4 papers
Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS
Mohanad Obeed, Ming Jian
Deep neural networks (DNNs) have been increasingly explored for receiver design because they can handle complex environments without relying on explicit channel models. Nevertheles…
CoNet-Rx: Collaborative Neural Networks for OFDM Receivers
Mohanad Obeed, Ming Jian
Deep learning (DL) based methods for orthogonal frequency division multiplexing (OFDM) radio receivers demonstrated higher signal detection performance compared to the traditional…
Hybrid Neural/Traditional OFDM Receiver with Learnable Decider
Mohanad Obeed, Ming Jian
Deep learning (DL) methods have emerged as promising solutions for enhancing receiver performance in wireless orthogonal frequency-division multiplexing (OFDM) systems, offering si…
Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers
Mohanad Obeed, Ming Jian
Towards fast, hardware-efficient, and low-complexity receivers, we propose a compression-aware learning approach and examine it on free-space optical (FSO) receivers for turbulence…