arXiv (OAI Expanded)
open access
Physics-Informed Direction-of-Arrival Estimation Over Distributed Edge Devices
Direction-of-arrival (DoA) estimation is a fundamental array processing task that has benefited substantially from deep learning. Deploying such methods using data distributed across multiple edge devices introduces communications overhead, due to the aggregation of large datasets that federated lea
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels#798758
- Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels#797344
- Beyond Random Couplings: Contrastive Noise Alignment in Generative Flows#1012670
- Smart beamforming and deep-learning reader for RFID systems in edge IoT.#218926
- Deep Tech to Space: Space Data Centers and AI Revolution at the Edge#204741
Record · ID 1110284
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive