Work

Energy efficient power allocation for ultra‐reliable and low‐latency communications via unsupervised learning

Year: 2023

Type: article

Abstract: Energy efficiency (EE) is an important indicator in ultra‐reliable and low‐latency communication (URLLC). Power allocation is considered as an effective method to achieve high EE in URLLC. However, since the EE optimization problem is non‐convex, it is difficult to obtain the analytical solution efficiently. Moreover, to ensure reliable and low‐latency communication within a finite blocklength, the Shannon formula becomes impractical for URLLC. Therefore, finite blocklength coding theory is used to meet the requirements of URLLC. In this paper, the EE problem of URLLC is formulated and the power allocation function is parameterized to be optimized through a deep neural network (DNN). The DNN is trained through the primal‐dual iterative algorithm offline in the unsupervised manner, and can be deployed online to achieve real time power allocation results. The numerical results show the effectiveness of the proposed method.

Source: IET Communications

DOI: 10.1049/cmu2.12605

Authors: Haitao Zhao, Bangning Xu, Hao Huang, Qin Wang, Chun Zhu, Guan Gui

Institution: Nanjing University of Posts and Telecommunications

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,420