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Found 5 papers, 0 papers with code
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection
In this paper, we incorporate multi-granular system dynamics (MSDs) into FL, including (M1) dynamic wireless channel capacity, captured by a set of discrete-time events, called $\mathscr{D}$-Events, and (M2) dynamic datasets of users.
Interference Cancellation GAN Framework for Dynamic Channels
Symbol detection is a fundamental and challenging problem in modern communication systems, e. g., multiuser multiple-input multiple-output (MIMO) setting.
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point
CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility.
Adversarial Neural Networks for Error Correcting Codes
To demonstrate the performance of our framework, we combine it with the very recent neural decoders and show improved performance compared to the original models and traditional decoding algorithms on various codes.
On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge
Edge computing has revolutionized the world of mobile and wireless networks world thanks to its flexible, secure, and performing characteristics.
