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Found 4 papers, 0 papers with code
Learning to Estimate: A Real-Time Online Learning Framework for MIMO-OFDM Channel Estimation
In this paper we introduce StructNet-CE, a novel real-time online learning framework for MIMO-OFDM channel estimation, which only utilizes over-the-air (OTA) pilot symbols for online training and converges within one OFDM subframe.
Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing
The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols.
RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection
The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity.
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G
Thus, in this paper, we design a novel edge computing-assisted federated learning framework, in which the communication constraints between IoT devices and edge servers and the effect of various IoT devices on the training accuracy are taken into account.
