Unsupervised Learning for Energy Efficient Power Allocation in Ultra-Reliable and Low-Latency Communications

Abstract

The ultra-reliable and low-latency communication (URLLC) is one of the critical scenarios in future communications. Energy efficiency (EE), as an important indicator in URLLC, has attracted more and more attention especially in the fields of industrial internet and automation control, etc. At present, power allocation is considered as an effective method to achieve high EE in URLLC. However, since the EE optimization problem in URLLC is usually formulated in the form of fractions with several statistical constraints, it is difficult to obtain the real time analytical solution. Moreover, the traditional expression based on Shannon formula is no longer applicable. In this paper, we formulate the EE problem of URLLC and adopt an unsupervised learning method to parameterize the power allocation function to be optimized through a deep neural network (DNN). The DNN is trained through the primal-dual iterative algorithm offline, and can be deployed online to achieve real time power allocation results. The numerical results show the effectiveness of the proposed method.