## 2 Related Work
To solve the non-convex optimization problem, it proposes an asymptotically optimal solution using the Lagrange dual method and Hungarian algorithm. Simulation results demonstrate the asymptotic optimality of the algorithm and its superior performance compared to traditional Orthogonal Multiple Access (OMA) schemes and other existing resource allocation methods. The above methods mainly aim to maximize the throughput of a single user without considering network-level user experience in multi-frequency multi-cell scenarios. Ref. [27] addresses the problem of optimal CC selection and resource allocation in 5G networks. It considers delays associated with CC activation and deactivation, control channel overhead for switching CCs, and various service types with different 5G QoS Identifiers (5 QIs). It proposes QAP and QAP-ACDC methods to jointly allocate RBs and CCs in downlink transmission based on Channel Quality Index (CQI) and QoS requirements. QAP proportionally maximizes the average throughput of different service level users while satisfying user rate and delay requirements. QAP-ACDC minimizes activation/deactivation frequency, reducing control channel information for configuring CC transmission to a minimum. The proposed solutions outperform state-of-the-art approaches in meeting QoS requirements while reducing CC activation/deactivation by approximately 95.5%. Ref. [28] proposes a proportional fair (PF) scheduling-based Radio Resource Management (RRM) optimization solution for carrier aggregation. This method models RRM functionality as an ILP problem related to CA, modulation and coding scheme (MCS) allocation, secondary component carrier (SCC) selection, and RB allocation. By solving the joint optimization problem that maximizes the PF objective function, simulation results demonstrate the superiority of the algorithm in terms of fair throughput and average cell throughput. However, this method is primarily applicable to Long Term Evolution (LTE) systems and does not consider the impact of Multi-User (MU) pairing on RRM resource allocation in 5G multibeam scenarios. Ref. [21] addresses the resource allocation problem of joint carrier aggregation in cellular networks. It proposes a robust distributed optimal resource allocation method using a utility proportionally fair allocation strategy to achieve fairness in resource allocation among users. Simulation results demonstrate the convergence of the algorithm to optimal resource allocation. Ref. [24] proposes a novel scheduling algorithm based on user grouping, investigating throughput fairness among different users and load balancing among different CCs. The algorithm achieves better coverage and fairness, appropriately allocating resources to users at the cell edge. The above three approaches only consider the fairness of multi-user in resource allocation, without considering network-level user experience. In summary, it is essential to explore resource allocation schemes that aim to optimize network-level user experience while considering the coordinated allocation of time, frequency, and spatial domain resources to enhance network resource utilization and user experience.