EE-SE Trade-Off in Deep Learning-Based URLLC Cell-Free Massive MIMO Systems

In smart Internet of Things (IoT) factories with dense terminal access, the communication scenarios and requirements differ significantly between ground terminals (GTs) and aerial terminals (ATs). Consequently, their demands for the two key performance indicators under URLLC constraints - Energy Efficiency (EE) and Spectral Efficiency (SE) - also exhibit distinct characteristics and variations. To achieve differentiated trade-offs between EE and SE for these two types of terminals, this letter proposes an unsupervised power allocation network (U-ESTPANet) and designs a differentiated trade-off mechanism. Compared with traditional iterative algorithms, it exhibits superior real-time performance and adaptability to complex scenarios. In contrast to fully supervised deep neural networks, this network can enhance the stability and real-time performance of outputs without the need for labeled training samples. Simulation results demonstrate that the proposed unsupervised method can effectively optimize the spectral efficiency and energy efficiency of the system.