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Found 6 papers, 0 papers with code
Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes
Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are gaining interest recently due to their improved performance and flexibility; particularly for communication scenarios in which high-performing structured code designs do not exist.
All you need is feedback: Communication with block attention feedback codes
Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions.
AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
The training methods can potentially be generalized to other wireless communication applications with machine learning.
DRF Codes: Deep SNR-Robust Feedback Codes
We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code.
Spatial-Interference Aware Cooperative Resource Allocation for 5G NR Sidelink Communications
Distributed resource allocation (RA) schemes have been introduced in cellular vehicle-to-everything (C-V2X) standard for vehicle-to-vehicle (V2V) sidelink (SL) communications to share the limited spectrum (sub-6GHz) efficiently.
LoS-Map Construction for Proactive Relay of Opportunity Selection in 6G V2X Systems
Recent advances in Vehicle-to-Everything (V2X) technology and the upcoming sixth-generation (6G) network will dawn a new era for vehicular services with enhanced communication capabilities.
