2025 Volume E108.A Issue 2 Pages 129-139
IoT devices, which possess limited battery capacity and computing capabilities, are unable to meet many applications’ demands. The integration of wireless power transfer and edge computing has emerged as a promising solution for this problem. Nevertheless, efficiently making offloading decisions and allocating resources pose significant challenges, particularly in the scenarios of multiple access points (APs). This paper focuses on optimizing the sum computation rate (SCR) in a wireless powered network having multiple APs. The devices work in binary offloading, operating under frequency-division multiple access (FDMA) and time-division multiple access (TDMA), respectively. To efficiently address these two mixed-integer nonlinear programming problems, a deep reinforcement learning based algorithm is employed to determine the near-optimal offloading decisions. Additionally, under the given offloading decision, we present an algorithm using the golden section search for FDMA to obtain the subsequent optimal time allocation, and apply convex optimization algorithm to obtain the optimal time allocation for TDMA. Our algorithms achieve over 95 percent of the maximum SCR with low complexity. In comparison to the baseline algorithms, our proposed algorithms exhibit advantages in terms of convergence speed and attained SCR.
