## 4 Resource Management in Heterogeneous Network (RM HetNet)
### User Association (UA)
#### 4.2.1 Signal to Interference Noise Ratio (SINR)
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Characteristics** & **Issue** & **Methodologies** & **Advantages** & **Limitations/ Future Work** & **Ref.** \\ \hline \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } & User association in fog radio access HetNet & Joint UA and channel allocation scheme. & Effectively addressed statistical delay provisioning issues in HetNet. & EE needs more attention and consideration. & [107] \\ \cline{2-6}  & Enhance the system EE and reduce the total interference & Lagrange multiplier approach & Improved the EE and utilized the total interference & The tradeoff between EE and SE is not considered. & [108] \\ \cline{2-6}  & Find a more optimal user association & Non-cooperative game theory & Effective in ensuring dynamic user association, a higher total rate for all uses. & EE and frequency allocation is needed more attention and consideration. & [109] \\ \cline{2-6}  & Associate users with BSs in an iterative and distributed way & Reinforcement Learning based technique & The achievable data rate, power consumption, and users’ fairness. & The interference will rise with increasing the number of devices; hence the performance will degrade. & [110] \\ \cline{2-6}  & User association in HetNets & User association approach & Boost the network throughput and system fairness in various HetNets. & Power consumption is not considered. & [111] \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._the authors considered both UA with HetNets consisting of large sizes, MBS, and relays operating in the microwave band as well as small sizes, SBS, and D2 Ds operating in the mmWave band. UA optimization problems are formulated to compare the performance of user association schemes based on downlink uplink decoupled access with the performance of traditional downlink uplink coupled access in HetNets with respect to accommodating users and SE in mmWave and microwave bands. The formulated problems are NP-hard, meaning that the best possible algorithm is required to find a solution. In order to guarantee that users always associate with the BS in both the downlink and uplink, this access method uses the SINR as a criterion in the downlink. The simulation results showed that compared to downlink uplink coupled access, downlink uplink decoupled access is superior in terms of UA, data rate, and SE in the microwave and mmWave bands. In addition, simulation results gave an overview of the HetNets, showing that most users promote association with BSs operating in the untapped mmWave band rather than the scarce microwave band to meet higher data rate demands in the beyond 5G (B5G) HetNets. However, EE is needed more attention and consideration. In [103], the challenge of user association in HetNets millimeter-wave that employ different radio access technologies is investigated. They introduced a novel, distributed method based on deep reinforcement learning to manage network dynamics. In the suggested multi-agent reinforcement learning algorithm, agent decisions are made on the basis of partial and local observations. This reduces the signaling overhead and computational complexity in comparison to centralized approaches. The association of a specific UE to the BS is dependent on the SINR between them. Moreover, they proposed a robust solution to massive changes in the statistical dynamics of the environment by incorporating a distillation procedure. The proposed algorithm outperforms the exhaustive search algorithm in terms of network performance. Furthermore, the algorithm achieved a significant sum rate when considering the dynamic traffic. However, the user's mobility within a dynamic environment is not considered. The coexistence of high-power MBS and low-power SBS is challenging to guarantee the efficient utilization of radio resources for the benefit of users with QoS needs [104].