Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications

This article studies an intelligent reflecting surface (IRS)-aided communication system under the time-varying channels and stochastic data arrivals. In this system, we jointly optimize the phase-shift coefficient and the transmit power in sequential time slots to maximize the long-term energy consumption for all mobile devices while ensuring queue stability. Due to the dynamic environment, it is challenging to ensure queue stability. In addition, making real-time decisions in each short time slot also needs to be considered. To this end, we propose a method (called LETO) that combines Lyapunov optimization with evolutionary transfer optimization (ETO) to solve the above optimization problem. LETO first adopts Lyapunov optimization to decouple the long-term stochastic optimization problem into deterministic optimization problems in sequential time slots. As a result, it can ensure queue stability since the deterministic optimization problem in each time slot does not involve future information. After that, LETO develops an evolutionary transfer method to solve the optimization problem in each time slot. Specifically, we first define a metric to identify the optimization problems in past time slots similar to that in the current time slot, and then transfer their optimal solutions to construct a high-quality initial population in the current time slot. Since ETO effectively accelerates the search, we can make real-time decisions in each short time slot. Experimental studies verify the effectiveness of LETO by comparison with other algorithms.

Automated summary

This article studies an intelligent reflecting surface (IRS)-aided communication system under the time-varying channels and stochastic data arrivals. It proposes a method called LETO that combines Lyapunov optimization with evolutionary transfer optimization (ETO) to solve the optimization problem. LETO decouples the long-term optimization problem into deterministic optimization problems in short time slots, ensuring queue stability, and then solves the optimization problem in each time slot using the evolutionary transfer method to achieve real-time decisions.