Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff
Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff explores A novel deep learning framework for adaptive wireless broadcasting that optimizes image quality, transmission rate, and processing complexity for heterogeneous edge devices.. Commercial viability score: 4/10 in Wireless Communications AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisTopology-Preserving Deep Joint Source-Channel Coding for Semantic CommunicationViability: 2.0
- Builds On ThisOn the Rate-Distortion-Complexity Tradeoff for Semantic CommunicationViability: 3.0
- Builds On ThisBeyond Fixed Frames: Dynamic Character-Aligned Speech TokenizationViability: 3.0
- Prior WorkBringing Network Coding into Multi-Robot Systems: Interplay Study for Autonomous Systems over Wireless CommunicationsViability: 4.0
- Prior WorkSAFT: Sensitivity-Aware Filtering and Transmission for Adaptive 3D Point Cloud Communication over Wireless Channels