963 citations · 3.4k across the 52 of their papers we have counts for
10 papers · 1 filter
Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding
Qiyu Hu, Yunlong Cai, Kai Kang +3
In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, w…
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer
Brian Rappaport, Emre Gönültaş, Jakob Hoydis +3
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state informa…
Learning OFDM Waveforms with PAPR and ACLR Constraints
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characterist…
Waveform Learning for Next-Generation Wireless Communication Systems
Fayçal Ait Aoudia, Jakob Hoydis
We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-b…
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning
Mateus P. Mota, Alvaro Valcarce, Jean-Marie Gorce +1
In this paper, we propose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) t…
End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
Fayçal Ait Aoudia, Jakob Hoydis
As communication systems are foreseen to enable new services such as joint communication and sensing and utilize parts of the sub-THz spectrum, the design of novel waveforms that c…
Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
Machine learning (ML) can be used in various ways to improve multi-user multiple-input multiple-output (MU-MIMO) receive processing. Typical approaches either augment a single proc…
End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment. However, it suffers from a p…
Unsupervised Learning in Next-Generation Networks: Real-Time Performance Self-Diagnosis
Faris B. Mismar, Jakob Hoydis
This letter demonstrates the use of unsupervised machine learning to enable performance self-diagnosis of next-generation cellular networks. We propose two simplified applications…
Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning
Fayçal Ait Aoudia, Jakob Hoydis
Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation. However, it suffers from a…