TY - GEN
T1 - Statistical QoS-Driven Beamforming and Trajectory Optimizations in UAV/IRS-Based 6G Wireless Networks in the Non-Asymptotic Regime
AU - Zhang, Xi
AU - Wang, Jingqing
AU - Vincent Poor, H.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In order to support extremely diverse and challenging delay and error-rate bounded quality-of-service (QoS) requirements for ultra-reliable and low latency communications (URLLC), a number of of promising 6G techniques, including unmanned-aerial-vehicles (UAVs), intelligent reflecting surfaces (IRSs), finite blocklength coding (FBC), etc., are being developed for potential use in 6G wireless networks. In addition, to implement over-the-air intelligent reflection and enlarge wireless service areas, integrating UAVs and IRSs provides a promising means to significantly enhance line-of-sight (LOS) coverage due to the relatively high altitude and 3D mobility of the UAVs. However, it is very challenging to characterize system models and guarantee statistical delay and error rate bounded QoS requirements in such complicated and dynamic UAV/IRS-based wireless network environments while supporting URLLC. To overcome these difficulties, in this paper we propose joint passive IRS beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS provisioning for URLLC over UAV/IRS-based wireless networks using FBC. First, we develop UAV/IRS-based 3D wireless channel models in the finite blocklength regime. Second, we formulate and solve the FBC-based ϵ-effective energy-efficiency maximization problem by jointly optimizing power allocation, passive IRS beamforming, and UAV trajectory for our developed schemes. Finally, the obtained simulation results validate and evaluate our proposed schemes over UAV/IRS-based wireless networks.
AB - In order to support extremely diverse and challenging delay and error-rate bounded quality-of-service (QoS) requirements for ultra-reliable and low latency communications (URLLC), a number of of promising 6G techniques, including unmanned-aerial-vehicles (UAVs), intelligent reflecting surfaces (IRSs), finite blocklength coding (FBC), etc., are being developed for potential use in 6G wireless networks. In addition, to implement over-the-air intelligent reflection and enlarge wireless service areas, integrating UAVs and IRSs provides a promising means to significantly enhance line-of-sight (LOS) coverage due to the relatively high altitude and 3D mobility of the UAVs. However, it is very challenging to characterize system models and guarantee statistical delay and error rate bounded QoS requirements in such complicated and dynamic UAV/IRS-based wireless network environments while supporting URLLC. To overcome these difficulties, in this paper we propose joint passive IRS beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS provisioning for URLLC over UAV/IRS-based wireless networks using FBC. First, we develop UAV/IRS-based 3D wireless channel models in the finite blocklength regime. Second, we formulate and solve the FBC-based ϵ-effective energy-efficiency maximization problem by jointly optimizing power allocation, passive IRS beamforming, and UAV trajectory for our developed schemes. Finally, the obtained simulation results validate and evaluate our proposed schemes over UAV/IRS-based wireless networks.
KW - 6G wireless networks
KW - FBC
KW - Joint beamforming and UAV trajectory optimization
KW - UAV/IRS
KW - URLLC
KW - statistical QoS
UR - http://www.scopus.com/inward/record.url?scp=85136278656&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136278656&partnerID=8YFLogxK
U2 - 10.1109/ISIT50566.2022.9834715
DO - 10.1109/ISIT50566.2022.9834715
M3 - Conference contribution
AN - SCOPUS:85136278656
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 3333
EP - 3338
BT - 2022 IEEE International Symposium on Information Theory, ISIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Information Theory, ISIT 2022
Y2 - 26 June 2022 through 1 July 2022
ER -