## 2 Related Work  
Resource management and computation offloading optimization problems usually involve non-convex optimization with multi-objective coupling, such as power control, user association and arithmetic allocation. To address this problem, several classes of traditional algorithms are applied by researchers. For instance, the decomposition optimization algorithm is used to split the large-scale non-convex programming problem into easily solvable subproblems. In [9], the authors consider a binary offloading policy in a wireless-powered multi-user MEC system and propose a joint ADMM-based decomposition algorithm to tackle the combinatorial coupling between offloading decisions and time allocation. In order to overcome the doubly near-far effect, [PERSON] et al. [10] address the \"doubly near-far\" problem in two-device WPT-powered MEC via a two-phase Lagrangian and bisection scheme that yields closed-form offloading and timing rules, thus minimizing the AP energy under latency constraints. [PERSON] et al. [11] formulate an MINLP for energy-efficient coverage in 6 G space-air-sea networks and apply [PERSON] decomposition, coupled with [PERSON]'s fractional-programming and ADMM, to achieve near-optimal energy efficiency. Similarly, in [12], the authors study joint task computation and time allocation for blue data in 6 G space-air-sea non-terrestrial networks, formulating a mixed-integer linear program (MILP) for weighted sum-rate maximization across high- and low-antenna gain maritime users. They apply [PERSON] decomposition to separate binary offloading decisions from continuous time allocation and use primal decomposition to handle coupling constraints, achieving near-optimal performance with polynomial time complexity. To address the multi-party conflict of interest and pricing problem, many researchers adopt the Stackelberg game and two-way auction model. [PERSON] et al. [13] study radio resource allocation in UAV-supported vehicular networks employing NOMA, formulating an MINLP to maximize I2V users' data rates while minimizing V2V interference under SIC constraints. They propose two low-complexity near-optimal methods: first, a DC (difference-of-concave) approximation-based iterative algorithm for joint power and RB allocation; and second, a Stackelberg game framework in which leaders (I2V users) and followers (V2V users) optimize the transmit power via closed-form KKT solutions, yielding fast convergence and scalability. In [14], the authors focus on joint computation offloading and service pricing in vehicular edge computing (VEC), modeling the interactions among the requesting vehicle, edge server, and cloud as a Stackelberg game, and transform the game into a convex optimization via backward induction, prove the existence and uniqueness of the Nash equilibrium for offloading decisions, and propose a genetic algorithm-based search to derive optimal pricing strategies for edge and cloud servers, demonstrating improved delay-cost trade-offs. [PERSON] et al. [15] study an incentive-oriented two-tier task offloading scheme in marine edge computing networks, introducing a hybrid game-theoretic approach spanning an underwater acoustic tier and an RF tier. They formulate the underwater offloading between the UWS, UUV, and SN as a Stackelberg game and the aerial offloading between the SN and UAV as a double auction, deriving equilibrium offloading and pricing/bidding strategies that maximize each party's utility. In order to realize real-time adaptive scheduling in the face of time-varying networks with unknown cost functions, [PERSON] et al.