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Performance Analysis and Sensing-Aware Resource Allocation for Full-Duplex Massive MIMO ISCC Networks — arXiv:2610.10598

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10598
License (as archived): CC0-1.0
Archived: 2026-10-09T06:29:29.788Z
SHA-256 of the archived text: 3bfe672c6b29f1f7e14affba1284861620c7ad65f4de5ef0540d71924aa617fa
Stable URL of this page: https://metraipolis.com/documento/dc_4ff047db5587

How to cite

Performance Analysis and Sensing-Aware Resource Allocation for Full-Duplex Massive MIMO ISCC Networks — arXiv:2610.10598 — arXiv (en), https://arxiv.org/abs/2610.10598 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4ff047db5587 (Metraipolis archive dc_4ff047db5587, sha256 3bfe672c6b29f1f7e14affba1284861620c7ad65f4de5ef0540d71924aa617fa)

BibTeX

@misc{dc4ff047db5587,
  title = {Performance Analysis and Sensing-Aware Resource Allocation for Full-Duplex Massive MIMO ISCC Networks — arXiv:2610.10598},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10598},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4ff047db5587; sha256 3bfe672c6b29f1f7e14affba1284861620c7ad65f4de5ef0540d71924aa617fa},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10598. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Emerging edge applications must sense their environment and process data within tight latency budgets. Full-duplex (FD) integrated sensing, communication, and computing (ISCC) supports these functions concurrently, but radar probing interferes with uplink reception while user transmissions degrade target estimation. These coupled effects call for joint sensing and offloading decisions. We investigate a FD massive multiple-input multiple-output (mMIMO) ISCC network in which users partially offload tasks to an edge server while an access point probes a target. For zero-forcing uplink reception with imperfect channel estimates, we derive a tractable rate bound and target-angle Cramer-Rao lower bounds (CRLBs) under the stated interference and beam-alignment assumptions. We then jointly optimize offloading fractions, user and radar transmit powers, and edge computing resources to minimize average end-to-end task latency subject to sensing-accuracy, communication-rate, energy, deadline, and resource constraints. A successive convex approximation algorithm is developed to efficiently address the resulting nonconvex problem. The analysis shows that larger arrays improve sensing and uplink performance, but residual radar interference creates a finite rate ceiling when user powers scale down with array size. Simulation results show joint allocation reduces radar transmit power by approximately 86% relative to equal communication-user power at a -40 dB CRLB threshold and lowers latency compared with fixed-allocation and half-duplex schemes.

Tema degli esploratori: matematica e informatica teorica (elenco arXiv math.IT, nuovi lavori del giorno).
Categorie arXiv: Information Theory (cs.IT).
Pagina del lavoro: https://arxiv.org/abs/2610.10598 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T05:54:06.687Z dalla pagina https://arxiv.org/list/math.IT/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).