TY - JOUR
T1 - Physical layer signal processing for XR communications and systems
AU - Wu, Yongpeng
AU - Xu, Mai
AU - Zhai, Guangtao
AU - Zhang, Wenjun
N1 - Publisher Copyright:
© Science China Press 2024.
PY - 2024/12
Y1 - 2024/12
N2 - The explosive growth of immersive and metaverse services has driven the demand for extended reality (XR) transmissions across wireless networks. XR 360° video, captured by omnidirectional cameras and supported by interactive sensors, often provides users with unique immersive experiences and real-time interactions. However, the ultra-high data rate and ultra-low latency requirements for XR 360° video transmissions present new signal processing challenges for XR communication systems. This paper provides a comprehensive survey of promising physical layer signal processing technologies for XR communications and systems. These include multiple antenna technologies for XR, mmWave/terahertz waves for XR communication, machine-learning-based XR transmission, and resource allocations for XR communications. Additionally, we propose a novel signal processing and transmission framework that fully exploits the space-time-frequency dimensions of virtual reality communications. Finally, we summarize the current technical challenges in signal processing for XR communications and related systems and discuss future trends in XR communications.
AB - The explosive growth of immersive and metaverse services has driven the demand for extended reality (XR) transmissions across wireless networks. XR 360° video, captured by omnidirectional cameras and supported by interactive sensors, often provides users with unique immersive experiences and real-time interactions. However, the ultra-high data rate and ultra-low latency requirements for XR 360° video transmissions present new signal processing challenges for XR communication systems. This paper provides a comprehensive survey of promising physical layer signal processing technologies for XR communications and systems. These include multiple antenna technologies for XR, mmWave/terahertz waves for XR communication, machine-learning-based XR transmission, and resource allocations for XR communications. Additionally, we propose a novel signal processing and transmission framework that fully exploits the space-time-frequency dimensions of virtual reality communications. Finally, we summarize the current technical challenges in signal processing for XR communications and related systems and discuss future trends in XR communications.
KW - XR
KW - machine learning
KW - mmWave/terahertz wave
KW - multiple antennas
KW - resource allocations
UR - https://www.scopus.com/pages/publications/85211319237
U2 - 10.1007/s11432-023-4122-4
DO - 10.1007/s11432-023-4122-4
M3 - 文献综述
AN - SCOPUS:85211319237
SN - 1674-733X
VL - 67
JO - Science China Information Sciences
JF - Science China Information Sciences
IS - 12
M1 - 221301
ER -