Tootfinder

Sensing-Aware Transmit Waveform/Receive Filter Design for OFDM-MBS Systems
Xinghe Li, Kainan Cheng, Huiyong Li, Huiyong Li, Ziyang Cheng
https://arxiv.org/abs/2506.20231

Sensing-Aware Transmit Waveform/Receive Filter Design for OFDM-MBS Systems
In this letter, we study the problem of cooperative sensing design for an orthogonal frequency division multiplexing (OFDM) multiple base stations (MBS) system. We consider a practical scenario where the base stations (BSs) exploit certain subcarriers to realize a sensing function. Since the high sidelobe level (SLL) of OFDM waveforms degrades radar detection for weak targets, and the cross-correlation generated by other BSs further exacerbates detection performance, we devise a joint design sc…

Is this a parody?
I don’t do #InfoSec or other cons so I don’t have a strong sense of whether the “Open Space” concept is brilliant or uproariously absurd. I lean towards the latter because it just seems to me like a recipe for people standing around.

Covert Entanglement Generation over Bosonic Channels
Evan J. D. Anderson, Michael S. Bullock, Ohad Kimelfeld, Christopher K. Eyre, Filip Rozp\k{e}dek, Uzi Pereg, Boulat A. Bash
https://arxiv.org/abs/2506.09474

Covert Entanglement Generation over Bosonic Channels
We explore covert entanglement generation over the lossy thermal-noise bosonic channel, which is a quantum-mechanical model of many practical settings, including optical, microwave, and radio-frequency (RF) channels. Covert communication ensures that an adversary is unable to detect the presence of transmissions, which are concealed in channel noise. We show that a $\textit{square root law}$ (SRL) for covert entanglement generation similar to that for classical: $L_{\rm EG}\sqrt{n}$ entangled b…

Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
Soheyb Ribouh, Osama Saleem, Mohamed Ababsa
https://arxiv.org/abs/2506.20597

Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
The emergence of 6G wireless networks promises to revolutionize vehicular communications by enabling ultra-reliable, low-latency, and high-capacity data exchange. In this context, collaborative perception techniques, where multiple vehicles or infrastructure nodes cooperate to jointly receive and decode transmitted signals, aim to enhance reliability and spectral efficiency for Connected Autonomous Vehicle (CAV) applications. In this paper, we propose an end-to-end wireless neural receiver base…