A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems
2026-07-08T08:51:41Z•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
What happened
Collection of newly announced arXiv papers (July 8, 2026) covering advances in information theory, wireless communications, coding, and security for next-generation networks. Highlights include: a survey of neural learn-to-compute methods for rate-distortion problems; a 3GPP-compliant CSI compression/prediction framework using Contrastive Predictive Coding with publicly available code; a Grover-based quantum-search assisted physical-layer security (PLS) scheme for CD-NOMA employing artificial-noise beamforming; product-coding constructions that convert bit-level capacity achievement into block
Why it matters
A reviewed impact interpretation has not been published for this record.
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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