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169,181 papers · 148 categories

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0111 · Aug 201719922001200920182026
5 results for ACK/NACK

Reinforcement learning improves wireless systems' rate adaptation.

problem Optimizing rate adaptation in 4G/5G systems using ACK/NACK feedback.
method Formulated as a Multi-Armed Bandit problem, proposed binary search algorithm with PAC guarantees.
result Achieved PAC solution for OLLA with binary search, outperforming UCB methods.

Optimizes data transmission timing to minimize user information age.

problem Minimizing long-term average age of information in multi-user networks.
method Reinforcement learning applied to scheduling decisions without channel state information.
result RL approach effectively reduces AoI compared to traditional ARQ and HARQ.

We prove that within a certain threshold, the odd Betti numbers of any compact almost-hermitian manifold satisfying a degenerate Kähler condition are even, and the even Betti numbers are strictly positive.

problem The topology of Kähler manifolds is largely determined by the geometry due to its rigidity.
method We prove that within a certain threshold, the odd Betti numbers of any compact almost-hermitian manifold satisfying a degenerate Kähler condition are even, and the even Betti numbers are strictly positive.
result We prove that within a certain threshold, the odd Betti numbers of any compact almost-hermitian manifold satisfying a degenerate Kähler condition are even, and the even Betti numbers are strictly positive.

New algorithm optimizes beam and rate allocation in mmWave systems for multiple users.

problem Optimizing beam and rate allocation in mmWave systems for multiple users with limited feedback.
method Introducing SAT-CTS, a combinatorial semi-bandit policy with satisficing objective.
result SAT-CTS achieves finite-time regret bounds and reduces satisficing regret in mmWave systems.

Deep learning enhances jamming and defense in wireless communications.

problem Improving wireless communication security against jamming attacks.
method Adversarial machine learning for jamming and a GAN for training data augmentation.
result Deep learning significantly reduces transmitter performance compared to random jamming.