# QoS-Aware Downlink Traffic Scheduling for Cellular Networks with Dual Connectivity
## 4 Proposed QoS Downlink Data Dispatching (QDD) Method  
In this section, we introduce the proposed QDD method to dispatch downlink data flows between the MeNB and SeNBs. The QDD method is composed of a linear programming formulation, in which the goal is to maximize the network throughput under the considerations of network capabilities and QoS requirements. Recall that for a bearer \(b_{j}\), its incoming data rate is \(r_{in}(b_{j})\). In QDD, if a bearer \(b_{j}\) belongs to \(B^{C}\), the QDD will determine a \(r_{sp}(b_{j})\) value, which represents the data splitting rate. More specifically, after determining \(r_{sp}(b_{j})\), the MeNB will dispatch data flow with a rate \(r_{sp}(b_{j})\) to the corresponding SeNB, and the MeNB itself will handle the remaining data with a rate \((r_{in}(b_{j})-r_{sp}(b_{j}))\). Before showing the detailed formulation of QDD, we define the following parameters. 1. \(RB_{max}\) and \(RB_{max}(s_{k})\): The total number of RBs that can be used by the MeNB and SeNB \(s_{k}\) during \(I_{t}\), respectively. 2. \(M_{u2s}(u_{i})=s_{k}\): This relationship represents that the UE \(u_{i}\) is connected to the SeNB \(s_{k}\). 3. \(D_{r}^{M}(u_{i})\) and \(D_{r}^{S}(u_{i})\): The amount of UE \(u_{i}\)'s downlink data currently stored in the MeNB and the corresponding SeNB, respectively. 4. \(O_{m}(u_{i})\) and \(O_{s}(u_{i})\): The amount of data that can be carried in an RB for UE \(u_{i}\) allocated by the MeNB and the corresponding SeNB, respectively. These parameters can be estimated by averaging the CQI reports of \(u_{i}\) in the previous period. Further, let \(rs\) be the size of an RB in units of seconds. From \(O_{s}(u_{i})\), we can further estimate transmission capabilities \(\hat{O}_{s}(u_{i})\) (in bps) of \(u_{i}\), where \(\hat{O}_{s}(u_{i})=\frac{O_{s}(u_{i})}{rs}\). 5. \(Rb^{M}(u_{i})\) and \(Rb^{S}(u_{i})\): The number of RBs that the MeNB and the corresponding SeNB expect to assign to UE \(u_{i}\), respectively. Note that these two parameters are unknown  
Figure 3: The proposed framework. variables in our formulation. In other words, these two variables can be derived after solving the following linear programming formulation. 6. \(\mathcal{D}_{p}^{M}(u_{i})\) and \(\mathcal{D}_{p}^{S}(u_{i})\): The amount of UE \(u_{i}\)'s data that can be consumed by the MeNB and the corresponding SeNB in the next \(I_{t}\), respectively. More specifically, \(\mathcal{D}_{p}^{M}(u_{i})\) and \(\mathcal{D}_{p}^{S}(u_{i})\) can be obtained by the following two equations: \[\mathcal{D}_{p}^{M}(u_{i})=O_{m}(u_{i})\times Rb^{M}(u_{i})\] \[\mathcal{D}_{p}^{S}(u_{i})=O_{s}(u_{i})\times Rb^{S}(u_{i})\]
7. \(R_{eff}^{M}(u_{i})\) and \(R_{eff}^{S}(u_{i})\): UE \(u_{i}\)'s effective input data rates through the MeNB and the corresponding SeNB, respectively. These two parameters can be divided into the following two cases. * If \(u_{i}\in U^{M}\), i.e., \(u_{i}\) is only served by the MeNB, \(R_{eff}^{M}(u_{i})=r_{in}(b_{i})\) and \(R_{eff}^{S}(u_{i})=0\). * If \(u_{i}\in U^{C}\), i.e., \(u_{i}\) is served by the MeNB and the SeNB \(M_{i2s}(u_{i})\), \(R_{eff}^{M}(u_{i})=r_{in}(b_{i})-R_{eff}^{S}(u_{i})\), and \(R_{eff}^{S}(u_{i})=r_{sp}(b_{i})\). In QDD, the objective of the proposed linear programming formulation is to maximize the network throughput, i.e., the total amount of data allocated to all UEs connected to the MeNB and SeNBs in the next \(I_{t}\) interval. The proposed formulation is as follows. \[\max\sum_{u_{i}\in U}\mathcal{D}_{p}^{M}(u_{i})+\mathcal{D}_{p}^{S}(u_{i})\] (1) s.t. \[