TY - JOUR
T1 - Cloud–Edge–End CF–MMIMO for UAV Swarms
T2 - Integrated Sensing and Robust Interference Detection in Perception Networks
AU - Sun, Jian
AU - He, Dongxuan
AU - Hou, Huazhou
AU - Du, Pengguang
AU - Fang, Chao
AU - Zhang, Changwei
AU - Xu, Wei
AU - Wang, Dongming
AU - Huang, Yongming
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2026
Y1 - 2026
N2 - With the rapid advancement of sixth-generation (6G) communications, uncrewed aerial vehicle (UAV) swarms have emerged as a key application scenario. However, their high mobility and complex electromagnetic environment pose significant communication challenges, such as frequent handovers and severe co-channel interference. Although conventional cell-free massive MIMO (CF-mMIMO) systems offer ubiquitous coverage, their high backhaul overhead and complex baseband processing limit practical deployment. To address these challenges, this paper proposes a novel cloud-edge-end architecture-based CF-mMIMO communication system to support robust communication in UAV swarm perception networks. Our designed scalable architecture leverages distributed edge units (EDUs) and user-centric distributed units (UCDUs) for collaborative processing, enabling distributed localized processing and user-centric coordination, thus supporting infinite collaboration expansion. Furthermore, we develop a hybrid signal detection scheme that combines EDU-level local minimum mean squared error estimation (MMSE) detection with UCDU-level global signal aggregation, achieving performance close to that of a centralized receiver while significantly reducing backhaul overhead. To address the problem of dynamic interference, we also propose a spatiotemporal-frequency forward continuous mean excision (FCME)-based interference detection framework. This framework effectively suppresses both narrowband and wideband interference by dynamically identifying interfered frequency bands and beams. Simulation results demonstrate that the proposed method outperforms conventional approaches in terms of signal combining and interference suppression, providing a reliable communication link for UAV swarm communications.
AB - With the rapid advancement of sixth-generation (6G) communications, uncrewed aerial vehicle (UAV) swarms have emerged as a key application scenario. However, their high mobility and complex electromagnetic environment pose significant communication challenges, such as frequent handovers and severe co-channel interference. Although conventional cell-free massive MIMO (CF-mMIMO) systems offer ubiquitous coverage, their high backhaul overhead and complex baseband processing limit practical deployment. To address these challenges, this paper proposes a novel cloud-edge-end architecture-based CF-mMIMO communication system to support robust communication in UAV swarm perception networks. Our designed scalable architecture leverages distributed edge units (EDUs) and user-centric distributed units (UCDUs) for collaborative processing, enabling distributed localized processing and user-centric coordination, thus supporting infinite collaboration expansion. Furthermore, we develop a hybrid signal detection scheme that combines EDU-level local minimum mean squared error estimation (MMSE) detection with UCDU-level global signal aggregation, achieving performance close to that of a centralized receiver while significantly reducing backhaul overhead. To address the problem of dynamic interference, we also propose a spatiotemporal-frequency forward continuous mean excision (FCME)-based interference detection framework. This framework effectively suppresses both narrowband and wideband interference by dynamically identifying interfered frequency bands and beams. Simulation results demonstrate that the proposed method outperforms conventional approaches in terms of signal combining and interference suppression, providing a reliable communication link for UAV swarm communications.
KW - CF-mMIMO
KW - UAV swarms
KW - cloud-edge-end architecture
KW - hybrid signal detection
KW - interference detection
UR - https://www.scopus.com/pages/publications/105023316587
U2 - 10.1109/TNSE.2025.3637039
DO - 10.1109/TNSE.2025.3637039
M3 - Article
AN - SCOPUS:105023316587
SN - 2327-4697
VL - 13
SP - 3879
EP - 3893
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
ER -