arXiv subjects

Dani Korpi

Publications and source records attributed to Dani Korpi.

At least 19 recordsLinked to original sources

arXiv2026

A Novel Space-Time Coding Architecture for Rydberg Atomic Quantum Receiver-Based Systems

Rydberg atomic quantum receivers (RAQRs) offer high sensitivity and wide tunability, but their magnitude-based readout yields a nonlinear model incompatible with conventional complex-valued multi-input multi-output (MIMO) processing. We propose a low-complexity space-time coding framework for point-to-point RAQR-assisted MIMO links. Data symbols are encoded using real orthogonal designs, while strong-reference heterodyne reception yields an equivalent real-valued linear model. The preserved orthogonality enables matched filter symbol-wise detection without matrix inversion or vector search. An analytical bit error probability expression is derived, proving the proposed scheme achieves the full transmit-receive diversity. Simulations validate the analysis and demonstrate improved performance over spatial multiplexing benchmarks.

cs.ET↗

arXiv2026

Input-Correlated Supervision Noise Limits the Benefits of OTA Training for Learned Receivers

While learned wireless receivers are typically studied using synthetic data, the impact of over-the-air (OTA) measurements for training remains unclear. We conducted a 5.88 GHz measurement campaign with a 5G/6G-like orthogonal frequency-division multiplexing (OFDM) system across diverse environments and mobility conditions, and trained a neural channel estimator and a capacity-matched end-to-end neural receiver using mixtures of measured and synthetic data. Increasing the OTA fraction revealed a fundamental asymmetry: measured data consistently improved the end-to-end receiver, whereas the channel estimator peaked at an intermediate fraction and degraded with fully measured training. We showed that this difference arises from the supervision target: OTA channel labels are derived from noisy received signals and therefore contain supervision errors correlated with the receiver input, whereas decoded bits validated by a cyclic redundancy check (CRC) provide effectively error-free supervision. A controlled denoising experiment confirmed that this correlation, rather than limited data diversity, caused the degradation. These results provide practical guidance for training learned receivers with OTA data: end-to-end receivers benefit from fully measured training, whereas channel estimators benefit from moderate OTA fractions but require improved label quality, e.g. via denoising, to unlock further gains.

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arXiv2026

EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver

While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization. This paper presents EqDeepRx, a practical deep-learning-aided multiple-input multiple-output (MIMO) receiver, which is built by augmenting linear receiver processing with carefully engineered ML blocks. At the core of the receiver model is a shared-weight DetectorNN that operates independently on each spatial stream or layer, enabling near-linear complexity scaling with respect to multiplexing order. To ensure better explainability and generalization, EqDeepRx retains conventional channel estimation and augments it with a lightweight DenoiseNN that learns frequency-domain smoothing. To reduce the dimensionality of the DetectorNN inputs, the receiver utilizes two linear equalizers in parallel: a linear minimum mean-square error (LMMSE) equalizer with interference-plus-noise covariance estimation and a regularized zero-forcing (RZF) equalizer. The parallel equalized streams are jointly consumed by the DetectorNN, after which a compact DemapperNN produces bit log-likelihood ratios for channel decoding. 5G/6G-compliant end-to-end simulations across multiple channel scenarios, pilot patterns, and inter-cell interference conditions show improved error rate and spectral efficiency over a conventional baseline, while maintaining low-complexity inference and support for different MIMO configurations without retraining.

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arXiv2025

Knowledge Graph-Based approach for Sustainable 6G End-to-End System Design

Previous generations of cellular communication, such as 5G, have been designed with the objective of improving key performance indicators (KPIs) such as throughput, latency, etc. However, to meet the evolving KPI demands and the ambitious sustainability targets for the Information and Communication Technology (ICT) industry, 6G will need to be designed differently. 6G will need to consider both the performance and sustainability targets for the various use cases it will serve. In addition, 6G will have various candidate technological enablers, making the design space of the system even more complex. Furthermore, due to the subjective nature of sustainability indicators, especially social sustainability, the literature still lacks clear methods to link them with technical enablers and 6G system design. Hence, in this article a novel method for 6G end-to-end (E2E) system design based on Knowledge graphs (KG) has been introduced. It considers as its input: the use case KPIs, use case sustainability requirements expressed as Key Values (KV) and KV Indicators (KVIs), the ability of the technological enablers to satisfy these KPIs and KVIs, the 6G system design principles defined in Hexa-X-II project, the maturity of a technological enabler and the dependencies between the various enablers. The KG method also introduces a novel approach for determining the key values addressed by a technological enabler. The effectiveness of the KG method was demonstrated by its application in designing the 6G E2E system for the cooperating mobile robot use case defined in the Hexa-X-II project, where 82 enablers were selected. Lastly, results from proof-of-concept demonstrations for a subset of the selected enablers have also been provided, which reinforce the efficacy of the KG method for designing a sustainable 6G system.

cs.NI↗

arXiv2025

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning

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 feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, where the user equipment (UE) and base station have no access to each others' ML models. In this paper, we present a novel approach for training interoperable compression and decompression ML models in a confidential manner, and demonstrate the accuracy of the ensuing models using prototype UEs and base stations. The performance of the ML-based channel feedback is measured both in terms of the accuracy of the reconstructed channel information and achieved downlink throughput gains when using the channel information for beamforming. The reported measurement results demonstrate that it is possible to develop an accurate ML-based channel feedback link without having to share ML models between device and network vendors. These results pave the way for a practical implementation of ML-based channel feedback in commercial 6G networks.

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arXiv2025

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers

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 interference from both the user's own data and that of others. However, this approach incurs a significant overhead, as a substantial portion of the time-frequency resources must be reserved for pilot transmission. Moreover, the overhead increases with the number of users and transmission layers. To address these limitations in the context of emerging sixth-generation (6G) systems and to support data transmission across the entire time-frequency grid, the superposition of data and DMRS symbols has been explored as an alternative DMRS transmission strategy. In this study, we propose an enhanced version of DeepRx, a deep convolutional neural network (CNN)-based receiver, capable of estimating the channel from received superimposed (SI) DMRS symbols and reliably detecting the transmitted data. We also design a conventional receiver for comparison, which estimates the channel from SI DMRS using classical signal processing techniques. Extensive evaluations in both uplink single-user and multi-user scenarios demonstrate that DeepRx consistently outperforms the conventional receivers in terms of performance.

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arXiv2025

Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers

Neural network-based radio receivers are expected to play a key role in future wireless systems, making reliable Out-Of-Distribution (OOD) detection essential. We propose a post-hoc, layerwise OOD framework based on channelwise feature aggregation that avoids classwise statistics--critical for multi-label soft-bit outputs with astronomically many classes. Receiver activations exhibit no discrete clusters but a smooth Signal-to-Noise-Ratio (SNR)-aligned manifold, consistent with classical receiver behavior and motivating manifold-aware OOD detection. We evaluate multiple OOD feature types, distance metrics, and methods across layers. Gaussian Mahalanobis with mean activations is the strongest single detector, earlier layers outperform later, and SNR/classifier fusions offer small, inconsistent AUROC gains. High-delay OOD is detected reliably, while high-speed remains challenging.

cs.LG↗

arXiv2025

Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels

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 training and validation, raising concerns about real-world generalization, which is vital for ensuring reliable field performance. In this study, we train DeepRx, a convolutional neural network (CNN)-based OFDM receiver, under various simulated channel scenarios and validate its performance over-the-air (OTA) using software-defined radio (SDR) technology in a small cell-type setup. To enhance receiver training, we investigate a randomized 3GPP TS38.901 channel model to diversify the training data, thereby improving performance over conventional receivers and matching or exceeding the performance of receivers trained on narrowly targeted channel models. These results demonstrate DeepRx's robust generalization capability and suggest that narrowly scoped, individual TS38.901 models can compromise both training and validation, underscoring the need for tailored channel models, careful training strategies, and OTA testing in learned receiver development.

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arXiv2024

Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios

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 parameter(s) of the interest, providing insights at both global and local levels -- with global explanations aggregating local ones. Experiments on link-level simulations demonstrate the method's effectiveness in identifying units that contribute most (and least) to signal-to-noise ratio processing. Although we focus on a radio receiver model, the method generalizes to other neural network architectures and applications, offering robust estimation even in high-dimensional settings.

cs.LG↗

arXiv2023

Deep Learning-Based Pilotless Spatial Multiplexing

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 jointly, the transmitter can learn such constellation shapes for the spatial streams which facilitate completely blind separation and detection by the simultaneously learned receiver. To the best of our knowledge, this is the first time ML-based spatial multiplexing without channel estimation pilots is demonstrated. The results show that the learned pilotless scheme can outperform a conventional pilot-based system by as much as 15-20% in terms of spectral efficiency, depending on the modulation order and signal-to-noise ratio.

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arXiv2022

DeepTx: Deep Learning Beamforming with Channel Prediction

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 to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.

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arXiv2022

Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier

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 receiver. 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. The system also learns a constellation shape that facilitates pilotless detection by the simultaneously learned receiver. The simulation results show that such an end-to-end optimized system can communicate data more accurately and with less out-of-band emissions than conventional systems, thereby demonstrating the potential of ML in optimizing the air interface. To the best of our knowledge, there are no prior works considering the power amplifier induced emissions in an end-to-end learned system. These findings pave the way towards an ML-native air interface, which could be one of the building blocks of 6G.

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arXiv2021

HybridDeepRx: Deep Learning Receiver for High-EVM Signals

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 network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.

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arXiv2021

DeepRx: Fully Convolutional Deep Learning Receiver

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 for many tasks. While some gains have been obtained by learning individual parts of a receiver, a better approach is to jointly learn the whole receiver. This, however, often results in a challenging nonlinear problem, for which the optimal solution is infeasible to implement. To this end, we propose a deep fully convolutional neural network, DeepRx, which executes the whole receiver pipeline from frequency domain signal stream to uncoded bits in a 5G-compliant fashion. We facilitate accurate channel estimation by constructing the input of the convolutional neural network in a very specific manner using both the data and pilot symbols. Also, DeepRx outputs soft bits that are compatible with the channel coding used in 5G systems. Using 3GPP-defined channel models, we demonstrate that DeepRx outperforms traditional methods. We also show that the high performance can likely be attributed to DeepRx learning to utilize the known constellation points of the unknown data symbols, together with the local symbol distribution, for improved detection accuracy.

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arXiv2020

DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations

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 context of multiple-input and multiple-output (MIMO) receivers. In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together. We propose two novel alternatives for the transformation layer: a maximal ratio combining-based transformation, or a fully learned transformation. The former relies more on expert knowledge, while the latter utilizes learned multiplicative layers. Both proposed transformation layers are shown to clearly outperform the conventional baseline receiver, especially with sparse pilot configurations. To the best of our knowledge, these are some of the first results showing such high performance for a fully learned MIMO receiver.

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arXiv2020

Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity Digital Predistortion

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 method, referred to as the spline-based Hammerstein (SPH) approach, builds on complex spline-interpolated lookup table (LUT) followed by a linear finite impulse response (FIR) filter. The second proposed method, the spline-based memory polynomial (SMP) approach, contains multiple parallel complex spline-interpolated LUTs together with an input delay line such that more versatile memory modeling can be achieved. For both structures, gradient-based learning algorithms are derived to efficiently estimate the LUT control points and other related DPD parameters. Large set of experimental results are provided, with specific focus on 5G New Radio (NR) systems, showing successful linearization of multiple sub-6 GHz PA samples as well as a 28 GHz active antenna array, incorporating channel bandwidths up to 200 MHz. Explicit performance-complexity comparisons are also reported between the SPH and SMP DPD systems and the widely-applied ordinary memory-polynomial (MP) DPD solution. The results show that the linearization capabilities of the proposed methods are very close to that of the ordinary MP DPD, particularly with the proposed SMP approach, while having substantially lower processing complexity.

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arXiv2020

Downlink Coverage and Rate Analysis of Low Earth Orbit Satellite Constellations Using Stochastic Geometry

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 models that have previously been applied to analyze satellite systems are typically restricted to support simulations only. In this paper, we derive analytical expressions for the downlink coverage probability and average data rate of generic LEO networks, regardless of the actual satellites' locality and their service area geometry. Our solution stems from stochastic geometry, which abstracts the generic networks into uniform binomial point processes. Applying the proposed model, we then study the performance of the networks as a function of key constellation design parameters. Finally, to fit the theoretical modeling more precisely to real deterministic constellations, we introduce the effective number of satellites as a parameter to compensate for the practical uneven distribution of satellites on different latitudes. In addition to deriving exact network performance metrics, the study reveals several guidelines for selecting the design parameters for future massive LEO constellations, e.g., the number of frequency channels and altitude.

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arXiv2018

Digital Cancellation of Passive Intermodulation in FDD Transceivers

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) mobile networks, in case of frequency division duplexing (FDD), may incur self-interference challenges with certain band combinations. More specifically, the nonlinear distortion products of the transmit signals or component carriers (CCs), stemming from the passive radio frequency (RF) front-end components of the transceiver, can appear in one or more of the configured receiver bands, potentially leading to the receiver desensitization. In this paper, we present advanced baseband equivalent signal models for such passive intermodulation (PIM) distortion viewed from the RX point of view, considering also potential memory effects in the PIM generation. Then, building on these signal models, a digital self-interference cancellation technique operating in the transceiver digital front-end is presented. The performance of the proposed solution is evaluated with real-life RF measurements for LTE-Advanced type user equipment (UE) with dual CC inter-band CA, demonstrating excellent suppression properties. The findings in this work indicate that digital cancellation is a feasible approach for improving the receiver sensitivity of mobile devices that may be prone to RF front-end induced PIM challenges.

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