A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems
arXiv 2607.05417•580055c2ab8172543a717c8ead3a5293411a3635c0b1b40ce58caccf2bff5b81
3GPP6GCSI-compressionGrover-algorithmHAPSISACNOMANTNPLSartificial-noiseattacker-detection','HARQ','delay-modeling','queueing-theory','fbeamformingcarrier-shutdowncontrastive-predictive-codingenergy-efficiencygame-theoryinformation-theoryintegrated-sensing-and-communicationmutual-information-estimationneural-estimationphysical-layer-securityrate-distortionreinforcement-learningvariational-inferencewireless-communications
Paper metadata
- arXiv ID
- 2607.05417
- Version
- Not specified by this published record
- Category
- Mathematics — Information Theory (math.IT)
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Evidence and limitations
- Source ID
- arxiv_math_it
- Record identifier
- 580055c2ab8172543a717c8ead3a5293411a3635c0b1b40ce58caccf2bff5b81
- Enrichment time
- 2026-07-08T08:51:41Z
- AI-assisted enrichment
- Yes
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