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arXiv Machine Learning

MILAAP: Mobile Link Allocation via Attention-based Prediction

The paper introduces MiLAAP, an attention‑based framework that predicts channel occupancy and node motion in channel‑hopping communication systems without exchanging state information. By leveraging self‑attention and multi‑head attention, each node passively observes local channel activity to forecast interference patterns and mobility, achieving near‑perfect prediction accuracy across varied mobility scenarios and demonstrating zero‑shot generalizability to new channel‑sequence periods.

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arXiv AI
Aug 28

MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction

MambaCSP is a hybrid-attention state space model that replaces transformer-based backbones with a linear-time Mamba architecture for channel state prediction. By adding lightweight patch‑mixer attention layers, it captures long‑range dependencies while maintaining hardware efficiency. Experiments on MISO‑OFDM show 9‑12% higher accuracy, 3× faster throughput, 2.6× lower VRAM usage, and 2.9× faster inference compared to LLM‑based methods.

By Aladin Djuhera, Haris Gacanin, Holger Boche
llmsefficiency
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arXiv AI
Jun 3

AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling

arXiv:2606. 03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control.

By Maxime Elkael, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia
agents
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arXiv AI
Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
llmsagentsreinforcement-learningfine-tuning
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arXiv Machine Learning
Oct 22

CellTwin: Reproducible Cellular Network Emulation via High Resolution, in situ Measurement-Driven ML

CellTwin, also known as NeuralEmu, is a machine‑learning‑based emulation framework that learns 5G scheduler behavior from high‑resolution network telemetry. It predicts resource block allocations and modulation schemes for multiple clients, reconstructs background traffic patterns, and runs as a high‑performance Linux middlebox. Compared to existing tools, it reduces emulation error by up to 57% for key real‑time applications such as web‑page load, WebRTC, and cloud gaming.

By Haoran Wan, Yaxiong Xie, Kyle Jamieson
efficiency
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arXiv Machine Learning
1d ago

CSI-JEPA: Towards Foundation Representations for Ubiquitous Wi-Fi Sensing with Minimal Supervision

CSI-JEPA is a self‑supervised framework that learns reusable temporal‑spectral representations from unlabeled Wi‑Fi channel state information (CSI) by predicting latent features of masked channel regions. It tokenizes CSI along time and subcarrier dimensions and uses a channel‑variation‑aware masking strategy to focus on dynamic regions. After pretraining, the frozen encoder is adapted with lightweight adapters to a variety of downstream Wi‑Fi sensing tasks, achieving significant accuracy gains and label savings compared to supervised baselines.

By Xuanhao Luo, Zhizhen Li, Yuchen Liu
llms
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arXiv AI
Oct 5

CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Prediction

CoMemNet is a Continual Memory Network designed for traffic prediction on evolving sensor networks. It combines an online branch that adapts to current data with an exponential‑moving‑average target branch for stable reference, and uses a Wasserstein‑based drift sampler to selectively update drift‑sensitive nodes. A lightweight temporal memory replay buffer stores compact states, allowing efficient adaptation without traversing all historical data. Experiments on PeMS datasets show that CoMemNet maintains stable accuracy and outperforms retraining baselines in both prediction performance and training time.

By Mei Wu, Wenchao Weng, Wenxin Su, Wenjie Tang, Wei Zhou
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