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Found 27 papers, 7 papers with code
CRAP Part II: Clutter Removal with Continuous Acquisitions Under Phase Noise
The mitigation of clutter is an important research branch in Integrated Sensing and Communication (ISAC), one of the emerging technologies of future cellular networks.
Antenna Array Design for Mono-Static ISAC
In this work, we propose a model to evaluate the angular capabilities of a mono-static setup, constrained to the shape of the communications array and its topology requirements in wireless networks.
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).
Multi-Target Localization in Multi-Static Integrated Sensing and Communication Deployments
In contrast, in this work we propose an ensemble of techniques for processing the information gathered from multiple sensing nodes, jointly observing an environment with multiple targets.
CRAP: Clutter Removal with Acquisitions Under Phase Noise
One of those is clutter removal, which should be applied to remove the influence of unwanted components, scattered by the environment, in the acquired sensing signal.
Component Training of Turbo Autoencoders
In this paper we extend the component training to structures with an inner and outer autoencoder, where we propose a new 1-bit quantization strategy for the encoder outputs based on the underlying communication problem.
Learning to exploit z-Spatial Diversity for Coherent Nonlinear Optical Fiber Communication
Higher-order solitons inherently possess a spatial periodicity along the propagation axis.
Deep Reinforcement Learning for mmWave Initial Beam Alignment
We investigate the applicability of deep reinforcement learning algorithms to the adaptive initial access beam alignment problem for mmWave communications using the state-of-the-art proximal policy optimization algorithm as an example.
Optimizing Serially Concatenated Neural Codes with Classical Decoders
For improving short-length codes, we demonstrate that classic decoders can also be used with real-valued, neural encoders, i. e., deep-learning based codeword sequence generators.
Learning Quantization in LDPC Decoders
Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding.
Probabilistic 5G Indoor Positioning Proof of Concept with Outlier Rejection
In this work, we introduce an iterative positioning method that reweights the time of arrival (ToA) and angle of arrival (AoA) measurements originating from multiple locators in order to efficiently remove outliers.
Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements
The objective of channel charting is to learn a virtual map of the radio environment from high-dimensional CSI that is acquired by a multi-antenna wireless system.
Geometry-Based Phase and Time Synchronization for Multi-Antenna Channel Measurements
Synchronization of transceiver chains is a major challenge in the practical realization of massive MIMO and especially distributed massive MIMO.
A Computationally Efficient 2D MUSIC Approach for 5G and 6G Sensing Networks
Future cellular networks are intended to have the ability to sense the environment by utilizing reflections of transmitted signals.
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
Massive MIMO Channel Measurements and Achievable Rates in a Residential Area
In this paper we present a measurement set-up for massive MIMO channel sounding that shows very good long-term phase stability.
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
