Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks
arXiv 2606.05208•712e6785ad9a46f21557dfd03f6f4d2ba9eb88a95d6dcc39f85a1696c377795a
1-bit quantization6GCSI estimationJEPALatentWaveMIMOPyCC','equation discovery','structural identifiability','clu...RF signal classificationTHzTTDbeamformingdeep unfoldingditherfluid antennasfoundation modelslow-resolution ADC/DACpositioningreinforcement learningresource allocationschedulingsemantic communicationspatial reconfigurationtransformersvariational Bayesianwireless
Paper metadata
- arXiv ID
- 2606.05208
- Version
- Not specified by this published record
- Category
- Electrical Engineering and Systems Science — Signal Processing (eess.SP)
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Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- 712e6785ad9a46f21557dfd03f6f4d2ba9eb88a95d6dcc39f85a1696c377795a
- Enrichment time
- 2026-06-05T08:51:44Z
- AI-assisted enrichment
- Yes
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