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Found 7 papers, 1 papers with code
Applications of Deep Learning to the Design of Enhanced Wireless Communication Systems
First, we describe a neural network (NN)-based block strategy, where an NN is optimized to replace a block in a communication system.
Learning OFDM Waveforms with PAPR and ACLR Constraints
An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characteristics.
Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems
Machine learning (ML) can be used in various ways to improve multi-user multiple-input multiple-output (MU-MIMO) receive processing.
End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints
Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment.
Machine Learning for MU-MIMO Receive Processing in OFDM Systems
Machine learning (ML) starts to be widely used to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) receivers.
Deep HyperNetwork-Based MIMO Detection
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem.
Deep Reinforcement Learning Autoencoder with Noisy Feedback
However, this approach requires feedback of real-valued losses from the receiver to the transmitter during training.
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