## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Power Allocation (PA)
#### 4.1.2 Energy Efficiency (EE)  
The EE in HetNet can be ensured by allocating bandwidth to users following their QoS requirements. Including EE in the allocation schemes for beyond 5G systems is essential, which calls for an increase in connections and services. In [88], we discussed the research that has been conducted on different approaches to resource distribution that consider EE as one of the optimization parameters. A NOMA-based heterogeneous network is deemed to support large-scale communications between different kinds of BSs and users in the 5G system. Data transmission using NOMA technology has been used to increase the access ratio and spectrum utilization. An NP-hard and nonconvex resource allocation optimization problem has been formulated to maximize energy efficiency. An iterative algorithm is proposed for joint power allocation and subchannel assignment in imperfect CSI, with maximum cross-layer interference, transmit power and quality-of-service requirements. Subsequently, the Lagrangian approach and the Dinkelbach method, both of which have guaranteed convergence and low computational complexity, can be used to solve optimization problems in a way that yields the best possible solution. The simulation results confirm the efficiency of the proposed algorithm and clearly demonstrate its superior performance in comparison to the baseline algorithms. Nevertheless, user association requires attention and consideration. In [89], the authors examined the power allocation issue of three-tier HetNets in a downlink scenario. The goal was to improve the capacity of indoor users in cases where the terrestrial network is either insufficient or out of service, such as during disaster situations or temporary hotspot regions, by deploying unmanned aerial vehicles (UAVs) as flying BSs and small cells (SCs). UAVs are energy-constrained devices with relatively limited flying time. Consequently, they necessitate a two-layer optimization scheme in which they first optimize the power consumption of each tier for improving EE under a minimum QoS requirement and then optimize the average hover time of UAVs. The Lagrange multipliers technique is used to initialize the problem, and then a sub-gradient method is used to achieve convergence, yielding a solution to the nonlinearly constrained optimization problem. The simulation results show that both the system EE and the flying time of UAVs are significantly improved in contrast to the situation where the maximum amount of power was given to users. However, interferences between UAVs are not considered. In [90], we present a joint approach of power allocation and user association with the goal of optimizing downlink energy efficiency while satisfying backhaul link constraints and ensuring the quality of service. In order to maximize the average cumulative reward, they suggest updating the learning policy with the novel parameterized deep Q-network (P-DQN). Furthermore, both the QoS of each UE and the limitations of the wireless backhaul are considered in the learning process. The simulation results demonstrate that the proposed P-DQN achieves better energy efficiency than the conventional methods, such as the deep Q-network (DQN) and distance-based association, while still meeting the QoS and backhaul capacity restrictions. In a HetNet, the average energy efficiency improvement of the proposed P-DQN may reach 77.6%, and in some cases, 140.6% compared to the traditional DQN and distance-based association approaches. However, the authors considered a limited number of users and small base stations. In [91], they examined the problem of maximizing energy efficiency throughout downlink transmissions in a two-tier heterogeneous network with multiple small cells by considering user association and power allocation simultaneously. First, they considered a system model that does not include the co-channel interference that occurs between small cells.