Energy Minimization of RIS-Assisted Cooperative UAV-USV MEC Network

Unmanned surface vehicles (USVs) are becoming increasingly significant in fulfilling integrated sensing, computing and communication with the emergence of bidirectional computation tasks. However, QoS provisioning is still challenging since USVs are restricted with limited on-board resources and direct links between them and shore-based terrestrial base stations (TBSs) are frequently blocked. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted cooperative unmanned aerial vehicle (UAV)-USV mobile edge computing (MEC) network architecture, where RIS-mounted tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are collaboratively utilized to serve USVs. RUAVs energy minimization is formulated by jointly considering TUAV hovering altitude, RIS phase shift vector, RUAV service selection indicator and RUAVs turning points. A heuristic solution is proposed to tackle the formulated problem, where the original problem is first decoupled into three subproblems, e.g., the joint optimization of RIS phase shift vector and TUAV hovering altitude subproblem, RUAVs service selection indicators subproblem and RUAVs turning points subproblem, each of which is solved by the proposed modified alternative direction method of multiplier (ADMM) algorithm, the proposed enhanced simulated annealing (ESA) algorithm and the proposed successive convex approximation (SCA)-based algorithm. In this way, the challenging problem can be efficiently solved iteratively. The results show that the proposed solution can decrease RUAVs energy consumption by nearly 29% compared to numerous selected advanced algorithms. Moreover, the performance of the proposed solution regarding typical penalty coefficients and number of RIS reflecting elements is investigated.