### Related Work  
Effective spectrum allocation and power control enable both primary links (PLs) and secondary links (SLs) to operate at a high-performance level. Various mathematical frameworks and performance metrics have been applied to develop network-centric and user-centric schemes [11]. A network-centric scheme requires a central controller and typically adopts mathematical frameworks such as convex optimization [12; 13] and fractional programming (FP) [14; 15; 16]. In contrast, a user-centric scheme is implemented in a distributed manner and mainly relies on frameworks such as contract theory [17; 18] and game theory [19; 20; 21]. For an extended period, beginning with the early days of CR, the primary emphasis in spectrum allocation and power control has been optimizing overall system throughput or achievable user rates. For example, the researchers of [12] introduced a cooperative D2D communication framework where cellular and D2D users can switch between the underlay, overlay, or cooperative spectrum-sharing mode. Based on this framework, they proposed an adaptive mode selection and spectrum allocation scheme to maximize the D2D users' overall throughput. The simulation results showed that the proposed framework and scheme are effective in various scenarios. The study in [13] proposed an overlay spectrum-sharing framework using three technologies: cooperative relay, full-duplex (FD), and NOMA. Additionally, it integrated simultaneous wireless information and power transfer (SWIPT) techniques. Under this framework, they derived the throughput in tight closed-form approximations for the primary and secondary networks. After that, a rapid convergent iterative algorithm was proposed to optimize the system throughput. In [17], the researchers examined cooperative spectrum sharing with incomplete information, i.e., SUs' wireless channel characteristics are unknown to a PU. Using contract theory, they modeled the interaction between the PU and SUs as a labor market. Then, they studied the optimal contract design and proposed an algorithm in which the PU aims to maximize the average transmission rate. At the same time, each SU aims to choose relay power and time allocation to maximize its normalized payoff. The authors of [18] further extended the research of [17] to the scenario of OFDMA-assisted cognitive Internet of Things (IoT) networks. Based on matching game theory, the authors in [19] developed a two-timescale wireless resource allocation scheme for cooperative overlay D2D communication systems. The scheme operates at two timescales: (1) long-term matching between cellular and D2D links, and (2) short-term transmission time allocation for matched pairs. After that, they proposed a distributed pairing algorithm to optimize the expected data rate of the D2D link while guaranteeing the QoS of the cellular link. In [20], the researchers studied the performance of overlay D2D communication links based on carrier-sense multiple access (CSMA) protocols. Each D2D user aims to achieve its data rate requirement while maximizing its payoff. A Stackelberg game was established, where the base station (BS) acts as a leader in regulating the D2D users' payoff. In [21], the researchers designed a group-based framework without channel quality indicators (CQIs) that realizes a pricing-based Stackelberg game for mode selection and spectrum allocation feedback. They proposed an incentive-compatible pricing strategy to optimize the achievable rate of potential D2D links while effectively preventing selfish D2D users from negatively impacting overall system performance. Green communication is now a cornerstone of wireless system design, driven by a growing global emphasis on sustainable development. Consequently, energy efficiency (EE)--the ratio between global achievable throughput and power consumption--has become a primary metric for evaluating system cost-effectiveness [22]. For instance, the authors in [14] addressed the resource allocation problem for one PL and SL pair in the overlay or underlay mode.