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Found 7 papers, 0 papers with code
WAIR-D: Wireless AI Research Dataset
The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models.
Distributed Learning for Time-varying Networks: A Scalable Design
The wireless network is undergoing a trend from "onnection of things" to "connection of intelligence".
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate.
Realistic Channel Models Pre-training
In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.
Deep Reinforcement Learning for Scheduling in Cellular Networks
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia.
Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks
Specifically, before each SC decoding attempt, a long short-term memory (LSTM) network is exploited to either (i) locate the first error bit, or (ii) undo a previous `wrong' flip.
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models
Accurate prediction of fading channel in future is essential to realize adaptive transmission and other methods that can save power and provide gains.
