arXiv subjects

Anke Schmeink

Publications and source records attributed to Anke Schmeink.

At least 19 recordsLinked to original sources

arXiv2026

AoI-Oriented Globally Optimal Joint Source and Update Scheduling in Fluid Antenna Systems

As a promising technique, fluid antennas enable adaptive radio environment management and interference mitigation through reconfigurable fluid port selections. In this work, to explore the benefits of fluid antennas for data freshness enhancement, we consider a fluid-antenna assisted status update system supported by multiple source nodes monitoring the same environmental status. We assume a subset of the source nodes are activated to report status updates with different periods. Each user is assigned to one source node and equipped with an fluid antenna to adaptively enhance the channel gain to assigned source node while mitigating interference from other activated source nodes. With maximal signal-to-interference-noise ratio (SINR)-based fluid port selection at all users, we formulate an optimization problem to minimize the maximum average age of information (AoI), where source node activation and assignment, i.e., source scheduling, is jointly optimized with the update periods of all activated source nodes. To optimally solve the resulting mixed-integer nonlinear problem, we first consider given source scheduling decision and apply performance achievability analysis. Aided by fixpoint theory, we propose an efficient bisection algorithm for optimal update scheduling. Based on these characterizations, we further propose a filtering algorithm which efficiently eliminates all non-optimal source scheduling decisions. The globally optimal joint solution is then obtained by combining the resulting optimal source scheduling with its corresponding optimal update scheduling. Numerical results validate the optimality of proposed solution and demonstrate the effectiveness of fluid antennas in enhancing data freshness.

eess.SY↗

arXiv2026

Minimizing Quantized Semantic Age of Information (QSAoI) in Foundation Model-Based Semantic Communications

The emerging techniques of semantic communications and edge computing in 6G networks necessitate a paradigm shift toward co-designed semantic-aware and adaptive resource allocation for short-packet transmissions. However, there is a fundamental gap between the semantic layer and the physical layer under low-latency finite blocklength (FBL) effects. To bridge this gap, we introduce the Quantized Semantic Age of Information (QSAoI), a novel metric that rigorously captures the trade-offs among freshness and semantic efficiency of high-level features in real-time communication in the FBL regime. Guided by this metric, we propose a novel foundation model-based efficient co-designed framework to minimize the expected QSAoI over wireless fading channels in latency-constrained semantic communication. Specifically, we formulate a non-linear joint optimization problem to dynamically optimize the block-wise mixed-precision quantization (MPQ) strategy and the physical blocklength. To efficiently resolve this complex problem, we develop a high-efficiency low-complexity algorithm based on fixpoint inspection and bisection search. Extensive simulations validate that our proposed algorithm dynamically adapts the semantic quantization precision to varying channel conditions, effectively minimizing the expected QSAoI compared to baselines.

eess.SP↗

arXiv2026

Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey

Resource constraints increasingly determine what can be trained, fine-tuned, and deployed in large language models (LLMs), yet efficiency is often studied through isolated techniques rather than as an interacting system of limits. This survey adopts a constraint-centric perspective and organizes recent progress around three coupled bottlenecks: data efficiency (what to train on), memory efficiency (how to fit training), and compute budget awareness (when and where to spend FLOPs). On the data axis, we review selection and pruning methods that maximize learning per token, ranging from scalable proxy signals based on learning dynamics to gradient- and influence-based scoring, as well as difficulty-aware and curriculum-style strategies. We highlight emerging evidence that different notions of good data dominate in different regimes, implying that optimal subsets depend on the task objective and resource budget rather than being universal. On the systems side, we show that GPU memory, not raw compute, is often the dominant bottleneck in fine-tuning, and that effective scaling requires jointly reducing weight storage, optimizer states, and activation memory rather than optimizing any single component in isolation. Beyond memory, we frame training and inference as compute-governed processes in which optimization, data selection, and decoding must explicitly account for finite FLOP budgets. We review evidence for compute-optimal allocation and stopping rules, where computation should be halted or reallocated once marginal performance gains fall below a budget-dependent threshold. Together, these results unify compute-aware data selection, scaling laws, and adaptive inference under a common principle of resource-conditioned decision-making.

cs.LG↗

arXiv2026

Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness

Building on recent advances in representation learning for wireless channels, this work investigates the cost-benefit trade-offs of high-dimensional channel embeddings in practical systems. We benchmark multiple wireless representations: high-dimensional learned embeddings from a wireless foundation model, compact autoencoder-based representations with significantly lower dimensionality, and raw data baselines, evaluating their performance across diverse downstream tasks. We then systematically analyze data efficiency, noise robustness, and computational complexity, explicitly characterizing the resource overhead associated with high-dimensional embeddings. Beyond standard tasks such as line-of-sight/non-line-of-sight (LoS/NLoS) classification and beam selection, we introduce power allocation as a new downstream task. Our results reveal clear trade-offs: while high-dimensional embeddings can perform well in few-shot regimes for certain tasks, they incur substantial latency and parameter overhead. In contrast, compressed latent representations learned by autoencoders demonstrate improved noise robustness and more stable performance across tasks, while significantly reducing computational and transmission costs.

eess.SP↗

arXiv2026

Analytically Characterized Optimal Power Control for Signal-Level-Integrated Sensing, Computing and Communication in Federated Learning

In the Internet-of-Things (IoT) era, efficient functionality integration is essential to address the growing demands of communication, computation, and sensing. Signal-level integrated sensing, computing, and communication (Sig-ISCC) is envisioned, where a single waveform simultaneously supports sensing, computing and communication via over-the-air computation (AirComp). Meanwhile, federated learning (FL) is widely regarded as a promising distributed machine learning framework that enables network intelligence in a privacy-preserving and secure manner, and exhibits strong synergy with AirComp, which alleviates the communication bottleneck of FL. In this paper, we study uplink Sig-ISCC design for AirComp-FL with joint target detection. We formulate the joint power and receive-scaling control problem, where edge devices' transmitted signals should serve both sensing and AirComp purposes. The goal is to minimize the AirComp aggregation distortion subject to a joint target-detection requirement. Although the resulting problem is non-convex in the original variables, we show that it admits an equivalent convex reformulation after a suitable variable transformation. By exploiting analytical optimality properties, we develop a robust, optimal, and polynomial-time-complexity algorithm that efficiently achieves the optimal transmit powers and receive scaling factor. Simulation results validate the optimality and numerical robustness of the proposed algorithm and show its superior FL performance compared to baseline methods.

cs.IT↗

arXiv2026

Physical Layer Deception as a Stackelberg Game: Strategy Regimes, Equilibrium, and Robust Design

Physical layer deception (PLD) combines physical layer security (PLS) with deception: the transmitter actively misleads the eavesdropper with falsified information. We model the transmitter-eavesdropper interaction as a Stackelberg game in which the transmitter commits to a resource allocation and encryption strategy, and each receiver best-responds by selecting among three decryption modes: Perception, Dropping, and Exclusion. Using semantic distortion as the metric, we derive closed-form switching surfaces that partition the parameter space into strategy regimes and identify conditions under which each regime dominates. The robust operating point, at the peak of the worst-case distortion envelope, is shown to be a Stackelberg equilibrium; iterative best-response dynamics oscillate around it with strictly lower time-averaged security. We evaluate the design under Nakagami-m fading with static and adaptive transmitter strategies, benchmarked against a classical PLS baseline. Numerical results validate the regime characterization and show 12-55% higher eavesdropper distortion than the erasure-only baseline across all fading conditions.

cs.CR↗

arXiv2026

A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments

Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, FL often suffers from significant communication and computational overhead, limiting its scalability and sustainability. In this work, we introduce a Full Compression Pipeline (FCP) for FL in communication-constrained environments. FCP integrates three complementary deep compression techniques (pruning, quantization, and Huffman encoding) into a unified end-to-end framework. By compressing local models and communication payloads, FCP substantially reduces transmission costs and resource consumption while maintaining competitive accuracy. To quantify its impact, we develop an evaluation framework that captures both communication and computation overheads as a unified model cost, allowing a holistic assessment of efficiency trade-offs. The pipeline is evaluated in an independent and identically distributed (IID) and non-IID data setting. In one representative scenario, training a ResNet-12 model on the CIFAR-10 dataset with ten clients and a 2 Mbps bandwidth, the FCP achieves more than 11$\times$ reduction in model size, with only a 2% drop in accuracy compared to the uncompressed baseline. This results in an FL training that is more than 60% faster.

cs.LG↗

arXiv2026

Deep Reinforcement Learning for Cognitive Time-Division Joint SAR and Secure Communications

Synthetic aperture radar (SAR) imaging can be exploited to enhance wireless communication performance through high-precision environmental awareness. However, integrating sensing and communication functionalities in such wideband systems remains challenging, motivating the development of a joint SAR and communication (JSARC) framework. We propose a dynamic time-division JSARC (TD-JSARC) framework for secure aerial communications that is relevant for critical scenarios, such as surveillance or post-disaster communication, where conventional localization of mobile adversaries often fails. In particular, we consider a secure downlink communication scenario where an aerial base station (ABS) serves a ground user (UE) in the presence of a ground-moving eavesdropper. To detect and track the eavesdropper, the ABS uses cognitive SAR along-track interferometry (ATI) to estimate its position and velocity. Based on these estimates, the ABS applies adaptive beamforming and artificial-noise jamming to enhance secrecy. To this end, we jointly optimize the time and power allocation to maximize the worst-case secrecy rate, while satisfying both SAR and communication constraints. Using the estimated eavesdropper trajectory, we formulate the problem as a Markov decision process (MDP) and solve it via deep reinforcement learning (DRL). Simulation results show that the proposed learning-based approach outperforms both learning and non-learning baseline schemes employing equal-aperture and random time allocation. The proposed method also generalizes well to previously unseen eavesdropper motion patterns.

cs.IT↗

arXiv2026

On Signal Peak Power Constraint of Over-the-Air Federated Learning

Federated learning (FL) has been considered a promising privacy preserving distributed edge learning framework. Over-the-air computation (AirComp) leveraging analog transmission enables the aggregation of local updates directly over-the-air by exploiting the superposition properties of wireless multiple-access channels, thereby alleviating the communication bottleneck issues of FL compared with digital transmission schemes. This work points out that existing AirComp-FL overlooks a key practical constraint, the instantaneous peak-power constraints due to the non-linearity of radio-frequency power amplifiers. Operating directly in non-linear region causes in-band and out-of-band distortions. We present and analyze the effect of the default method that limits the signal's peak power and out-of-band distortions, iterative amplitude clipping combined with filtering. We investigate the effect of imposing instantaneous peak-power constraints in AirComp-FL for both single-carrier and multi-carrier orthogonal frequency-division multiplexing (OFDM) systems. Simulation results demonstrate that, in practical settings, the instantaneous transmit power in AirComp-FL regularly exceeds the power-amplifier linearity limit. As the first work of this line of research, it is essential to evaluate if this is an actual problem that has an impact on FL performance. We therefore apply the classic method of iterative clipping and filtering, and show that the FL performance degrades more or less depending on the scenarios. The degradation becomes pronounced especially in multi-carrier OFDM systems due to the in-band distortions caused by clipping and filtering.

eess.SP↗

arXiv2026

Conformal Cross-Modal Active Learning

Foundation models for vision have transformed visual recognition with powerful pretrained representations and strong zero-shot capabilities, yet their potential for data-efficient learning remains largely untapped. Active Learning (AL) aims to minimize annotation costs by strategically selecting the most informative samples for labeling, but existing methods largely overlook the rich multimodal knowledge embedded in modern vision-language models (VLMs). We introduce Conformal Cross-Modal Acquisition (CCMA), a novel AL framework that bridges vision and language modalities through a teacher-student architecture. CCMA employs a pretrained VLM as a teacher to provide semantically grounded uncertainty estimates, conformally calibrated to guide sample selection for a vision-only student model. By integrating multimodal conformal scoring with diversity-aware selection strategies, CCMA achieves superior data efficiency across multiple benchmarks. Our approach consistently outperforms state-of-the-art AL baselines, demonstrating clear advantages over methods relying solely on uncertainty or diversity metrics.

cs.CV↗

arXiv2026

Efficient Joint Resource Allocation for Wireless Powered ISAC with Target Localization

Wireless powered integrated sensing and communication (ISAC) faces a fundamental tradeoff between energy supply, communication throughput, and sensing accuracy. This paper investigates a wireless powered ISAC system with target localization requirements, where users harvest energy from wireless power transfer (WPT) and then conduct ISAC transmissions in a time-division manner. In addition to energy supply, the WPT signal also contributes to target sensing, and the localization accuracy is characterized by Cramér-Rao bound (CRB) constraints. Under this setting, we formulate a max-min throughput maximization problem by jointly allocating the WPT duration, ISAC transmission time allocation, and transmit power. Due to the nonconvexity of the resulting problem, a suitable reformulation is developed by exploiting variable substitutions and the monotonicity of logarithmic functions, based on which an efficient successive convex approximation (SCA)-based iterative algorithm is proposed. Simulation results demonstrate convergence and significant performance gains over benchmark schemes, highlighting the importance of coordinated time-power optimization in balancing sensing accuracy and communication performance in wireless powered ISAC systems.

eess.SP↗

arXiv2026

FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring

Wearable sensors with local data processing can detect health threats early, enhance documentation, and support personalized therapy. In the context of spinal cord injury (SCI), which involves risks such as pressure injuries and blood pressure instability, continuous monitoring can help mitigate these by enabling early deDtection and intervention. In this work, we present a novel distributed machine learning (DML) protocol for human activity recognition (HAR) from wearable sensor data based on gradient-boosted decision trees (XGBoost). The proposed architecture is inspired by Party-Adaptive XGBoost (PAX) while explicitly preserving key structural and optimization properties of standard XGBoost, including histogram-based split construction and tree-ensemble dynamics. First, we provide a theoretical analysis showing that, under appropriate data conditions and suitable hyperparameter selection, the proposed distributed protocol can converge to solutions equivalent to centralized XGBoost training. Second, the protocol is empirically evaluated on a representative wearable-sensor HAR dataset, reflecting the heterogeneity and data fragmentation typical of remote monitoring scenarios. Benchmarking against centralized XGBoost and IBM PAX demonstrates that the theoretical convergence properties are reflected in practice. The results indicate that the proposed approach can match centralized performance up to a gap under 1\% while retaining the structural advantages of XGBoost in distributed wearable-based HAR settings.

cs.LG↗

arXiv2026

Parallel Split Learning with Global Sampling

Parallel split learning (PSL) suffers from two intertwined issues: the effective batch size grows with the number of clients, and data that is not identically and independently distributed (non-IID) skews global batches. We present parallel split learning with global sampling (GPSL), a server-driven scheme that fixes the global batch size while computing per-client batch-size schedules using pooled-level proportions. The actual samples are drawn locally without replacement by each selected client. This eliminates per-class rounding, decouples the effective batch from the client count, and makes each global batch distributionally equivalent to centralized uniform sampling without replacement. Consequently, we obtain finite-population deviation guarantees via Serfling's inequality, yielding a zero rounding bias compared to local sampling schemes. GPSL is a drop-in replacement for PSL with negligible overhead and scales to large client populations. In extensive experiments on CIFAR-10/100 and ResNet-18/34 under non-IID splits, GPSL stabilizes optimization and achieves centralized-like accuracy, while fixed local batching trails by up to 60%. Furthermore, GPSL shortens training time by avoiding inflation of training steps induced by data-depletion. These findings suggest GPSL is a promising and scalable approach for learning in resource-constrained environments.

cs.LG↗

arXiv2026

Active Learning Using Aggregated Acquisition Functions: Accuracy and Sustainability Analysis

Active learning (AL) is a machine learning (ML) approach that strategically selects the most informative samples for annotation during training, aiming to minimize annotation costs. This strategy not only reduces labeling expenses but also results in energy savings during neural network training, thereby enhancing both data and energy efficiency. In this paper, we implement and evaluate various state-of-the-art acquisition functions, analyzing their accuracy and computational costs, while discussing the advantages and disadvantages of each method. Our findings reveal that representativity-based acquisition functions effectively explore the dataset but do not prioritize boundary decisions, whereas uncertainty-based acquisition functions focus on refining boundary decisions already identified by the neural network. This trade-off is known as the exploration-exploitation dilemma. To address this dilemma, we introduce six aggregation structures: series, parallel, hybrid, adaptive feedback, random exploration, and annealing exploration. Our aggregated acquisition functions alleviate common AL pathologies such as batch mode inefficiency and the cold start problem. Additionally, we focus on balancing accuracy and energy consumption, contributing to the development of more sustainable, energy-aware artificial intelligence (AI). We evaluate our proposed structures on various models and datasets. Our results demonstrate the potential of these structures to reduce computational costs while maintaining or even improving accuracy. Innovative aggregation approaches, such as alternating between acquisition functions such as BALD and BADGE, have shown robust results. Sequentially running functions like $K$-Centers followed by BALD has achieved the same performance goals with up to 12\% fewer samples, while reducing the acquisition cost by almost half.

cs.LG↗

arXiv2026

Efficient Trajectory Design and Communication Scheduling for Dual-UAV Jamming-Aided Secure Communication Networks

We study dual-unmanned aerial vehicle (UAV) jamming-aided secure communication networks, in which one UAV delivers confidential data to multiple ground users (GUs), while a cooperative UAV provides protective interference against a ground eavesdropper. To enforce fairness, we maximize the minimum secrecy throughput across GUs by jointly designing trajectories and communication scheduling. The key difficulty lies in the continuous-time nature of UAV trajectories and the tight space-time coupling between the transmitter and the jammer, which jointly render the problem infinite-dimensional and nonconvex. To address these challenges, we characterize, for the first time, the structure of the optimal trajectories and rigorously prove that they follow a collaborative successive hover-and-fly (co-SHF) structure, where the two UAVs visit a limited number of synchronized co-hovering point pairs, and during each flight segment at least one UAV moves at maximum speed. Leveraging this structure, we reformulate the problem into a finite-dimensional form, without loss of optimality, over hovering and turning points, hovering durations, and scheduling. For tractability, we adopt a minimum-distance approximation of continuous anti-collision constraints and employ concave lower bounds on secrecy throughput within a successive convex approximation (SCA) method, which converges and, thanks to the co-SHF reduction in optimization variables and constraints, achieves low computational complexity. Numerical results show that, compared with time-discretization and no-jamming benchmarks, the proposed co-SHF design improves the min-secrecy and user fairness while requiring significantly less runtime.

eess.SY↗

arXiv2026

Study of Robust Power Allocation for User-Centric Cell-Free Massive MIMO Networks

In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence of channel uncertainties resulting from imperfect channel state information (CSI). In this work, we propose a robust power allocation method that formulates the power optimization problem into a least-squares framework, enhanced by Tikhonov regularization to mitigate the adverse effects of channel estimation errors. We integrate our approach with zero-forcing precoding, enabling a design that is both computationally efficient and resilient to CSI imperfections. Numerical results indicate that the proposed method outperforms existing non-robust techniques while benefiting from low computational overhead, making it well-suited for large-scale deployments under CSI uncertainty.

cs.IT↗

arXiv2026

Joint Communication Scheduling and Resource Allocation for Distributed Edge Learning: Seamless Integration in Next-Generation Wireless Networks

Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires a coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round-wise designs that assume a rigid resource allocation throughout each communication round (CR). However, rigid resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior, especially when CR duration far exceeds the channel coherence time due to large model size or limited resources. This is due to the heterogeneous nature of the system, as clients inherently may need to access the network at different time instants. This work zooms into one arbitrary CR, and demonstrates the importance of considering a time-dependent design for sharing the resource pool with HB traffic. We first formulate a timeslot-wise optimization problem to minimize the consumed time by DL within the CR while constrained by a DL energy budget. Due to its intractability, a session-based optimization problem is formulated assuming a CR lasts less than a large-scale coherence time. Some scheduling properties of such multi-server joint communication scheduling and resource allocation framework have been established. An iterative algorithm has been designed to solve such non-convex and non-block-separable-constrained problems. Simulation results confirm the importance of the efficient and accurate integration design proposed in this work.

eess.SY↗

arXiv2026

Confusions and Erasures of Error-Bounded Block Decoders with Finite Blocklength

This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR.

cs.IT↗