TY - GEN
T1 - Deep Learning Based Power Allocation in 6G URLLC for Jointly Optimizing Latency and Reliability
AU - Murshed, Rafid Umayer
AU - Horaira Hridhon, Abu
AU - Hossain, Md Farhad
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Supporting ultra-reliable and low-latency communications (URLLC) is one of the primary goals for the sixth-generation (6G) cellular networks. Minimizing latency while maintaining high reliability is the central concept of URLLC. In this paper, we propose a deep learning (DL) based power allocation mechanism for jointly optimizing latency and reliability in 6G URLLC. Existing iterative algorithm-based solutions provide valuable insights, but there are significant challenges in implementing them in a real world system. The central theme of our paper revolves around merging theoretical network models and channel information in analyzing latency and reliability and training deep neural networks (DNNs) for satisfying the requirements of URLLC. This paper demonstrates a distinct approach on how to apply data-driven supervised DL in URLLC. The performance of the proposed system is evaluated through extensive simulations. Scrupulous comparison of results with those of the weighted MMSE (WMMSE) based systems validate that the proposed DNN models reduce latency drastically and simultaneously ensure service reliability.
AB - Supporting ultra-reliable and low-latency communications (URLLC) is one of the primary goals for the sixth-generation (6G) cellular networks. Minimizing latency while maintaining high reliability is the central concept of URLLC. In this paper, we propose a deep learning (DL) based power allocation mechanism for jointly optimizing latency and reliability in 6G URLLC. Existing iterative algorithm-based solutions provide valuable insights, but there are significant challenges in implementing them in a real world system. The central theme of our paper revolves around merging theoretical network models and channel information in analyzing latency and reliability and training deep neural networks (DNNs) for satisfying the requirements of URLLC. This paper demonstrates a distinct approach on how to apply data-driven supervised DL in URLLC. The performance of the proposed system is evaluated through extensive simulations. Scrupulous comparison of results with those of the weighted MMSE (WMMSE) based systems validate that the proposed DNN models reduce latency drastically and simultaneously ensure service reliability.
KW - 6G
KW - DNN
KW - latency
KW - power allocation
KW - reliability
KW - URLLC
KW - WMMSE
UR - http://www.scopus.com/inward/record.url?scp=85127720621&partnerID=8YFLogxK
UR - https://www.kuet.ac.bd/eict2021/
UR - https://www.kuet.ac.bd/eict2021/?page_id=63
U2 - 10.1109/EICT54103.2021.9733558
DO - 10.1109/EICT54103.2021.9733558
M3 - Conference contribution
AN - SCOPUS:85127720621
T3 - 2021 5th International Conference on Electrical Information and Communication Technology, EICT 2021
SP - 1
EP - 6
BT - 2021 5th International Conference on Electrical Information and Communication Technology, EICT 2021
A2 - Hasan, K. M. Azharul
A2 - Shaifur Rahman, Mohammad
A2 - Chandra Shill, Pintu
A2 - Kumar Choudhury, Pallab
A2 - Kumar Roy, Naruttam
A2 - Mollah, Md. Nurunnabi
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 5th International Conference on Electrical Information and Communication Technology, EICT 2021
Y2 - 17 December 2021 through 19 December 2021
ER -
