arXiv:2506. 00452v5 Announce Type: replace-cross Abstract: In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial.
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arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.
arXiv:2601. 17108v2 Announce Type: replace-cross Abstract: This paper proposes a hybrid Mamba-attention neural architecture to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly for configurations with a large number of subcarriers.
arXiv:2602. 04728v3 Announce Type: replace-cross Abstract: We propose a cross-attention Transformer for joint decoding of uplink OFDM signals received by multiple coordinated access points.
arXiv:2608. 14511v1 Announce Type: cross Abstract: High-order multiple-input multiple-output (MIMO) detection requires efficient search over a large discrete symbol space while producing reliable soft information for channel decoding.
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.
arXiv:2602. 03681v2 Announce Type: replace-cross Abstract: The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios.