emerge as a
disruptive way to obtain near-optimal performance for MINLP problems with
affordable computational complexity. However, a key challenge is that these
methods require huge amounts of training samples, which are difficult to obtain
in practice. Furthermore, they suffer from severe performance deterioration
when the network parameters change, which commonly happens and can be
characterized as the task mismatch issue. In this paper, to address the sample
complexity issue, instead of directly learning the input-output mapping of a
particular resource allocation algorithm, we propose a Learning to Optimize
framework for Resource Allocation, called LORA, that learns the pruning policy
in the optimal bran 查看全文>>