TY - GEN
T1 - Joint Beamforming and Trajectory Optimizations for Statistical Delay and Error-Rate Bounded QoS Over MIMO-UAV/IRS-Based 6G Mobile Edge Computing Networks Using FBC
AU - Zhang, Xi
AU - Wang, Jingqing
AU - Poor, H. Vincent
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Tremendous research efforts have been made in conceptualizing 6G mobile wireless networks to support unprecedented scenarios with extremely diverse and challenging delay and error-rate bounded quality-of-services (QoS) requirements for ultra-reliable and low latency communications (URLLC), especially for cell-edge users. However, QoS performance is greatly limited by the computation capacity and finite battery capacity. To address this issue, mobile edge computing (MEC) has been developed by enabling mobile users to offload partial or complete computation-intensive tasks to MEC servers for computing. In addition, leveraging the significant improvements in coverage rate and spectral efficiency, intelligent reflecting surface (IRS)-unmanned aerial vehicle (UAV) integrated MEC systems, which smartly reconfigure and design wireless propagation environments by bypassing blockage of line-of-sight (LOS) communications, can avoid service starvation of cell-edge users while supporting QoS for URLLC. However, how to statistically upper-bound both delay and error rate for URLLC in multiple-input multiple-output (MIMO)-UAV/IRS-based MEC systems still remains a challenging problem, especially when considering short-packet communications, such as finite blocklength coding (FBC). To overcome these difficulties, in this paper we propose FBC-based joint beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS for URLLC with MEC. First, we develop MIMO-UAV/IRS-based 3D wireless channel models using FBC. Second, we formulate and solve the ϵ-effective energy-efficiency maximization problems by converting non-convex problems into convex problems in both single-user and multiple-user scenarios. Finally, the obtained numerical analyses validate and evaluate our developed MIMO-UAV/IRS-based schemes.
AB - Tremendous research efforts have been made in conceptualizing 6G mobile wireless networks to support unprecedented scenarios with extremely diverse and challenging delay and error-rate bounded quality-of-services (QoS) requirements for ultra-reliable and low latency communications (URLLC), especially for cell-edge users. However, QoS performance is greatly limited by the computation capacity and finite battery capacity. To address this issue, mobile edge computing (MEC) has been developed by enabling mobile users to offload partial or complete computation-intensive tasks to MEC servers for computing. In addition, leveraging the significant improvements in coverage rate and spectral efficiency, intelligent reflecting surface (IRS)-unmanned aerial vehicle (UAV) integrated MEC systems, which smartly reconfigure and design wireless propagation environments by bypassing blockage of line-of-sight (LOS) communications, can avoid service starvation of cell-edge users while supporting QoS for URLLC. However, how to statistically upper-bound both delay and error rate for URLLC in multiple-input multiple-output (MIMO)-UAV/IRS-based MEC systems still remains a challenging problem, especially when considering short-packet communications, such as finite blocklength coding (FBC). To overcome these difficulties, in this paper we propose FBC-based joint beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS for URLLC with MEC. First, we develop MIMO-UAV/IRS-based 3D wireless channel models using FBC. Second, we formulate and solve the ϵ-effective energy-efficiency maximization problems by converting non-convex problems into convex problems in both single-user and multiple-user scenarios. Finally, the obtained numerical analyses validate and evaluate our developed MIMO-UAV/IRS-based schemes.
KW - FBC
KW - Joint active/passive beamforming and UAV trajectory optimization
KW - MIMO
KW - UAV-IRS
KW - URLLC
KW - mobile edge computing
KW - statistical QoS
UR - http://www.scopus.com/inward/record.url?scp=85140887188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140887188&partnerID=8YFLogxK
U2 - 10.1109/ICDCS54860.2022.00099
DO - 10.1109/ICDCS54860.2022.00099
M3 - Conference contribution
AN - SCOPUS:85140887188
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 983
EP - 993
BT - Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems, ICDCS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd IEEE International Conference on Distributed Computing Systems, ICDCS 2022
Y2 - 10 July 2022 through 13 July 2022
ER -