TY - GEN
T1 - Distributed MMSE Detection with One-Shot Combining for Cooperative Aerial Networks in Presence of Imperfect CSI
AU - Pan, Xuesong
AU - Zheng, Zhong
AU - Huang, Xueqing
AU - Fei, Zesong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we investigate the multiple access technique for the aerial networks, where the multi-antenna base stations are carried by unmanned aerial vehicles (UAVs) to serve ground users. The uplink signals are transmitted by the users simultaneously, then detected and recovered by the aerial networks. On one hand, compared to the case that each UAV individually detects the signals of serving users, we aim to improve the quality of the recovered signals by using cooperative techniques that leverage the signals from multiple UAVs. On the other hand, the fully-centralized cooperation requires signaling exchange between UAVs, which incurs huge signaling overhead and latency, and is infeasible for aerial networks. Therefore, we propose a two-stage distributed minimum mean squared error (MMSE) detection with one-shot signal combining. Specifically, the users' signals are first locally detected by each UAV via the MMSE detector, and then weighted combined at the central UAV in a one-shot manner, where the combining weights are designed to minimize the MSE between the combined signals and the original signals. When only imperfect channel state information is locally available at each UAV, by using the random matrix theory, these combining weights are shown to depend on the long-term channel statistics and thus, greatly reduce the interaction overhead and latency. Numerical results show that the proposed scheme outperforms the non-cooperative detection in terms of the achievable spectral efficiency. Meanwhile, with a much smaller interaction overhead, the proposed scheme achieves comparable spectral efficiency as the fully centralized signal detection.
AB - In this paper, we investigate the multiple access technique for the aerial networks, where the multi-antenna base stations are carried by unmanned aerial vehicles (UAVs) to serve ground users. The uplink signals are transmitted by the users simultaneously, then detected and recovered by the aerial networks. On one hand, compared to the case that each UAV individually detects the signals of serving users, we aim to improve the quality of the recovered signals by using cooperative techniques that leverage the signals from multiple UAVs. On the other hand, the fully-centralized cooperation requires signaling exchange between UAVs, which incurs huge signaling overhead and latency, and is infeasible for aerial networks. Therefore, we propose a two-stage distributed minimum mean squared error (MMSE) detection with one-shot signal combining. Specifically, the users' signals are first locally detected by each UAV via the MMSE detector, and then weighted combined at the central UAV in a one-shot manner, where the combining weights are designed to minimize the MSE between the combined signals and the original signals. When only imperfect channel state information is locally available at each UAV, by using the random matrix theory, these combining weights are shown to depend on the long-term channel statistics and thus, greatly reduce the interaction overhead and latency. Numerical results show that the proposed scheme outperforms the non-cooperative detection in terms of the achievable spectral efficiency. Meanwhile, with a much smaller interaction overhead, the proposed scheme achieves comparable spectral efficiency as the fully centralized signal detection.
KW - Stieltjes transform
KW - Unmanned aerial vehicle
KW - channel estimation
KW - deterministic equivalents
UR - http://www.scopus.com/inward/record.url?scp=85178292269&partnerID=8YFLogxK
U2 - 10.1109/ICC45041.2023.10279641
DO - 10.1109/ICC45041.2023.10279641
M3 - Conference contribution
AN - SCOPUS:85178292269
T3 - IEEE International Conference on Communications
SP - 5978
EP - 5984
BT - ICC 2023 - IEEE International Conference on Communications
A2 - Zorzi, Michele
A2 - Tao, Meixia
A2 - Saad, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Communications, ICC 2023
Y2 - 28 May 2023 through 1 June 2023
ER -