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Found 17 papers, 10 papers with code
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.
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.
Automorphism Ensemble Decoding of Reed-Muller Codes
Reed-Muller (RM) codes are known for their good maximum likelihood (ML) performance in the short block-length regime.
Information Theory Information Theory
Iterative Detection and Decoding of Finite-Length Polar Codes in Gaussian Multiple Access Channels
We consider the usage of finite-length polar codes for the Gaussian multiple access channel (GMAC) with a finite number of users.
Information Theory Information Theory
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
Deep Learning-based Polar Code Design
In this work, we introduce a deep learning-based polar code construction algorithm.
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.
Decoder-in-the-Loop: Genetic Optimization-based LDPC Code Design
Moreover, GenAlg can be used to design LDPC codes with the aim of reducing decoding latency and complexity, leading to coding gains of up to $0. 325$ dB and $0. 8$ dB at BLER of $10^{-5}$ for both AWGN and Rayleigh fading channels, respectively, when compared to state-of-the-art short LDPC codes.
Information Theory Information Theory
Decoder-tailored Polar Code Design Using the Genetic Algorithm
We propose a new framework for constructing polar codes (i. e., selecting the frozen bit positions) for arbitrary channels, and tailored to a given decoding algorithm, rather than based on the (not necessarily optimal) assumption of successive cancellation (SC) decoding.
Genetic Algorithm-based Polar Code Construction for the AWGN Channel
We propose a new polar code construction framework (i. e., selecting the frozen bit positions) for the additive white Gaussian noise (AWGN) channel, tailored to a given decoding algorithm, rather than based on the (not necessarily optimal) assumption of successive cancellation (SC) decoding.
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.
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
