Future 6G networks are envisioned to integrate low Earth orbit satellite mega-constellations to enable seamless global connectivity, particularly in underserved and remote areas. However, the deployment of dense mega-constellations introduces interference among satellites operating over shared frequency bands. This represents a rather new setup for studying spectrum sharing, which exacerbates the limited flexibility of conventional FDD systems based on fixed bands for downlink and uplink transmissions. We address this spectrum-sharing problem and propose dynamic re-assignment of FDD bands for improved interference management in dense deployments, as well as evaluate the performance gain of this approach. To this end, we formulate a joint optimization problem that incorporates dynamic band assignment, user scheduling, and power allocation in both directions. This non-convex mixed integer problem is solved using a combination of equivalence transforms, alternating optimization, and state-of-the-art industrial-grade mixed integer solvers. Numerical results demonstrate that the proposed approach of dynamic FDD band assignment significantly enhances system performance over conventional FDD, achieving up to 30% improvement in throughput in dense deployments.

Semantic communication has gained significant attention with the advances in machine learning. Most semantic communication works focus on either task execution or data reconstruction, with some recent works combining the two. In this work, we propose a semantic communication system for concurrent task execution and data reconstruction for a multi-view scenario, which we formulate as the maximization of mutual information. To investigate the trade-off between the two objectives, we formulate a joint objective as a convex combination of task execution and data reconstruction. We show that under specific assumptions, the SSIM loss can be obtained from the mutual information maximization objective for data reconstruction, which takes human visual perception into account. Furthermore, for constant resource use, we show that by increasing the weight of the reconstruction objective up to a certain point, the task execution performance can be kept nearly constant, while the data reconstruction can be significantly improved.

Mutual-Information Maximizing Finite-Alphabet (MIM-FA) decoders have been introduced as an approach to reduce the decoding complexity of Low Density Parity Check codes while maintaining performance close to that of Floating-Point (FP) Belief-Propagation (BP) decoders. The applicability of these low-complexity decoders to practically relevant systems with higher-order modulated signals transmitted over multicarrier systems like Orthogonal Frequency Division Multiplexing (OFDM) is crucial for their application in 5G and 6G systems. We propose a pragmatic approach for the design of a receiver structure for a MIM-FA decoders that can deal with the varying reliabilities of OFDM subcarriers and being agnostic to the modulation scheme. The simulation results indicate that the proposed scheme in combination with a 3-bit MIM-FA decoder exploits the full frequency diversity and performs close to FP-BP decoder.

With advances in onboard processing, satellites are becoming more capable of solving complex tasks. This added capability increases computational load and energy consumption. We propose strategies to extend the battery lifetime of satellites by tailoring energy consumption and energy harvesting to two key constraints: computational workload and sunlight-eclipse pattern. Satellites operate in periodic sunlight and eclipse cycles: during sunlight, they harvest solar energy to charge their batteries; during eclipse, they rely solely on stored energy. Repeated charging and discharging accelerates battery degradation, progressively reducing capacity over time. Once degradation surpasses a critical threshold, satellite operation becomes unreliable. To model and mitigate battery aging, we adopt a degradation model based on the rainflow counting algorithm. The model accounts for the satellite’s sunlight and eclipse pattern to predict battery degradation over charge and discharge cycles more accurately. We formulate an optimization problem to minimize battery degradation by scheduling computational tasks across sunlight and eclipse phases, and by determining the appropriate amount of energy to harvest for battery charging. The proposed scheme reduces battery degradation by a factor of three, thereby extending the operational life of the satellite.

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

Accurate channel state information (CSI) is essential for spectral efficiency (SE) in 6G multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, yet conventional methods incur substantial pilot overhead. This letter introduces KronFormer, a pilot-to-prediction (P2P) Transformer with factorized spatial-temporal attention aligned to the Kronecker structure of MIMO channel correlations. Unlike unfactorized Transformers that flatten spatial dimensions, KronFormer preserves the 4-D tensor structure throughout processing and decouples spatial and temporal attention, avoiding the quadratic complexity of joint spatial-temporal processing. Simulations on COST 259 channels demonstrate that KronFormer reduces attention complexity by 20 & times; and mean squared error (MSE) by up to 87 & times; over unfactorized Transformers in 8 & times;8 MIMO. KronFormer limits SE degradation to 10-13% from 50 to 200 km/h, a 3 & times; reduction compared to the 34-39% loss of conventional Wiener filters. The architecture enables direct multi-step inference for scalable 6G channel prediction.

This paper proposes a novel physical-layer security framework for multi-UAV Integrated Sensing and Communication (ISAC) networks operating in adversarial environments. To maximize the sum secrecy rate of legitimate ground users (GUs) while satisfying minimum sensing beampattern-gain constraints for target illumination, we introduce a dynamic role allocation mechanism in which each UAV can switch, on a per-time-slot basis, between an ISAC mode—combining coherent communications with radar sensing—and a dedicated artificial noise (AN) jammer mode. The resulting optimization is cast as a highly coupled Mixed-Integer Non-Linear Program (MINLP) that jointly optimizes binary role indicators, transmit beamforming and sensing covariance matrices, and UAV trajectories. We solve this problem with a tailored Alternating Optimization (AO) algorithm that integrates a penalty-based Convex-Concave Procedure (CCP) for the binary role subproblem, Semidefinite Relaxation (SDR) for the beamforming subproblem, and a trust-region Successive Convex Approximation (SCA) for the trajectory subproblem. Numerical results demonstrate that the proposed dynamic-role architecture consistently outperforms both a static dedicated-jammer scheme and a fully optimized all-ISAC embedded-AN benchmark, confirming that its secrecy advantage arises from adaptive spatial-functional specialization rather than from artificial-noise transmission alone. Furthermore, we characterize the fundamental tradeoff between secrecy performance and stringent sensing beampattern-gain constraints, showing that moderate sensing requirements can be accommodated with no secrecy penalty.

This paper highlights key areas in which AI/ML can play a transformative role in 6G, with an emphasis on energy-efficient solutions. Contextualized within Germany’s national 6G research initiative, "6G Access, Network of Networks, Automation and Simplification" (6G-ANNA) emerges as the lighthouse project, providing a holistic vision that sets the direction for numerous specialized research efforts. Within this context, this paper aims to present ideas relevant to both academia and industry by identifying key research directions for AI/ML. We begin by reviewing the latest developments in using AI/ML for the Fifth Generation (5G) New Radio (NR) air interface as discussed in 3rd Generation Partnership Project (3GPP) Releases 18 and 19, and examine how these advancements pave the way for a native and energy-efficient AI/ML air interface in 6G. Key results are presented on AI/ML-driven optimization of radio frequency (RF) frontends, along with a strong focus on the role of AI/ML in diverse signal processing tasks and energy saving mechanisms, which demonstrate the potential of AI/ML in improving spectral efficiency and reducing energy consumption. The discussion further introduces methodologies for testing AI/ML-based signal processing tailored for the 6G physical layer, addressing practical challenges relevant to industry stakeholders and standard development organizations. Finally, we discuss the standardization aspects critical for realizing a future AI-native air interface in 6G, aligning our findings with ongoing and upcoming global standardization activities.

As early as 1949, Weaver defined communication in a very broad sense to include all procedures by which one mind or technical system can influence another, thus establishing the idea of semantic communication. With the recent success of machine learning in expert assistance systems where sensed information is wirelessly provided to a human to assist task execution, the need to design effective and efficient communications has become increasingly apparent. In particular, semantic communication aims to convey the meaning behind the sensed information relevant for Human Decision-Making (HDM). Regarding the interplay between semantic communication and HDM, many questions remain, such as how to model the entire end-to-end sensing-decision-making process, how to design semantic communication for the HDM and which information should be provided for HDM. To address these questions, we propose to integrate semantic communication and HDM into one probabilistic end-to-end sensing-decision framework that bridges communications and psychology. In our interdisciplinary framework, we model the human through a HDM process, allowing us to explore how feature extraction from semantic communication can best support HDM both in theory and in simulations. In this sense, our study reveals the fundamental design trade-off between maximizing the relevant semantic information and matching the cognitive capabilities of the HDM model. Our initial analysis shows how semantic communication can balance the level of detail with human cognitive capabilities while demanding less bandwidth, power, and latency.

The proliferation of low-Earth orbit (LEO) satellite constellations presents unprecedented opportunities for distributed machine learning (ML) applications. However, the inherent challenges of sparse connectivity, heterogeneous communication windows, and non-independent and identically distributed (non-IID) data across satellites hinder the effectiveness of conventional federated learning (FL) frameworks. To address these challenges, we propose Model Contrastive Federated Learning (MCFL), a novel framework tailored for LEO satellite constellations. MCFL introduces a two-stage approach: (1) similarity-based satellite clustering to mitigate intra-cluster data imbalance by grouping satellites with aligned data distributions, and (2) collaborative staleness-aware learning that employs semi-asynchronous model aggregation within clusters to balance convergence speed and model accuracy. The key contributions include a contrastive loss function for robust representation learning under class imbalance, gradient sparsification to minimize communication overhead, and an inter-cluster knowledge-sharing mechanism to prevent cluster-specific model bias. Extensive simulations on the EuroSAT dataset demonstrate that MCFL achieves an improvement of 15% in test accuracy and reduces training time 3× compared to state-of-the-art FL baselines while reducing communication costs by 40%. This work bridges the gap between distributed learning theory and practical satellite constraints, offering a scalable solution for real-time ML applications in dynamic space-terrestrial networks.

Industrial networks often demand hyper-reliable, low-latency communication (HRLLC) to support closed-loop control and automation. However, performance is severely undermined by dynamic fluctuations caused by unknown interferers, making it difficult to uphold reliability requirements. Channel statistics and mobility of interferers lead to significant variations in perceived signal-to-interference-plus-noise ratio (SINR). To cope with such interference dynamics, predictive resource allocation is required, whereby resource selection must be determined before transmission, making SINR prediction essential. This paper introduces Reliability-Aware SINR Prediction (RASP), a probabilistic framework that predicts the SINR distribution under explicit block error rate (BLER) constraints. RASP employs a Mixture Density Network within a Conditional Value-at-Risk (CVaR)-based primal–dual optimization scheme to adapt streaming SINR samples while satisfying reliability constraints. Based on real-world industrial measurements with co-subband interferers, we show that RASP achieves the target BLER of 10−6 at the 95th percentile across three distinct scenarios, outperforming the baselines by a significant margin.

6G networks are expected to integrate low Earth orbit satellites to ensure global connectivity by extending coverage to underserved and remote regions. However, the deployment of dense mega-constellations introduces severe interference among satellites operating over shared frequency bands. This is, in part, due to the limited flexibility of conventional frequency division duplex (FDD) systems, where fixed bands for downlink (DL) and uplink (UL) transmissions are employed. In this work, we propose dynamic re-assignment of FDD bands for improved interference management in dense deployments and evaluate the performance gain of this approach. To this end, we formulate a joint optimization problem that incorporates dynamic band assignment, user scheduling, and power allocation in both directions. This non-convex mixed integer problem is solved using a combination of equivalence transforms, alternating optimization, and state-of-the-art industrial-grade mixed integer solvers. We show numerical results for simple setup to demonstrate the effectiveness of the the proposed approach over conventional FDD, achieving up to 94% improvement in throughput in dense deployments.

In this article, we introduce a Reinforcement Learning (RL)–driven framework for Information Bottleneck (IB)–based distributed Joint Source–Channel Coding (JSCC) that operates reliably when the forward channels are unknown, non-differentiable, stochastic, or entirely black-box. Current state-of-the-art deep variational IB methods require differentiable end-to-end models and full knowledge of the forward channels’ statistics, which renders them ineffective in many practical scenarios involving hidden channel states, human-in-the-loop setups, or proprietary simulators. To overcome these limitations, we reformulate the IB-based JSCC design problem as a sequential decision-making task. This enables the use of deep Multi-Agent Reinforcement Learning (MARL). We first revisit the single-terminal setup and show that the encoder can be treated as a policy in a contextual bandit whose sampled reward is set as the decoder reconstruction term, while the compression penalty is applied directly as an analytic actor regularizer derived from a variational surrogate of the IB objective. This yields a model-free compressor that learns to preserve relevance while respecting the rate constraint, without requiring gradients through the channel. We then extend the framework to the multiterminal setting, where multiple encoders observe noisy versions of a common source and must coordinate implicitly through the environment. Two retrieval strategies are considered: a parallel scheme, which ignores the side-information at the decoder, and a successive scheme, which exploits the side-information at the decoder via conditional priors. For both scenarios, we derive RL-compatible variational lower-bounds on the original IB objectives, enabling Centralized Training with Decentralized Execution (CTDE). By this, we generalize state-of-the-art distributed data-driven IB-based JSCC schemes to arbitrary forward channels while retaining the scalability and sample efficiency. As the main highlight, this work demonstrates that MARL provides a principled foundation for learning distributed compressors in environments where model- or gradient-based approaches are fundamentally inapplicable.

5G-Beyond (5G-B)/6G require higher spectral efficiency (SE) and data throughput (DT), motivating reduced pilot overhead in orthogonal frequency-division multiplexing (OFDM) systems. Traditional methods target temporal prediction and address frequency selectivity via pilot-tone interpolation, leaving time-frequency prediction unexplored. To address this gap, we propose a Transformer-based pilot-to-prediction (P2P) neural network (NN) for joint channel estimation and prediction. Exploiting the Transformer's self-attention, our method captures temporal dynamics and inter-subcarrier correlations to predict the frequency-selective channel across all subcarriers using only past pilot observations, requiring no pilots in the symbols being predicted. Simulation results show that our approach closely matches the accuracy of a genie-aided receiver with perfect channel knowledge and outperforms Wiener filtering and long short-term memory (LSTM) baselines. Consequently, pilot overhead is substantially reduced, enhancing SE by 33.3%. Achievable data rates are thus improved, underscoring the suitability of Transformer-based channel prediction for future wireless standards.

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 88%, which further improves to more than 95%—approaching the 96.4% accuracy achieved with full image classification—when the sampling rate is increased to 7%. 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. The framework was further validated on GaN nanosurfaces, which confirmed that the sampling strategy generalizes effectively across different materials and achieves classification accuracies close to full-image performance.

The sixth generation (6G) of wireless networks is envisioned to achieve far beyond the capabilities of fifth generation (5G), necessitating significant innovations at the physical layer (PHY). These include exploration of several fundamental trade-offs between spectral efficiency, reliability, and energy consumption, and enhancing the performance of key enablers for 6G PHY. This paper synthesizes key insights from the 6G-ANNA research initiative on emerging PHY technologies for 6G to provide a holistic exploration of the ongoing trends in the 6G research. The investigations span novel waveform and channel coding techniques for improved energy efficiency, the “Gearbox PHY” concept for adaptive transceiver operations, and optimized radio transceiver designs that balance complexity and power consumption. The study also examines advanced multiple access schemes and cell-free massive multiple-input multiple-output (MIMO) architectures to enhance spectral efficiency and uniform coverage. Integrated artificial intelligence (AI) solutions at the PHY layer and insights to security and trustworthiness challenges in 6G networks are also provided. The findings offer insights into the fundamental trade-offs and provide several key PHY innovations that address sustainability, capacity, and resiliency challenges of future 6G wireless systems.

While learning-based channel predictors show promise for reducing pilot overhead in next-generation orthogonal frequency-division multiplexing (OFDM) systems, the lack of principled performance benchmarks prevents rigorous assessment of their proximity to theoretical limits. We derive a closed-form linear minimum mean-square error (LMMSE) benchmark for strictly causal pilot-to-prediction (P2P) in frequency-selective OFDM systems. For Gaussian wide-sense stationary uncorrelated scattering (WSSUS) channels, this benchmark equals the Bayesian Cramér–Rao bound (BCRB), decomposing irreducible error into temporal prediction and pilot-induced estimation components with closed-form eigenvalue expressions. Evaluating five neural architectures, we find that Transformer and state-space model (SSM) closely track the LMMSE benchmark on COST 259 channels, while long short-term memory (LSTM) and Autoformer exhibit larger gaps. Crossformer operates below the benchmark at most tested speeds, consistent with nonlinear exploitation of non-Gaussian structure in finite sum-of-sinusoids (SoS) channel models, though it exhibits elevated gaps at low Doppler; a reduced-capacity ablation indicates a capacity–architecture interaction. On 3rd Generation Partnership Project (3GPP) TR 38.901 Tapped Delay Line-A (TDL-A) channels, both Transformer and Crossformer exhibit strictly positive gaps across all tested speeds. This demonstrates that while nonlinear methods can surpass the linear benchmark, Transformer and SSM have closely approached it, implying that further gains depend primarily on pilot density, signal-to-noise ratio (SNR), or prediction horizons.

This paper addresses the challenge of pilot overhead reduction in Multi-Input Multi-Output (MIMO) systems employing Orthogonal Frequency Division Multiplexing (OFDM), a promising candidate waveform for future 6G networks. As networks scale with increasing antennas and subcarriers, the pilot overhead also rises, reducing spectral efficiency. Moreover, the stringent reliability requirements of 6G demand a robust channel estimation scheme with minimal pilot overhead. To meet this need, we introduce a Semi-Blind Channel Estimation framework with Adaptive Pilot Design (SBCE-APD) that aims to achieve a target estimation accuracy with minimal pilot overhead. Traditional techniques primarily aim to improve estimation accuracy for a fixed pilot budget. In contrast, the proposed SBCE-APD operates as a closed-loop framework that minimizes pilot overhead subject to a target accuracy constraint. Its main novelty lies in the system-level integration of adaptive pilot-count selection, pilot placement, and semi-blind channel estimation. The method combines two complementary estimation branches: a pilot-based estimator and a non-pilot-based estimator. Their integration forms a semi-blind estimation structure, reducing dependence on pilot symbols. Furthermore, the pilot pattern is adaptively adjusted over the air, optimizing the number of pilots under varying network conditions to maintain the desired accuracy efficiently. Simulation results demonstrate substantial gains in pilot efficiency while preserving reliable channel estimation, establishing SBCE-APD as an effective and scalable solution for 6G MIMO-OFDM networks.

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.

Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations. However, as we demonstrated in [1], cooperative multi-tasking can be either constructive or destructive, depending on the semantic relationships among tasks. To ensure constructive cooperation, we propose a semantic-aware task clustering method for CMT-SemCom. We have formulated a sequential multi-stage optimization problem in which semantically aligned tasks are clustered once after a short initial training phase, and then end-to-end (E2E) joint training is conducted exclusively within the discovered groups. Specifically, the problem decomposes into two stages: (i) a semantic clustering problem leveraging hierarchical density-based spatial clustering, and (ii) an intra-cluster E2E CMT-SemCom learning problem. Simulation results demonstrate that the proposed framework effectively mitigates destructive cooperation and negative transfer, yielding accuracy gains compared to unclustered multi-tasking and individual training baselines.