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Related Articles from SNS
Cooperative Mitigation against Learning-Based Reactive Jammers: Analysis and SDR Validation
arXiv:2606.01197v1 Announce Type: new Abstract: Motivated by recent developments in full-duplex radios, cognitive radios, and data-driven signal-processing, we propose a novel class of reactive jamming adversaries wherein the adversary transmits jamming energy on the victim's frequency band while simultaneously monitoring various energy statistics in the network to detect the presence of potential countermeasures, thereby trapping the victim. These adversaries employ generalized energy...
A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References
arXiv:2508.14623v2 Announce Type: replace-cross Abstract: This paper examines the implications of using the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) as both evaluation and training objective in supervised speech separation, when the training references contain noise, as is the case with the de facto benchmark WSJ0-2Mix. A derivation of the SI-SDR with noisy references reveals that noise limits the achievable SI-SDR, or leads to undesired noise in the separated outputs. To address...
Riemannian Stochastic Optimization for Sufficient Dimension Reduction
arXiv:2606.00413v1 Announce Type: cross Abstract: Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that preserves the conditional mean of the response. Existing gradient-based estimators either operate in the ambient space and suffer from the curse of dimensionality, or localize in the reduced space at a per-outer-iteration cost at least quadratic in the sample size. We show that minimizers of the...
AtlasRAN: Timing-Aware Evaluation of Open-source 5G Platforms for Integrated Wireless Testbeds
Announce Type: replace Abstract: Open-source 5G and O-RAN experimentation now spans discrete-event simulators, host-OS emulators, SDR hardware-in-the-loop testbeds, O-RU/Open Fronthaul deployments, wireless digital twins, and accelerator-backed RAN runtimes. These environments may expose similar protocol interfaces while preserving very different timing, I/O, synchronization, buffering, transport, and observability behavior. Thus, studies that appear to measure the same network property may...
Rotatable Antenna-Enabled Mobile Edge Computing
Announce Type: replace Abstract: In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to...
Secure RSMA-based Visible Light Networks under Spatial Correlation
arXiv:2606.01941v1 Announce Type: new Abstract: This paper investigates the secrecy sum rate (SSR) of rate-splitting multiple access (RSMA)-based visible light communication (VLC) systems considering internal eavesdropping, where legitimate users may intercept private data intended for others. We formulate an optimization problem to maximize the SSR of the system, which is inherently non-convex due to the complex coupling of the objective function and constraints. To this end, two different...
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Secure RSMA-based Visible Light Networks under Spatial Correlation
Announce Type: replace Abstract: This paper investigates the secrecy sum rate (SSR) of rate-splitting multiple access (RSMA)-based visible light communication (VLC) systems considering internal eavesdropping, where legitimate users may intercept private data intended for others. We formulate an optimization problem to maximize the SSR of the system, which is inherently non-convex due to the complex coupling of the objective function and constraints. To this end, two different approaches...
MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation
arXiv:2606.09677v1 Announce Type: cross Abstract: While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening quality. To address this, we propose a novel MeanFlow-based one-step generative corrector (MeCo). MeCo learns a conditional average velocity field to map discriminative estimates directly onto the clean speech manifold in a single step.
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