A Stackelberg Game-Based Energy Trading Framework for RIS-Enhanced Wireless Powered MEC Networks With Multiple Access Points

This paper focuses on a reconfigurable intelligent surface (RIS) enhanced wireless powered mobile edge computing network. With the assistance of an RIS, multiple passive devices (PDs) first capture energy from the radiated signals of an energy station (ES), and then use a portion of the captured energy for uplink task offloading following a hybrid time- and frequency- division multiple access (HTFDMA) protocol and another portion for local task computing. Considering that the ES and the PDs are not affiliated with the same service suppliers, we propose a partial offloading (PO) scheme that formulates an energy trading framework with price incentives through a Stackelberg game to replenish energy for PO. Specifically, the PDs as a leader receive the benefits of total computation bits with the charging payments as losses by adjusting the charging price, bandwidth and time allocation, task offloading phase shifts, PDs’ transmit power, and central processing unit frequency. Meanwhile, the ES as a follower receives the benefits of charging payments with the charging costs as losses by adapting the ES’s transmit power, power transfer phase shifts, and ES’s beamforming. Following backward induction method, the follower-level decision problem is first handled via an alternating optimization algorithm with majorization minimization. Then, based on the obtained follower-level solutions, the leader-level decision problem is tackled by introducing auxiliary variables and applying Lagrangian dual and block coordinate descent algorithm. Simulations exhibit that invoking RIS results in utility gains for both the PDs and the ES, especially when there are more RIS elements, ES antennas, and PDs.