## 4 Resource Management in Heterogeneous Network (RM HetNet)
### Mode Selection (MS)
#### 4.3.1 Static  
When using a conventional method, the devices communicating with one another do not switch modes. We discussed static mode selection methods, wherein the communicating devices can select a mode of communication (ranging from cellular user and D2D). In [114], they considered the resource allocation problem in HetNets with multiple BSs, where an optimal solution can significantly enhance the system EE. The MS and UA are joined with each other in multiple BSs. They regarded the joint problem of the MS, UA, channel allocation, and power allocation in the multiple BSs networks, which remains an open problem. Each D2D pairs have its unique channel gain on its respective channels in a  
Figure 7: Mode selection in HetNet. complicated form. Constraints such as total transmission power and BS load balancing are considered. An optimization problem involving mixed-integer variables is presented, and it is not convex. They proposed a joint MS, UA, power allocation, and channel allocation algorithm based on the particle swarm optimization algorithm. The simulation results demonstrate the feasibility of the proposed algorithm, which can effectively increase system EE by satisfying total transmit power and load balancing. However, the mobility of users needs to be considered. In [115], the authors explore the MS problem for a HetNet in the uplink scenario to improve EE by focusing on QoS for D2D and CU. Each user transferred between regular cellular mode and D2D mode, presenting a Markov decision process problem. Deep deterministic policy gradient is a model-free deep reinforcement learning algorithm used by the authors to address the Markov decision process problem in continuous state and action space. The architecture of the suggested method consists of two networks: an actor-network and a critic network. Both the former and the latter use value function-based Q networks to analyze the performance of the actor-network, with the former employing a deterministic policy technique to construct deterministic activities for the agent. According to the simulation results, the proposed algorithm outperforms the benchmark algorithms in terms of convergence properties and EE. Nonetheless, the SE needs more attention and consideration. In [116], the authors discussed the possibility of using full-duplex radio in the access and backhaul links of SBSs. Specifically, they thought about the issue of how to distribute resources between SBSs that can use full duplex and users that can only use half-duplex. The solution divides the problem into two parts; user scheduling and MS, and power allocation to address the nonconvex of the underlying issue. They provided a decentralized and efficiently computed solution to each individual subproblem. According to the numerical results, the proposed algorithm effectively uses full duplex technology to boost the network sum rate compared to the network that only uses a half-duplex or full-duplex strategy. The research resource allocation method aimed to maximize SE by utilizing a mix of full-duplex and half-duplex modes of operation across access links and backhaul. In addition, the resource allocation algorithm distributed applied for full duplex HetNets without a centralized station. Nevertheless, the proposed scenario does not consider all possible transmission modes simultaneously. In [117], the authors explored the MS issue for uplink scenario two-way D2D communication enabled with full duplex to improve SE in a cellular network. Four types of communication are considered: full-duplex underlay, full-duplex overlay, half-duplex underlay, and half-duplex overlay. They optimized the SE for each mode to ensure that both cellular users and D2D can meet the minimum data rate requirements and the maximum power transmission. In the full-duplex underlay mode, the optimization model has converted into a programming problem; the difference between convex functions and the concave-convex procedure method can be applied to solve the problem efficiently.