## 2 Related Work  
In this section, we analyze methods related to resource allocation. We primarily compare the existing methods with our proposed approach from two perspectives: optimization dimensions and optimization objectives of resource allocation schemes. Detailed comparisons can be found in Table 1.  
Firstly, from the perspective of optimization dimensions of resource allocation schemes, current methods mainly focus on the allocation of resources in the time and frequency domains, with little consideration for the joint allocation of spatial domain resources. Ref. [22] addresses resource allocation in wireless communication systems with multiple component carriers (CC) aggregation to achieve peak data rates of up to 1 Gbps for low mobility and 100 Mbps for high mobility targets in IMT-Advanced. It proposes an L2  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Optimization Objective**} & \multicolumn{3}{c}{**Optimization Dimension**} \\  & & **Time-Domain** & **Frequency-Domain** & **Spatial-Domain** \\ \hline
[18] & load balancing & \(\surd\) & \(\surd\) & \(\times\) \\
[19] & load balancing & \(\surd\) & \(\surd\) & \(\times\) \\
[22] & maximizing throughput of a single user & \(\surd\) & \(\surd\) & \(\times\) \\
[23] & maximizing throughput of a single user & \(\surd\) &  
carrier scheduling algorithm, aiming to allocate CCs to each user and reuse multiple users in each CC. It improved scheduling fairness and coverage performance by independently scheduling each carrier. However, it only adjusts scheduling priorities in the time domain and does not consider spatial resources. In the context of carrier aggregation (CA), CCs can be the activation and deactivation according to various factors such as user energy consumption and Quality of Service (QoS) requirements. Ref. [23] models resource allocation as a centralized multi-objective optimal CC management problem and formulates it as a stochastic game optimization problem. The paper proposes a multi-agent Double Deep Q-Network method to solve it, demonstrating a substantial reduction in User Equipment (UE) power consumption (approximately 20%) compared to full CC activation schemes. However, it also does not consider spatial domain beamforming resources. Ref. [21] focuses on optimizing resource allocation in scenarios involving CA. It models CC selection and resource block (RB) allocation as an Integer Linear Programming (ILP) problem and provides an optimization solution. It allocates the optimal rate to each user within the effective range of resource allocation algorithms under high and low traffic conditions. However, the algorithm models only time-frequency domain resource allocation and does not consider spatial beamforming resources extensively used in 5G. The above three methods primarily consider the allocation of resources in the time and frequency domains, lacking joint consideration of spatial domain resource allocation, limiting user experience, and resource utilization. From the perspective of optimization objectives of resource allocation schemes, current resource allocation schemes primarily optimize for load balancing [18; 19], single-user throughput, and fairness of multi-user whole network throughput, with minimal consideration for network-level user experience. In terms of the subchannel and power allocation in multi-carrier Non-Orthogonal Multiple Access (NOMA) communication systems, Ref. [26] designs a fast dynamic resource allocation (FDRA) algorithm to maximize system sum rate efficiently by quickly identifying interfering nodes with poor channel conditions, thus reducing search complexity. By jointly optimizing subcarrier and power resource allocation, it maximizes system performance while guaranteeing user QoS. Ref. [20] jointly optimized subcarrier allocation, subcarrier pairing, relay selection, and power allocation in multi-carrier cooperative relay NOMA systems.