# Semantic Communications in the THz Band

arXiv: https://arxiv.org/abs/2607.07455 · HTML: https://mlsanity.com/paper/2607.07455/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2026.

## Built on by (most cited first, 3 of 3)

- [FedGenSC: Federated Generative Semantic Communication with Channel-Aware Adaptation](https://mlsanity.com/paper/2609.08593/) (2026)
- [Privacy-Preserving Deep Joint Source-Channel Coding with In-Loop Concept Erasure](https://mlsanity.com/paper/2609.13393/) (2026)
- [Channel-Adaptive Region Adjacency Graph Carriers for Semantic Image Communication](https://mlsanity.com/paper/2609.14616/) (2026)

## Abstract (arXiv)

Semantic and terahertz (THz)-band communications are algorithmic and spectral enablers of future wireless networks. This work investigates deep learning-based semantic communication (DeepSC) over THz channels. We show that DeepSC models trained solely under additive white Gaussian noise generalize well to the tested THz block- and fast-fading channels when receiver-side compensation is applied. To enable fully data-driven reception, we propose a lightweight neural detector that does not require channel state information (CSI). At 0.3 THz, DeepSC outperforms a throughput-matched traditional coded communication system baseline over 0-12 dB signal-to-noise ratio (SNR), achieving more than 50 percentage-point higher Bilingual Evaluation Understudy unigram (BLEU-1) score. The proposed pilot-free detector outperforms minimum mean square error (MMSE) equalization with both perfect and imperfect CSI and remains robust to frequency offsets up to 50 MHz, highlighting the resilience of semantic communication to THz channel impairments.