## 1 Introduction
In addition, the objective of load balancing methods has traditionally been focused on achieving equilibrium involving user distribution or physical resource block (PRB) utilization [29]. These approaches also fall short of guaranteeing an enhancement in network-level user experience. The current optimization method of resource allocation in the industry predominantly relies on the best-effort model based on expert experience. For instance, resource allocation is achieved through algorithmic design and parameter tuning based on manual experience. This large-scale, long-term, and single-target method severely restricts the full exploitation of network capabilities involving complex scenarios with multiple entities and parameters at the cell and system layer. Therefore, it is necessary to explore a network-level optimization theory that serves as a top-level framework guiding resource allocation for shared resources from an overarching perspective. This study aims to investigate resource allocation methods that jointly consider time-domain, frequency-domain, and spatial-domain aspects to achieve optimal network-level user experience of multi-users under multi-carrier and multi-beam in the co-construction and sharing scenarios of 5G and future mobile communication networks. In situations where users are covered by multiple frequency bands simultaneously within a scheduling period, frequency resources and beam resources of a certain frequency band are reasonably allocated to the user. This allows us to comprehensively consider the communication performance of all users to obtain a better user experience. This paper proposes a time-frequency-space resource allocation method with the optimization goal of network-level user experience maximization. To achieve this, the Shannon theorem serves as the foundation, considering the multi-user and multi-carrierconditions in the scheduling period, the channel bandwidth is modeled by using the Resource Block Group (RBG) granularity resources and spatial-domain beam resources, and the signal-to-noise ratio (SNR) is modeled by using the spectral efficiency, and the channel capacity can be obtained through calculation. By maximizing channel capacity, the network-level user experience is optimized. Moreover, this paper innovatively proposes an air interface information prediction scheme based on smart grid technology and constructs an optimal parameter set for optimal modeling. By incorporating intelligent technologies to construct a virtual grid fingerprint map, the system can predict users' Received Signal Reference Power (RSRP), beam ID, and spectral efficiency on different carriers. This information serves as input to the optimization algorithm. Through the optimization algorithm, the optimal time-frequency-space resource allocation scheme can be obtained. The main contributions can be summarized as follows:  
* We propose a resource allocation scheme based on a joint optimization problem of time-frequency-space resources with the optimization goal of maximizing network-level user experience to enhance overall network performance and user satisfaction;
* We provide a smart grid technology to predictive air interface information, including RSRP, beam ID, and spectral efficiency, which are used as input for the optimization algorithm;
* The feasibility and superiority of the proposed method are verified through simulation platforms with extensive experiments. The main structure of this paper is as follows: Section 2 provides the related work; Section 3 analyzes the challenges faced in resource allocation under the co-construction and sharing network; Section 4 proposes a time-frequency-spatial resource allocation method for maximizing user experience in network-level; Section 5 validates the performance of the proposed method; finally, the paper concludes with a comprehensive summary.