arXiv:2606. 00263v1 Announce Type: cross Abstract: This letter studies CSI denoising for MIMO--OFDM with variable NR resource block (RB) allocations.
arXiv:2607. 08717v1 Announce Type: new Abstract: Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffective.
arXiv:2609.14735v1 Announce Type: cross Abstract: Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial N...
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.
arXiv:2610.15509v1 Announce Type: cross Abstract: KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill t...
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:2606.19617v2 Announce Type: replace-cross Abstract: We present GB-LSR (Global-Bandwidth Local Spectral Representation), a fixed-grid local spectral representation for continuous image decoding....
arXiv:2607. 05419v1 Announce Type: cross Abstract: Accurate and timely channel state information (CSI) is essential for next-generation wireless systems, yet existing works treat CSI compression and CSI prediction as separate problems, both in academia and in current 3GPP studies.
arXiv:2606. 16607v1 Announce Type: cross Abstract: This paper considers neural channel state information (CSI) compression for time-varying massive multiple-input multiple-output (MIMO) channels in frequency division duplex (FDD) systems with limited feedback resources.
arXiv:2606. 04752v1 Announce Type: cross Abstract: Transformers consuming multi-channel scalar signals must embed $C$ simultaneous values into one $d_{\text{model}}$-dimensional vector per time step.
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:2609.23945v1 Announce Type: new Abstract: In-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems enable simultaneous transmission and reception on the same frequency band, imp...
arXiv:2607. 17425v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) compress model activations into sparse codes, but equal reconstruction error and sparsity can preserve different linearly decodable signals.