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Found 44 papers, 16 papers with code
Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates
This paper proposes a novel channel response-based scheme that, unlike the state of the art, estimates and compensates for the phase errors in the RT-generated channel responses.
Learning Radio Environments by Differentiable Ray Tracing
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs).
Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow.
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.
Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems
Localization services for wireless devices play an increasingly important role in our daily lives and a plethora of emerging services and applications already rely on precise position information.
Graph Neural Networks for Channel Decoding
In this work, we propose a fully differentiable graph neural network (GNN)-based architecture for channel decoding and showcase a competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes.
Deep Learning-Based Synchronization for Uplink NB-IoT
We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT).
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow.
Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory
Another important use of post-equalization SINR is for physical layer (PHY) abstraction, where several PHY blocks like the channel encoder, the detector, and the channel decoder are replaced by an abstraction model in order to speed up system-level simulations.
Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications
This is the second part of a two-part paper that focuses on link-adaptation (LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems with non-linear receivers.
Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier
In particular, we consider a scenario where the transmitter power amplifier is operating in a nonlinear manner, and ML is used to optimize the waveform to minimize the out-of-band emissions.
Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding
To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed.
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state information (CSI) databases that are passively collected at infrastructure base stations or access points.
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.
Waveform Learning for Next-Generation Wireless Communication Systems
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)-based detector.
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning
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) to come up with a medium access control (MAC) protocol in a multiple access scenario.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
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 can support these emerging applications becomes increasingly challenging.
Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning
Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation.
Bayesian Optimization for Radio Resource Management: Open Loop Power Control
We provide the reader with an accessible yet rigorous introduction to Bayesian optimisation with Gaussian processes (BOGP) for the purpose of solving a wide variety of radio resource management (RRM) problems.
Bayesian Optimisation
Gaussian Processes
+1
Information Theory
Information Theory
Toward a 6G AI-Native Air Interface
Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Massive multiple-input multiple-output (MIMO) for 5G.
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.
End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication
The first comes from a neural network (NN)-based receiver operating over a large number of subcarriers and OFDM symbols which allows to significantly reduce the number of orthogonal pilots without loss of bit error rate (BER).
Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems
We introduce a trainable coded modulation scheme that enables joint optimization of the bit-wise mutual information (BMI) through probabilistic shaping, geometric shaping, bit labeling, and demapping for a specific channel model and for a wide range of signal-to-noise ratios (SNRs).
Deep HyperNetwork-Based MIMO Detection
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem.
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
Learning to Communicate and Energize: Modulation, Coding and Multiple Access Designs for Wireless Information-Power Transmission
Relying on the proposed model, the learning problem of modulation design for Simultaneous Wireless Information-Power Transmission (SWIPT) over a point-to-point link is studied.
Information Theory Signal Processing Information Theory
"Machine LLRning": Learning to Softly Demodulate
Soft demodulation, or demapping, of received symbols back into their conveyed soft bits, or bit log-likelihood ratios (LLRs), is at the very heart of any modern receiver.
Joint Learning of Geometric and Probabilistic Constellation Shaping
In this work, we show how autoencoders can be leveraged to perform probabilistic shaping of constellations.
Adaptive Neural Signal Detection for Massive MIMO
We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channels with the same or lower computational complexity.
Transmitter Classification With Supervised Deep Learning
Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others.
Towards Hardware Implementation of Neural Network-based Communication Algorithms
There is a recent interest in neural network (NN)-based communication algorithms which have shown to achieve (beyond) state-of-the-art performance for a variety of problems or lead to reduced implementation complexity.
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.
Model-free Training of End-to-end Communication Systems
The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model.
Hardware Distortion Correlation Has Negligible Impact on UL Massive MIMO Spectral Efficiency
To determine when this approximation is accurate, basic properties of distortion correlation are first uncovered.
Information Theory Information Theory
Deep Reinforcement Learning Autoencoder with Noisy Feedback
However, this approach requires feedback of real-valued losses from the receiver to the transmitter during training.
Information Theory Information Theory
End-to-End Learning of Communications Systems Without a Channel Model
The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model.
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.
Massive MIMO has Unlimited Capacity
The capacity of cellular networks can be improved by the unprecedented array gain and spatial multiplexing offered by Massive MIMO.
Information Theory Information Theory
An Introduction to Deep Learning for the Physical Layer
We present and discuss several novel applications of deep learning for the physical layer.
On Deep Learning-Based Channel Decoding
We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes.
Information Theory Information Theory
Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?
Numerical and analytical results show that the maximal EE is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve a relatively large number of users using ZF processing.
Information Theory Networking and Internet Architecture Information Theory
Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain.
Information Theory Information Theory
