TY - JOUR
T1 - AI/ML-aided capacity maximization strategies for URLLC in 5G/6G wireless systems
T2 - A survey
AU - Shaik, Razeena Begum
AU - Nagaradjane, Prabagarane
AU - Ioannou, Iacovos
AU - Sittakul, Vitawat
AU - Vasiliou, Vasos
AU - Pitsillides, Andreas
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/7
Y1 - 2024/7
N2 - Ultra-reliable low-latency communication (URLLC) refers to cellular applications in fifth and sixth-generation (5G/6G) networks with specific latency, reliability, and availability demands. Most of the reported 5G/6G applications are focused on URLLC, which necessitates a latency of milliseconds and very high dependability for transmitted data. These systems encounter several obstacles since conventional networks cannot fulfill such demands. According to the standards of the 3rd generation partnership project URLLC, it is predicted that the dependability of a single transmission of a 32-byte packet would be no less than 99.999%, and the latency will not exceed 1 ms. The exceptional degree of dependability and minimal delay will result in the emergence of many novel applications, including smart grids, industrial automation, and intelligent transport systems. This review discusses several methods for maximizing capacity in URLLC, focusing on resource allocation strategies, multi-access approaches, and beamforming with massive MIMO. Furthermore, it explores the requirements and constraints of URLLC and the role of AI/ML in URLLC. Finally, this study examines possible future research areas and obstacles to achieving the URLLC standards.
AB - Ultra-reliable low-latency communication (URLLC) refers to cellular applications in fifth and sixth-generation (5G/6G) networks with specific latency, reliability, and availability demands. Most of the reported 5G/6G applications are focused on URLLC, which necessitates a latency of milliseconds and very high dependability for transmitted data. These systems encounter several obstacles since conventional networks cannot fulfill such demands. According to the standards of the 3rd generation partnership project URLLC, it is predicted that the dependability of a single transmission of a 32-byte packet would be no less than 99.999%, and the latency will not exceed 1 ms. The exceptional degree of dependability and minimal delay will result in the emergence of many novel applications, including smart grids, industrial automation, and intelligent transport systems. This review discusses several methods for maximizing capacity in URLLC, focusing on resource allocation strategies, multi-access approaches, and beamforming with massive MIMO. Furthermore, it explores the requirements and constraints of URLLC and the role of AI/ML in URLLC. Finally, this study examines possible future research areas and obstacles to achieving the URLLC standards.
KW - Artificial intelligence
KW - Beamforming
KW - Capacity maximization
KW - Machine learning
KW - Resource allocation
KW - URLLC
UR - http://www.scopus.com/inward/record.url?scp=85193695275&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2024.110506
DO - 10.1016/j.comnet.2024.110506
M3 - Short survey
AN - SCOPUS:85193695275
SN - 1389-1286
VL - 249
JO - Computer Networks
JF - Computer Networks
M1 - 110506
ER -