Keskeiset käsitteet
The proposed detection scheme can accurately estimate the number of targets in joint communication and sensing cellular networks that employ time division mode (TDM) or concurrent mode (CM) resource sharing, even in the presence of clutter and temporally correlated noise.
Tiivistelmä
The paper proposes a detection scheme for estimating the number of targets in joint communication and sensing (JCAS) cellular networks that employ TDM or CM resource sharing. The proposed detection method allows for the presence of clutter and/or temporally correlated noise.
The key highlights are:
- The detection scheme is studied with respect to the JCAS trade-off parameters that control the time slots in TDM and the power resources in CM allocated to sensing and communications.
- The performance of two fundamental transmit beamforming schemes, typical for JCAS, is compared in terms of the receiver operating characteristics (ROC) curves.
- The results indicate that the TDM scheme generally gives better detection performance compared to the CM scheme, although both schemes outperform existing approaches provided that their respective trade-off parameters are tuned properly.
- The proposed ratio-based detection test can accurately estimate the number of targets, even in the presence of clutter and temporally correlated noise, whereas existing methods like MDL and AIC fail in such scenarios.
Tilastot
The total number of transmit (sensing and communication) antennas is M = 8.
The number of sensing receiver antennas is N = 16.
The total number of sensing and communication slots is T = 64.
Lainaukset
"The proposed detection method allows for the presence of clutter and/or temporally correlated noise."
"The results indicate that the TDM scheme generally gives better detection performance compared to the CM scheme, although both schemes outperform existing approaches provided that their respective trade-off parameters are tuned properly."
"The proposed ratio-based detection test can accurately estimate the number of targets, even in the presence of clutter and temporally correlated noise, whereas existing methods like MDL and AIC fail in such scenarios."