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View article: 6G AI-Driven Air Interface — Hexa-X-II View
6G AI-Driven Air Interface — Hexa-X-II View Open
View article: Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
Demonstrating Interoperable Channel State Feedback Compression with Machine Learning Open
Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feed…
View article: Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers
Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Open
Fifth-generation (5G) systems utilize orthogonal demodulation reference signals (DMRS) to enable channel estimation at the receiver. These orthogonal DMRS-also referred to as pilots-are effective in avoiding pilot contamination and interfe…
View article: Machine Learning Physical-Layer Receivers for DFT-s-OFDM
Machine Learning Physical-Layer Receivers for DFT-s-OFDM Open
View article: Adapting to Reality: Over-the-Air Validation of AI - Based Receivers Trained with Simulated Channels
Adapting to Reality: Over-the-Air Validation of AI - Based Receivers Trained with Simulated Channels Open
Recent research shows that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency. However, most AI-based receiver studies rely on simulated radio channel data for both tr…
View article: Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios
Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios Open
We propose a novel method for interpreting neural networks, focusing on convolutional neural network-based receiver model. The method identifies which unit or units of the model contain most (or least) information about the channel paramet…
View article: Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels
Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels Open
Recent research shows that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency. However, most AI-based receiver studies rely on simulated radio channel data for both tr…
View article: Deep Learning-Based Pilotless Spatial Multiplexing
Deep Learning-Based Pilotless Spatial Multiplexing Open
This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO) communication systems. Especially, it is shown that by training the transmitter and receiver…
View article: Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage
Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage Open
In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifica…
View article: The Hexa-X Project Vision on Artificial Intelligence and Machine Learning-Driven Communication and Computation Co-Design for 6G
The Hexa-X Project Vision on Artificial Intelligence and Machine Learning-Driven Communication and Computation Co-Design for 6G Open
This paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduc…
View article: DeepTx: Deep Learning Beamforming With Channel Prediction
DeepTx: Deep Learning Beamforming With Channel Prediction Open
Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it …
View article: Pervasive Artificial Intelligence in Next Generation Wireless: The Hexa-X Project Perspective
Pervasive Artificial Intelligence in Next Generation Wireless: The Hexa-X Project Perspective Open
The European 6G flagship project Hexa-X has the objective to conduct exploratory research on the next generation of mobile networks with the intention to connect human, physical and digital worlds with a fabric of technology enablers. With…
View article: Dual-mode Ultra Reliable Low Latency Communications for Industrial Wireless Control
Dual-mode Ultra Reliable Low Latency Communications for Industrial Wireless Control Open
Funding Information: ACKNOWLEDGMENT This work was supported in part by the Finnish public funding agency for research, Business Finland under the project “5G VIIMA”, grant number 6430/31/2018. 5G VIIMA is part of 5G Test Network Finland (5…
View article: Communications Survival Strategies for Industrial Wireless Control
Communications Survival Strategies for Industrial Wireless Control Open
Publisher Copyright: © 1986-2012 IEEE.
View article: DeepTx: Deep Learning Beamforming with Channel Prediction
DeepTx: Deep Learning Beamforming with Channel Prediction Open
Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it …
View article: Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier
Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier Open
Machine learning (ML) has shown great promise in optimizing various aspects of the physical layer processing in wireless communication systems. In this paper, we use ML to learn jointly the transmit waveform and the frequency-domain receiv…
View article: HybridDeepRx: Deep Learning Receiver for High-EVM Signals
HybridDeepRx: Deep Learning Receiver for High-EVM Signals Open
In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural…
View article: Optimized Survival Mode to Guarantee QoS for Time-critical Services
Optimized Survival Mode to Guarantee QoS for Time-critical Services Open
Funding Information: This work was supported in part by Business Finland funding for the project 5G VIIMA. Publisher Copyright: © 2021 IEEE.
View article: DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations
DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations Open
Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of encouraging results, most works have not considered spatial multiplexing in the…
View article: DeepRx: Fully Convolutional Deep Learning Receiver
DeepRx: Fully Convolutional Deep Learning Receiver Open
Deep learning has solved many problems that are out of reach of heuristic algorithms. It has also been successfully applied in wireless communications, even though the current radio systems are well-understood and optimal algorithms exist …
View article: Cascaded Spline-Based Models for Complex Nonlinear Systems: Methods and Applications
Cascaded Spline-Based Models for Complex Nonlinear Systems: Methods and Applications Open
In this paper, we present a class of cascaded nonlinear models for complex-valued system identification, aimed at baseband modeling of nonlinear radio systems. The proposed models consist of serially connected elementary linear and nonline…
View article: Full-Duplexing With SDR Devices: Algorithms, FPGA Implementation, and Real-Time Results
Full-Duplexing With SDR Devices: Algorithms, FPGA Implementation, and Real-Time Results Open
sponsorship: Manuscript received April 3, 2020; revised August 31, 2020; accepted November 9, 2020. Date of publication December 3, 2020; date of current version April 9, 2021. This work was supported in part by the European Union's Horizo…
View article: Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity Digital Predistortion
Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity Digital Predistortion Open
In this paper, new digital predistortion (DPD) solutions for power amplifier (PA) linearization are proposed, with particular emphasis on reduced processing complexity in future 5G and beyond wideband radio systems. The first proposed meth…
View article: DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative\n Transformations
DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative\n Transformations Open
Recently, deep learning has been proposed as a potential technique for\nimproving the physical layer performance of radio receivers. Despite the large\namount of encouraging results, most works have not considered spatial\nmultiplexing in …
View article: Downlink Coverage and Rate Analysis of Low Earth Orbit Satellite Constellations Using Stochastic Geometry
Downlink Coverage and Rate Analysis of Low Earth Orbit Satellite Constellations Using Stochastic Geometry Open
As low Earth orbit (LEO) satellite communication systems are gaining increasing popularity, new theoretical methodologies are required to investigate such networks' performance at large. This is because deterministic and location-based mod…
View article: Reduced-Complexity Digital Predistortion: Adaptive Spline-Based Hammerstein Approach
Reduced-Complexity Digital Predistortion: Adaptive Spline-Based Hammerstein Approach Open
In this paper, a novel digital predistorter concept for power amplifier (PA) linearization is proposed, with particular emphasis on reduced processing complexity in future 5G and beyond wideband radio systems. The proposed method builds on…
View article: Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity\n Digital Predistortion
Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity\n Digital Predistortion Open
In this paper, new digital predistortion (DPD) solutions for power amplifier\n(PA) linearization are proposed, with particular emphasis on reduced processing\ncomplexity in future 5G and beyond wideband radio systems. The first proposed\nm…
View article: Military Full-Duplex Radio Shield for Protection Against Adversary Receivers
Military Full-Duplex Radio Shield for Protection Against Adversary Receivers Open
This paper provides experimental results regarding an emerging physical-layer concept within the field of military communications, viz. the full-duplex 'radio shield', by building on the recently discovered full-duplex technology that allo…
View article: Digital Cancellation of Passive Intermodulation in FDD Transceivers
Digital Cancellation of Passive Intermodulation in FDD Transceivers Open
Modern radio systems and transceivers utilize carrier aggregation (CA) to meet the demands for higher and higher data rates. However, the adoption of CA in the existing Long Term Evolution (LTE)-Advanced and emerging 5G New Radio (NR) mobi…
View article: Nonlinear self-interference cancellation in MIMO full-duplex transceivers under crosstalk
Nonlinear self-interference cancellation in MIMO full-duplex transceivers under crosstalk Open
This paper presents a novel digital self-interference canceller for an inband multiple-input-multiple-output (MIMO) full-duplex radio. The signal model utilized by the canceller is capable of modeling the in-phase quadrature (IQ) imbalance…