End-to-End Deep Learning in Wireless Communication Systems: A Tutorial Review
2026-03-16T08:51:44Z•dee7a4aa16c38f1a0ac2869fc150253d1f5215c98386602ab5622f1dea25df2e
MDS-codesRISReed-SolomonUAVadversarial-robustnessautoencoderbanditsbeamformingchannel-estimationcoding-theorycomplex-probability-measuresdeep-learningindex-modulationinformation-theorylow-altitude-communicationsmMIMOmovable-antennamutual-couplingnonconvex-optimizationphysical-layerreconfigurable-intelligent-surfacesecurity-implicationsspatial-path-IMsub-packetizationwireless-communications
What happened
This silver document is an arXiv feed (March 16, 2026) containing multiple research preprints across wireless communications, coding theory, and information/machine learning theory. Major themes: an end-to-end deep-learning (autoencoder) survey for physical-layer (PHY) optimization; complex-valued probability measures and new information-theoretic quantities; a partial-exclusion repair scheme and RS-code constructions reducing MDS sub-packetization for single-node repair; RIS-aided mMIMO with spatial-path index modulation (SPIM) and hybrid beamforming for spectral-efficiency gains; analyses of
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_math_it
- Record identifier
- dee7a4aa16c38f1a0ac2869fc150253d1f5215c98386602ab5622f1dea25df2e
- Enrichment time
- 2026-03-16T08:51:44Z
- AI-assisted enrichment
- Yes
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