TY - GEN
T1 - Puncturing-Based Resource Allocation for URLLC and eMBB services via Unsupervised Deep Learning
AU - Shi, Bing
AU - Zheng, Fu Chun
AU - She, Changyang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this work, we establish a puncturing-based resource allocation framework to enable the coexistence of the Ultra-Reliable and Low-Latency Communications (URLLC) and the enhanced Mobile Broad Band (eMBB) services for the next generation of mobile communications networks. Since the optimal resource allocation depends on channel gains, we aim to find an optimal mapping from the channel gains to the bandwidth and transmit power allocated to both types of services. To solve the problem, we use a deep neural network to represent the mapping and train the parameters using an unsupervised learning method, where the Lagrangian function serves as the loss function. Simulation results show that, compared with the optimal solution obtained from exhaustive search, our proposed method can achieve a learning precision of more than 98.71%. When compared with the existing random puncturing scheme, our scheme can realize an average long-term data rate gain for eMBB of up to 351.1% under URLLC packet size of 512 bits.
AB - In this work, we establish a puncturing-based resource allocation framework to enable the coexistence of the Ultra-Reliable and Low-Latency Communications (URLLC) and the enhanced Mobile Broad Band (eMBB) services for the next generation of mobile communications networks. Since the optimal resource allocation depends on channel gains, we aim to find an optimal mapping from the channel gains to the bandwidth and transmit power allocated to both types of services. To solve the problem, we use a deep neural network to represent the mapping and train the parameters using an unsupervised learning method, where the Lagrangian function serves as the loss function. Simulation results show that, compared with the optimal solution obtained from exhaustive search, our proposed method can achieve a learning precision of more than 98.71%. When compared with the existing random puncturing scheme, our scheme can realize an average long-term data rate gain for eMBB of up to 351.1% under URLLC packet size of 512 bits.
KW - Lagrangian function
KW - URLLC
KW - eMBB
KW - functional optimization
KW - unsupervised deep learning
UR - https://www.scopus.com/pages/publications/85177842078
U2 - 10.1109/ICCWorkshops57953.2023.10283679
DO - 10.1109/ICCWorkshops57953.2023.10283679
M3 - 会议稿件
AN - SCOPUS:85177842078
T3 - 2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023
SP - 1729
EP - 1734
BT - 2023 IEEE International Conference on Communications Workshops
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Communications Workshops, ICC Workshops 2023
Y2 - 28 May 2023 through 1 June 2023
ER -