## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Power Allocation (PA)
#### 4.1.3 Spectrum Efficiency (SE)  
The SE is measured in bits per second per hertz and reflects the maximum number of services that can be derived from a particular spectrum. This measurement is helpful for users because it allows them to make informed decisions about who should have access to a given frequency range. Resource allocation techniques that include SE as a performance metric are discussed in this section. In [94], the authors considered a power control in downlink ultra-dense HetNet with different types of BSs. They suggest a reinforcement learning power allocation algorithm based on graph signal processing. At first, they developed a realistic model that operates within an ultra-dense HetNet environment, which includes the instantaneous rate and channel mode. After that, they used a graph signal processing tool to analyze network interference, and they obtained the interference analysis results to determine the optimal power allocation of reinforcement learning. According to the simulation results, the proposed algorithm enables more precise power allocation based on the interference results from a graph signal processing analysis. Therefore, the proposed algorithm can achieve better SE and higher throughput than the three baseline algorithms while simultaneously improving the throughput of edge UEs and guaranteeing a peak rate. However, imperfect channel state information impact requires attention and consideration. For HetNets utilizing non-orthogonal multiple access (NOMA), [95] explored the tradeoff between SE and EE concerning interference management and power allocation. The tradeoff between SE and EE is modeled as a non-convex multi-objective problem that is constrained by maximum power and QoS requirements. Combining a weighted sum strategy with the hypograph transformation can simplify the multi-objective problem into a convex single-objective problem. The single-objective problem can be solved in two different steps. First, they proposed a power allocation strategy for EE and SE in NOMA HetNets that is based on a non-cooperative game. To maximize the weighted sum of SE and EE, the macro base stations (MBSs) and small base stations (SBSs) compete in the proposed non-cooperative (NC) game by optimizing their transmit powers. Second, a closed-form formula is suggested to control the power allocation distributed to users while simultaneously considering the QoS and SIC constraints. The simulation results show that the tradeoff between SE and EE can be enhanced by appropriately selecting the EE threshold value and the balancing parameters. Nevertheless, the model that has been proposed is constrained by a limited number of users. In [96], the authors investigated an energy-efficient resource allocation of two-tier multi-carrier MIMO HetNet in a downlink scenario. The goal of this paper is to achieve a higher EE and SE. A massive MIMO transceiver design and antenna selection for macro and small cells are proposed to minimize power consumption. Furthermore, they proposed a joint resource block (RB) allocation algorithm, antenna selection, and a power optimization algorithm to enhance the EE and SE under constraints of minimal user rates and maximal capacity limits. The Dinkelbach technique was used to solve the power allocation problem successfully. The simulation result shows that the proposed algorithms guarantee higher EE and SE than the best-known benchmark algorithms while still being significantly easier to implement than an exhaustive search. Nonetheless, user mobility and imperfect CSI need to be considered. In [97], they focused on the joint stochastic optimization problem of maximizing SE, EE, and queue length in downlink 5G HetNet. A Lyapunov optimization model is proposed and considers all aspects of the joint problem that simultaneously involves power control, resource allocation, and user association in complex networks. Particularly, this work focused on the NP-hard multiple resource allocation problem.