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Found 7 papers, 1 papers with code
Deep Reinforcement Learning for mmWave Initial Beam Alignment
We investigate the applicability of deep reinforcement learning algorithms to the adaptive initial access beam alignment problem for mmWave communications using the state-of-the-art proximal policy optimization algorithm as an example.
Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements
The objective of channel charting is to learn a virtual map of the radio environment from high-dimensional CSI that is acquired by a multi-antenna wireless system.
Trainable Communication Systems: Concepts and Prototype
We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-wise mutual information (BMI) allows seamless integration with practical bit-metric decoding (BMD) receivers, as well as joint optimization of constellation shaping and labeling.
Information Theory Signal Processing Information Theory
Towards Practical Indoor Positioning Based on Massive MIMO Systems
We showcase the practicability of an indoor positioning system (IPS) solely based on Neural Networks (NNs) and the channel state information (CSI) of a (Massive) multiple-input multiple-output (MIMO) communication system, i. e., only build on the basis of data that is already existent in today's systems.
On Recurrent Neural Networks for Sequence-based Processing in Communications
In this work, we analyze the capabilities and practical limitations of neural networks (NNs) for sequence-based signal processing which can be seen as an omnipresent property in almost any modern communication systems.
Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction
A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding.
Deep Learning-Based Communication Over the Air
End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions.
