#wireless

9 curated events

papersTODAY 04:00 UTC

Hybrid Feedback-Guided Learning Approach Targets Wireless Panoramic Scene Delivery

A new arXiv preprint proposes a hybrid feedback-guided optimal learning method for delivering interactive panoramic content over wireless networks. The work addresses the tight latency, frame rate, and synchronization demands of immersive VR and AR applications, where edge servers must render scenes and stream them to users. It frames the problem as jointly optimizing rendering and transmission decisions under feedback from the wireless channel and viewer interaction.

arxiv/wireless/edge-computing/vr

papersTODAY 04:00 UTC

Joint optimization method targets client drift in wireless federated learning

A new arXiv paper proposes jointly optimizing federated learning and wireless transmission to counter client drift caused by heterogeneous data and multiple local updates. The approach also aims to handle unreliable wireless links, where transmission errors can invalidate model updates.

federated-learning/wireless/optimization/research

papersTODAY 04:00 UTC

Data-Driven Method for Outdoor Channel Prediction in 5G and Beyond

A revised arXiv paper examines how machine learning and data-driven techniques can predict wireless channel behavior in outdoor 5G and future networks. Accurate channel estimation is presented as key to improving quality of service for users. The work sits within ongoing efforts to make beyond-5G systems more reliable and efficient.

5g/wireless/channel-prediction/arxiv

papersTODAY 04:00 UTC

Adaptive 3D-RoPE: Physics-Aligned Positional Encoding for Wireless Foundation Models

A revised arXiv paper proposes Adaptive 3D-RoPE, a rotary positional encoding scheme designed to match the physical structure of wireless channel data. The method targets wireless foundation models used for unified channel state information (CSI) acquisition in 6G networks, where such models already outperform task-specific baselines. The authors argue that aligning positional encoding with physics improves how these models generalize across tasks.

wireless/positional-encoding/6g/foundation-models

papersSEP 10 04:00 UTC

Predicting HARQ Retransmissions to Improve MCS Selection in 5G AI-RAN

Researchers tackle a weakness in 5G link adaptation, where modulation and coding scheme choices depend on channel measurements and HARQ feedback that quickly become outdated in fast-changing conditions. Their data-driven approach predicts the likelihood of retransmissions, allowing the network to select more robust transmission parameters proactively. The work targets AI-enhanced radio access networks, where such predictions could improve throughput and reliability.

5g/wireless/machine-learning/research

papersSEP 10 04:00 UTC

Decision Transformer applied to UAV-mounted RIS-assisted dynamic D2D communications

A new arXiv paper proposes using a decision transformer to control a reconfigurable intelligent surface mounted on a UAV to support device-to-device links whose activation is stochastic. The approach models UAV motion and attitude along with time-varying Rician channel angles, adapting the surface configuration as conditions change.

transformers/reinforcement-learning/wireless/uav

papersSEP 10 04:00 UTC

Graph Neural Networks Proposed for Wideband Hybrid Beamforming Optimization in 6G

A new arXiv paper presents an efficient graph neural network method for optimizing multicarrier wideband hybrid beamforming, a key technique for highly directional 6G links. The work targets beam squint, a distortion that grows as 6G systems use much wider frequency bands and that traditionally requires costly true-time-delay filters. The authors position the learning-based approach as a more efficient alternative for next-generation wireless systems.

6g/wireless/graph-neural-networks/beamforming

papersSEP 10 04:00 UTC

Two-Stage MUSIC Method Aims at Reliable Near-Field Multi-User Positioning

A new arXiv paper addresses localization of multiple users in the near-field region of upcoming wireless networks, where coherent scattering typically undermines accuracy. The proposed method applies a two-stage MUSIC-based procedure to keep positioning reliable when multipath propagation degrades conventional techniques. The work targets high-precision multi-user localization for next-generation communication systems.

wireless/localization/signal-processing

papersSEP 10 04:00 UTC

Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

A new arXiv preprint presents an approach to predicting communication beams in vehicle-to-everything networks using integrated sensing and communication combined with multiple sensing modalities. The authors argue that current sensing-assisted beamforming leans too heavily on radio-frequency sensing, and their method adds complementary signals to improve robustness in dynamic road environments.

v2x/wireless/research/sensing