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Accurate acquisition of full channel state information (CSI) is essential for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems, while dense pilot-based estimation incurs substantial overhead. Directly minimizing the prediction error over raw CSI coefficients is frag...
Large-scale multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless datasets offer limited support for heterogeneous environments, particularly in terms of reusable high-fidelity scenes, scalable differentiable ray tracing, and...
Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks. We present Open Telco (OTel), an open telecom AI resource with derived datasets for retrieval, reranking, instruction tuning, and safety/abstention, plus 30 full-parameter post-trained baselines across embedding, reranking, a...
The TelecomGPT-R1 family of open source unified telecom reasoning models structured around four complementary axes: protocol, knowledge, modeling, and fault are introduced, and supervised fine-tuning instills telecom knowledge and evidence-grounded reasoning patterns to overcome the cold start barrier for reinforcement...
Experiments on single-band and cross-band reconstruction demonstrate improved symbol detection and, more notably, substantial beamforming gains despite modest NMSE improvements, indicating that FWM captures task-relevant spatial propagation structure beyond coefficient-wise CSI fitting.
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