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Found 22 papers, 6 papers with code
Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channels
Experiments are carried out under various conditions to compare the performance of the proposed NNBF framework against baseline methods zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming.
Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference
This paper investigates the significance of designing a reliable, intelligent, and true physical environment-aware precoding scheme by leveraging an accurately designed channel twin model to obtain realistic channel state information (CSI) for cellular communication systems.
How Critical is Site-Specific RAN Optimization? 5G Open-RAN Uplink Air Interface Performance Test and Optimization from Macro-Cell CIR Data
In this paper, we consider the importance of channel measurement data from specific sites and its impact on air interface optimization and test.
Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Design
We conduct experiments for diverse settings to compare the performance of NNBF-P with zero-forcing beamforming (ZFBF), minimum mean square error (MMSE) beamforming, and NNBF, which is also our deep learning based beamforming design without joint power allocation scheme.
Deep Learning Based Uplink Multi-User SIMO Beamforming Design
The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensive coverage, minimal latency and energy-efficient performance.
SVD-Embedded Deep Autoencoder for MIMO Communications
SVDembedded DAE largely outperforms theoretic linear precoding in terms of BER.
Deep Learning for Wireless Communications
Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom.
Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks
Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system.
Physical Layer Communications System Design Over-the-Air Using Adversarial Networks
This paper presents a novel method for synthesizing new physical layer modulation and coding schemes for communications systems using a learning-based approach which does not require an analytic model of the impairments in the channel.
Over the Air Deep Learning Based Radio Signal Classification
We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals.
Deep Learning Based MIMO Communications
We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder.
Information Theory Information Theory
Learning Approximate Neural Estimators for Wireless Channel State Information
Estimation is a critical component of synchronization in wireless and signal processing systems.
Deep Architectures for Modulation Recognition
We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition.
An Introduction to Deep Learning for the Physical Layer
We present and discuss several novel applications of deep learning for the physical layer.
Semi-Supervised Radio Signal Identification
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy.
Recurrent Neural Radio Anomaly Detection
We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies.
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks
We investigate sequence machine learning techniques on raw radio signal time-series data.
Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention
We address the problem of learning efficient and adaptive ways to communicate binary information over an impaired channel.
Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent
This paper presents research in progress investigating the viability and adaptation of reinforcement learning using deep neural network based function approximation for the task of radio control and signal detection in the wireless domain.
Radio Transformer Networks: Attention Models for Learning to Synchronize in Wireless Systems
This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signals structure based on optimization of the network for classification accuracy, sparse representation, and regularization.
Unsupervised Representation Learning of Structured Radio Communication Signals
We explore unsupervised representation learning of radio communication signals in raw sampled time series representation.
Convolutional Radio Modulation Recognition Networks
We study the adaptation of convolutional neural networks to the complex temporal radio signal domain.
