## 3 Transmission Protocol Configuration and Resource Allocation Scheme
### Traffic Flow Resource Allocation Algorithm
#### 3.2.2 Maximization of Cell Utility
There are specific \(a\) and \(b\) so that the business flow in sets \(\Gamma_{j,1}\) and \(\Gamma_{j,3}\) is allocated to the transmission resource from the normal frame of the base station. The business flows in sets \(\Gamma_{j,2}\) and \(\Gamma_{j,A}\) obtain transport resources from the LP-ABS. Therefore, the cell utility maximization problem can be transformed into the following three steps. Firstly, when \(a\) and \(b\) are fixed, and the cell utility maximization problem can be decomposed into three subproblems of the same structure. Secondly, according to the properties of the subproblems obtained after decomposition, the P1 problem was solved. Finally, the a and b values of the maximization problem P1 and the optimal solution of problem P1 are traversed. First, consider step 1. Under the condition of fixing a and b, problem P1 can be decomposed into the following three independent subproblems:  
\[\text{Sub-problem 1}:\text{ maximize }\sum\limits_{i\in\Gamma_{j,1}}U_{i}^{d} \Big{(}\alpha_{i}^{n,d}c_{i}^{n,d}\Big{)} \sum\limits_{i\in\Gamma_{j,1}}\alpha_{i}^{n,d}\leq\Phi^{n,d}\] (26) subject to \[0\leq\alpha_{i}^{n,d},\forall i\in\Gamma_{j,1}\] \[\text{Sub-problem 2}:\text{ maximize }\sum\limits_{i\in\Gamma_{j,1}}U_{i}^{u} \big{(}\alpha_{i}^{n,u}c_{i}^{n,u}\big{)} \sum\limits_{i\in\Gamma_{j,1}}\alpha_{i}^{n,u}\big{\rangle}\] \[\text{subject to }0\leq\alpha_{i}^{n,u},\forall i\in\Gamma_{j,2}\] \[\text{Sub-problem 3}:\text{ maximize }\] \[\sum\limits_{i\in\Gamma_{j,2}}U_{i}^{d}\Big{(}\alpha_{i}^{l,d}c_{i}^{ l,d}\Big{)}+\sum\limits_{k\in\Gamma_{j,4}}U_{k}^{u}\Big{(}\alpha_{k}^{l,u}c_{k}^{ l,u}\Big{)}\] \[\text{subject to }0\leq\alpha_{i}^{l,d},\alpha_{k}^{l,d},\forall i\in \Gamma_{j,2},\forall i\in\Gamma_{j,4}\]  
The optimization variables involved in each sub-problem are \(\alpha^{n,d}=\Big{\{}\alpha_{i}^{n,d},i\in\Gamma_{j,1}\Big{\}}\), \(\alpha^{n,u}=\big{\{}\alpha_{i}^{n,u},i\in\Gamma_{j,3}\big{\}}\), \(\alpha^{l,u}=\big{\{}\alpha_{i}^{l,u},i\in\Gamma_{j,4}\Big{\}}\), and \(\alpha^{l,d}=\big{\{}\alpha_{i}^{l,d},i\in\Gamma_{j,3}\big{\}}\). To be corresponding to the above three sub-problems. Normal downlink frames of base station j are allocated to business flows in set \(\Gamma_{j,1}\) to maximize the utility sum of business flows, normal up-link frames of base station j are allocated to business flows inset \(\Gamma_{j,3}\) to maximize the utility sum of business flows, and LP-ABS of base station j are allocated to sets \(\Gamma_{j,2}\) and \(\Gamma_{j,4}\) to maximize the utility and sum of business flows. The decomposition process above is independent of a particular type of business flow type. Secondly, considering step 2, the three subproblems obtained after decomposition have the same structure, so we focus on solving subproblem 1. For the user business flows in the collection, some are soft QoS business flows, and others are best-effort service business flows. As shown in Figure 6, the business flows in \(\Gamma_{j,1}\) are divided into two sets, one is the set of soft QoS business flows, and the other is the set of best-effort service business flows. In \(\Gamma_{j,1}^{Q}\) and \(\Gamma_{j,4}^{B}\) traffic flows are sorted in descending order of spectral efficiency as shown in Figure 5.  
Sub-problem 1 can be expressed equivalently in the following form P2. It should be noted that P2 is formulated as an equivalent to facilitate solving sub-problem 1. A similar approach can be applied for solving the other two sub-problems, sub-problem 2 and sub-problem 3. \[\text{Maximize }\sum\limits_{i\in\Gamma_{j,1}^{Q}}U_{i}^{Q}\Big{(}\alpha_{i}^{ n,d}c_{i}^{n,d}\Big{)}+\sum\limits_{k\in\Gamma_{j,1}^{B}}U_{k}^{B}\Big{(} \alpha_{k}^{n,d}c_{k}^{n,d}\Big{)} \tag{27}\]  
\[\text{Sub-problem 1}:\text{ maximize }\sum\limits_{i\in\Gamma_{j,1}}U_{i}^{d} \Big{(}\alpha_{i}^{n,d}c_{i}^{n,d}\Big{)} \sum\limits_{i\in\Gamma_{j,1}}\alpha_{i}^{n,d}\leq\Phi^{n,d}\]0 The business flows in \(\Gamma_{j,1}\) are classified into business types and sorted in descending order according to \(c_{i}^{n,d}\).