Joint Optimization of IRS and UAV-Trajectory: For Supporting Statistical Delay and Error-Rate Bounded QoS Over mURLLC-Driven 6G Mobile Wireless Networks Using FBC

Xi Zhang, Jingqing Wang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

Massive ultra-reliable and low-latency communications (mURLLC) has been developed as a new and dominating 6G-standard services to support statistical quality of service (QoS) provisioning while raising several major design issues, including massive connectivity, ultra-low latency, and super-reliability. Correspondingly, a number of emerging 6G candidate enablers, including statistical delay and error-rate bounded QoS provisioning, finite blocklength coding (FBC), intelligent reflecting surfaces (IRS), unmanned aerial vehicle (UAV), etc., have been developed to support mURLLC. Specifically, due to the potential improvements in coverage capability as well as the spectral efficiency, both IRS and UAV have been widely proposed to reconfigure wireless propagation environments to compensate for blocked line-of-sight (LOS) communication links and create controllable and smart radio environments. In addition, to solve the massive connectivity issues imposed by mURLLC, integrating UAV with IRS provides a promising means to significantly enhance LOS coverage due to the relatively high altitude and 3D mobility of the UAVs. However, although small-packet communications enabled by FBC are usually employed for massive access to reduce access latency and decoding complexity, how to upper-bound both delay and error rate while efficiently supporting mURLLC in IRS-UAV-integrated systems still remains a challenging problem.

Original languageEnglish (US)
Pages (from-to)55-63
Number of pages9
JournalIEEE Vehicular Technology Magazine
Volume17
Issue number2
DOIs
StatePublished - Jun 1 2022

All Science Journal Classification (ASJC) codes

  • Automotive Engineering

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