## 1 Introduction  
The development of mobile communication technology has gradually evolved towards ultra-high data rates, ultra-low latency, and massive connectivity [1, 2, 3]. Enhancing user experience remains a common objective pursued by both the industry and academia [4, 5, 6]. However, the advancement of mobile networks also encounters unprecedented challenges. The scarcity of spectrum resources and the constraints of network deployment costs pose significant obstacles to achieving extensive network coverage and site deployment in the short term [7, 8]. Additionally, issues such as network duplication and high energy consumption impose higher requirements on the evolution of mobile communication technology [9, 10]. Therefore, enhancing spectrum resource utilization, reducing network construction and operation costs, and minimizing system energy consumption present crucial challenges for global operators [11, 12]. Mobile communication network co-construction and sharing among multiple telecom operators effectively alleviate the aforementioned issues [13]. Specifically, different operators can engage in various degrees of cooperation or sharing regarding telecommunications network infrastructure, network equipment, and spectrum resources [14]. The infrastructure mainly includes site-related facilities such as site locations, equipment rooms, towers, power supply equipment, etc. Network equipment comprises telecommunication network devices such as base stations, transmission equipment, etc. The aforementioned approach can significantly reduce the number of sites, prevent redundant infrastructure construction, lower system energy consumption, enhance resource utilization efficiency, and expeditiously establish leading network infrastructure capabilities. However, after co-construction and sharing, spectrum resources increase, and network scenarios and network systems become complex. The questions of how to make full use of the advantages of increased spectrum resources after sharing and achieve reasonable resource allocation are paramount to improving user experience. In addition, it is noted that spectrum sharing for unlicensed frequencies can also increase resource utilization, which is another important topic that needs to be explored [15; 16; 17]. To enhance user experience and spectrum efficiency, researchers explore techniques such as load balancing [18; 19] and carrier aggregation [20; 21] to achieve resource allocation and integration. Extensive literature has delved into allocation methods for time-domain, frequency-domain, and component carrier resources [22; 23; 24], providing optimization modeling and solutions. However, methods comprehensively considered the collaborative allocation of spatial resources have not been fully explored. With the development of 5G technology, massive Multiple-Input Multiple-Output (MIMO) has garnered widespread attention as a key technology for improving spectrum efficiency [25]. By fully exploring spatial resources through beamforming techniques, scarce spectrum resources are efficiently utilized, leading to a significant increase in network capacity. Consequently, the rational allocation of spatial resources has become a critical factor influencing communication performance. In the context of co-construction and sharing, inefficient coordination and scheduling among different frequency bands can lead to an imbalance in spatial loads; thus, users may cluster around certain beams in specific frequency bands, making it challenging to achieve multi-user pairing in the spatial domain and hindering the maximization of spectrum utilization. In terms of the optimization objective of the resource allocation methods, traditional attempts involve maximizing the throughput of a single user [26; 27], ensuring fairness of multi-user whole network throughput [21; 28]. However, these methods cannot ensure the maximization of multi-user experience at the network level.