arXiv:2607. 11429v1 Announce Type: new Abstract: TR 38.
Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold. In this context, an outage re...
arXiv:2608. 15314v1 Announce Type: new Abstract: Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold.
arXiv:2607. 22637v1 Announce Type: new Abstract: Deep learning has shown strong potential for massive multiple-input multiple-output (Massive MIMO) physical-layer tasks, including channel state information (CSI) feedback and channel estimation.
The paper introduces DRIFT, a lightweight framework for joint channel estimation and prediction in low Earth orbit non-terrestrial networks, aiming to reduce pilot overhead by using data-driven processing after the initial slot. DRIFT refines data-aided channel estimates and forecasts future channel responses with low computational cost, offering two variants based on convolutional and LSTM layers. Simulations show up to 12% spectral efficiency gain over conventional pilot-based systems, with under 200k multiply-accumulate operations suitable for on-board satellite implementation.
arXiv:2405. 17366v3 Announce Type: replace Abstract: We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation that is used for wireless communication simulation in 3D indoor environments.
AIR-LLM is an edge inference architecture that broadcasts large language model (LLM) weights over radio, allowing edge devices to perform matrix-vector multiplications directly in the RF domain without storing or loading the weights. The system uses MIMO spatial multiplexing and an energy‑efficient precoder‑postcoder pair to reduce airtime and calibrate the wireless channel, enabling a single broadcast to serve unlimited users. Experiments on real urban channel models show that AIR-LLM achieves only a 4.0% perplexity loss on LLaMA‑3.1‑8B while saving energy by up to 157.7× compared to FP16 and reducing airtime by over 100× for 20 users.
FedGenSC introduces a federated generative semantic communication system that uses a global generator with local discriminators, a semantic prototype bank, and SNR‑conditioned generation to address instability, semantic drift, and channel‑agnostic issues in GAN‑based federated learning. Experiments on the Europarl dataset over Rayleigh fading channels show that FedGenSC outperforms the FedDeepSC baseline under non‑IID data, achieving up to a 58.2% relative improvement in BLEU‑1 at 18 dB. Ablation studies confirm that each component independently contributes to the overall performance gains.
arXiv:2509. 24935v3 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning.
arXiv:2608. 04050v1 Announce Type: cross Abstract: Wi-Fi networks are moving beyond random channel access toward tightly coordinated operation across access points (APs), a shift reflected in Wi-Fi 8's multi-AP coordination (MAPC).
arXiv:2604. 22005v2 Announce Type: replace-cross Abstract: Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems.
arXiv:2609.35763v3 Announce Type: replace Abstract: Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representa...
The paper introduces a semantic communication approach for CSI feedback in FDD massive MIMO, where the UE sends a learned embedding optimized for beam selection rather than reconstructing the full channel. Experiments show that an 8‑dimensional embedding derived from just 43 NR CSI‑RS pilots in the angular‑delay domain achieves the best beam prediction accuracy, surpassing methods that use the entire 512‑subcarrier channel. This demonstrates that beam‑relevant information is inherently low‑dimensional, allowing the semantic encoder to discard irrelevant details and transmit only the intent needed for beam selection.