Accuracy of Joint Time-Based and Carrier-Phase Positioning in 5G Networks under Correlated Measurement Errors

High-accuracy positioning is critical for emerging applications such as autonomous driving, industrial automation, augmented reality, and smart cities. 3GPP Release 18 introduced carrier-phase (CP) positioning for 5G that offers superior accuracy compared to conventional time-based methods such as time of arrival (ToA). However, CP-based positioning requires resolving the integer phase ambiguity, which refers to the unknown number of full-wavelength cycles completed during signal propagation. Joint processing of ToA and CP can mitigate this integer ambiguity by narrowing down the search space of possible integers, particularly for short wavelengths. This paper investigates the performance of a positioning method that integrates ToA and CP measurements. As a main contribution, the analysis explicitly accounts for the error correlation between ToA and CP measurements. Furthermore, the study analyzes the impact of key 5G system parameters on positioning accuracy using this correlation-aware joint method in both factory and urban environments, where many 5G positioning applications are expected to emerge. The results highlight that exploiting this correlation can further improve positioning performance by approximately 7 percent. Moreover, the findings of this study provide insight into how 5G system parameters can be tuned to achieve centimeter-level accuracy under favorable conditions.

Optimized Sampling of Angle-Resolved Scatterometry Data Using End-to-End Compressed Learning Model for Nanograss Deficiency Detection

Reliable inspection of nanosurfaces is essential to ensure the quality of nanostructure manufacturing. Angle-resolved scatterometry provides a non-invasive inspection method that can be used in-line but often suffers from long acquisition times due to dense angular sampling. This paper addresses the data acquisition challenge by proposing an end-to-end compressed learning framework for 5-level vacancy deficiency detection in zinc oxide nanograss using ARS images. The proposed framework integrates a learnable latitude-based sampling layer with a convolutional neural network, allowing sampling and classification to be jointly optimized during training. The sampling layer exploits the physical structure of ARS patterns and learns informative latitudinal regions, which reduces the sampling search space and improves convergence. Evaluation results show that the proposed approach achieves high and stable deficiency-level classification performance under different noise conditions. Using full ARS images, the model achieves 94.2% accuracy for five-level deficiency classification and 98.6% accuracy for separating deficient from non-deficient nanosurfaces. The proposed sampling model matches full-image performance while using up to 90% fewer angular sampling points. Even when sampling points are reduced by 99.7%, the classification accuracy decreases by less than 10 percentage points. To further improve training with limited data, we also studied a GAN-based augmentation approach and used GAN-generated data for model pretraining. Augmented data resulted in fast convergence within only a few fine-tuning epochs.

Compressed Learning for Nanosurface Deficiency Recognition Using Angle-resolved Scatterometry Data

Nanoscale manufacturing requires high-precision surface inspection to guarantee the quality of the produced nanostructures. For production environments, angle-resolved scatterometry offers a non- invasive and in-line compatible alternative to traditional surface inspection methods, such as scanning electron microscopy. However, angle-resolved scatterometry currently suffers from long data acquisition time. Our study addresses the issue of slow data acquisition by proposing a compressed learning framework for the accurate recognition of nanosurface deficiencies using angle-resolved scatterometry data. The framework uses the particle swarm optimization algorithm with a sampling scheme customized for scattering patterns. This combination allows the identification of optimal sampling points in scatterometry data that maximize the detection accuracy of five different levels of deficiency in ZnO nanosurfaces. The proposed method significantly reduces the amount of sampled data while maintaining a high accuracy in deficiency detection, even in noisy environments. Notably, by sampling only 1% of the data, the method achieves an accuracy of over 86%, which further improves to 94% when the sampling rate is increased to 6%. These results demonstrate a favorable balance between data reduction and classification performance. The obtained results also show that the compressed learning framework effectively identifies critical sampling areas.

Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources

In this paper, we explore a multi-task semantic communication (SemCom) system for distributed sources, extending the existing focus on collaborative single-task execution. We build on the cooperative multi-task processing introduced in [1], which divides the encoder into a common unit (CU) and multiple specific units (SUs). While earlier studies in multi-task SemCom focused on full observation settings, our research explores a more realistic case where only distributed partial observations are available, such as in a production line monitored by multiple sensing nodes. To address this, we propose an SemCom system that supports multi-task processing through cooperation on the transmitter side via split structure and collaboration on the receiver side. We have used an information-theoretic perspective with variational approximations for our end-to-end data-driven approach. Simulation results demonstrate that the proposed cooperative and collaborative multi-task (CCMT) SemCom system significantly improves task execution accuracy, particularly in complex datasets, if the noise introduced from the communication channel is not limiting the task performance too much. Our findings contribute to a more general SemCom framework capable of handling distributed sources and multiple tasks simultaneously, advancing the applicability of SemCom systems in real-world scenarios.

Throughput Requirements for RAN Functional Splits in 3D-Networks

The rapid growth of non-terrestrial communication necessitates its integration with existing terrestrial networks, as highlighted in 3GPP Releases 16 and 17. This paper analyses the concept of functional splits in 3D-Networks. To manage this complex structure effectively, the adoption of a Radio Access Network (RAN) architecture with Functional Split (FS) offers advantages in flexibility, scalability, and cost-efficiency. RAN achieves this by disaggregating functionalities into three separate units. Analogous to the terrestrial network approach, 3GPP is extending this concept to non-terrestrial platforms as well. This work presents a general analysis of the requested Fronthaul (FH) data rate on feeder link between a non-terrestrial platform and the ground-station. Each split option is a trade-of between FH data rate and the respected complexity. Since flying nodes face more limitations regarding power consumption and complexity on board in comparison to terrestrial ones, we are investigating the split options between lower and higher physical layer.

Instantaneous Bandwidth Estimation from Level-Crossing Samples via LSTM-based Encoder-Decoder Architecture

This paper presents an approach for instantaneous bandwidth estimation from level-crossing samples using a long short-term memory (LSTM) encoder-decoder architecture. Level-crossing sampling is a nonuniform sampling technique that is particularly useful for energy-efficient acquisition of signals with sparse spectra. Especially in combination with fully analog wireless sensor nodes, level-crossing sampling offers a viable alternative to traditional sampling methods. However, due to the nonuniform distribution of samples, reconstructing the original signal is a challenging task. One promising reconstruction approach is time-warping, where the local signal spectrum is taken into account. However, this requires an accurate estimate of the instantaneous bandwidth of the signal. In this paper, we show that applying neural networks (NNs) to the problem of estimating instantaneous bandwidth from level-crossing samples can improve the overall reconstruction accuracy. We conduct a comprehensive numerical analysis of the proposed approach and compare it to an intensity-based bandwidth estimation method from literature.

Semantic Communication for Cooperative Multi-Task Processing over Wireless Networks

In this paper, we have expanded the current status of semantic communication limited to processing one task to a more general system that can handle multiple tasks concurrently. In pursuit of this, we first introduced our definition of the "semantic source", enabling the interpretation of multiple semantics based on a single observation. A semantic encoder design is then introduced, featuring the division of the encoder into a common unit and multiple specific units enabling cooperative multi-task processing. Simulation results demonstrate the effectiveness of the proposed semantic source and the system design. Our approach employs information maximization (infomax) and end-to-end design principles.

Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement Learning

Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications. Space-Division Multiple Access (SDMA) precoding is a method that mitigates interference among satellite beams, boosting spectral efficiency. While optimal SDMA precoding solutions have been proposed for ideal channel knowledge in various scenarios, addressing robust precoding with imperfect channel information has primarily been limited to simplified models. However, these models might not capture the complexity of LEO satellite applications. We use the Soft Actor-Critic (SAC) deep Reinforcement Learning (RL) method to learn robust precoding strategies without the need for explicit insights into the system conditions and imperfections. Our results show flexibility to adapt to arbitrary system configurations while performing strongly in terms of achievable rate and robustness to disruptive influences compared to analytical benchmark precoders.

Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient

Motivated by the recent success of Machine Learning tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning, i.e., semantics, of a message instead of its exact version, allowing for information rate savings. In this work, we apply the Stochastic Policy Gradient (SPG) to design a semantic communication system by reinforcement learning, not requiring a known or differentiable channel model - a crucial step towards deployment in practice. Further, we motivate the use of SPG for both classic and semantic communication from the maximization of the mutual information between received and target variables. Numerical results show that our approach achieves comparable performance to a model-aware approach based on the reparametrization trick, albeit with a decreased convergence rate.

A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation

With increasing complexity of modern communication systems, machine learning algorithms have become a focal point of research. However, performance demands have tightened in parallel to complexity. For some of the key applications targeted by future wireless, such as the medical field, strict and reliable performance guarantees are essential, but vanilla machine learning methods have been shown to struggle with these types of requirements. Therefore, the question is raised whether these methods can be extended to better deal with the demands imposed by such applications. In this paper, we look at a combinatorial resource allocation challenge with rare, significant events which must be handled properly. We propose to treat this as a multi-task learning problem, select two methods from this domain, Elastic Weight Consolidation and Gradient Episodic Memory, and integrate them into a vanilla actor-critic scheduler. We compare their performance in dealing with Black Swan Events with the state-of-the-art of augmenting the training data distribution and report that the multi-task approach proves highly effective.