## 4 Resource Management in Heterogeneous Network (RM HetNet)
### User Association (UA)
#### 4.2.2 Data Rate
Since each user has its own unique set of communications characteristics and types, the optimum power is found by solving for the optimality of each BS's utility function while guaranteeing the utility function optimality for every associated user. After the contract-theoretic power distribution is completed, users may provide their subjective assessment of their actual data rate. Numerical results are presented to show how the proposed user association and power allocation architecture works and what advantages it provides to users in terms of achievable data rate, power consumption, and user fairness. Nonetheless, the level of interference will rise in direct proportion to the number of devices; hence the network performance will degrade. A joint optimization framework for UA of 5G HetNet was introduced in [111]. In order to achieve their goals, they modeled the user association approach as a mixed integer linear program that simultaneously optimized bandwidth allocation and UA across the network. They studied the performance of the UA by analyzing multiple active application profiles. These application profiles include massive machine-type communication (mMTC) and enhanced mobile broadband (eMBB). Moreover, they offer a novel investigation of the multiple dual connectivity modes, in which the user can be linked to either a single Macro Cell (MC) and a potential SC or with any two preferable candidates BSs. Dual connectivity constraints are defined, allowing users to choose between two BSs with a maximum data rate. They demonstrate that, compared to the baseline scenario, which is a traditional UA solution, the UA optimal solutions boost the performance of various HetNets in terms of total system throughput and fairness. In terms of the whole network throughput metric, the comparison between dual connectivity and single association revealed significant performance gains for the latter. However, power consumption is not considered. To address the problem of UA in mmWave HetNet, the authors proposed a novel machine learning-based strategy [112]. The UA problem is formulated as a multi-label problem to accommodate multiple connections. The problem is decomposed into a set of single-label problems if efficient multi-label algorithms are used. The UA scenario is formulated with the assistance of a graphical model that is characterized by both the physical locations of users and the structure of the mmWave HetNet. The authors proposed a novel feature extraction approach to collect the best information about users, similar to the inference procedures used in the graphical theory approach. In a supervised manner, the parameters of the classifiers and the means of feature extraction for each simple-label problem are jointly learned to be optimized. The research results demonstrated that the proposed approach could produce satisfactory results with a relatively small number of iterations and without the utilization of CSI. Nonetheless, the work relies on optimal UA schemes of training samples, which take time to collect. In [113], the authors developed a user-centric association algorithm based on data rate for HetNets in a downlink multi-antenna scenario. The algorithm improves for the maximum possible function of the predicted long-term rate, which changes depending on the multiantenna transmission technique used by the data communication system. Existing cellular networks need simple changes to their cell selection technique to use the proposed algorithm, which employs an overhead of the same order as max-power association. The user needs to assess the strength of the signals received from nearby BSs, run those assessments through a processing unit to estimate the anticipated rate from each BS, and then connect to the BS with the highest predicted value. As shown by the results of the simulations, the suggested approach, in contrast to the usual max power association, takes advantage of the multi-antenna transmission strategy to enhance the average user rate.