Search Results for author:
Found 11 papers, 7 papers with code
ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection.
DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems
We demonstrate the efficacy of DUIDD using NVIDIA's Sionna link-level simulator in a 5G-near multi-user MIMO-OFDM wireless system with a novel low-complexity soft-input soft-output data detector, an optimized low-density parity-check decoder, and channel vectors from a commercial ray-tracer.
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics
We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic.
Bit Error and Block Error Rate Training for ML-Assisted Communication
Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention.
Low Complexity Hybrid Beamforming for mmWave Full-Duplex Integrated Access and Backhaul
We consider an integrated access and backhaul (IAB) node operating in full-duplex (FD) mode.
Annealed Langevin Dynamics for Massive MIMO Detection
Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term -- the score of the likelihood -- by a neural network.
Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard
Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration.
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics
Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem.
Robust MIMO Detection using Hypernetworks with Learned Regularizers
Our method is based on hypernetworks that generate the parameters of a neural network-based detector that works well on a specific channel.
Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification
The estimation results of DPN along with its blind decoding performance are shown to outperform a blind signal processing algorithm for BPSK and QPSK on a simulated dataset.
Combining Deep Learning and Linear Processing for Modulation Classification and Symbol Decoding
Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding.
