# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 5 Simulation Parameters  
We implemented the proposed adaptive SPS algorithm for multi-service traffic classes with DRX power saving scheme using the Riverbed Modeler software (previously known as OPNET). We deployed an M2M scenario where the LTE eNodeB serves M2M UEs including both sensor nodes generating packet bursts intermittently and M2M GWs which  
Figure 8: UE power consumption model.  
aggregate the traffic generated by their respective M2M area networks to be sent to the server. The generated M2M traffic are assigned to different traffic classes based on their packet delay budget values. The scope of the packet delay budget is from the M2M UE to the eNodeB (the core network delay is not considered here). The important simulation parameters are described in Table 1.  
The simulated multi-service traffic classes and their respective PDB values are listed in Table 2.  
The selected value of \(f_{S}\) controls the value of \(T_{SPS,S}\) and also affects the service capacity \(N_{S}\), which is the maximum supported number of adaptive SPS users of each traffic class. which is demonstrated in Figure 9 for single-class simulation environment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline LTE Configuration & 3 MHz TDD \\ \hline Uplink:Downlink ratio & 5:5 \\ \hline Maximum UE transmission power & 0.5 W \\ \hline Maximum eNodeB transmission power & 5 W \\ \hline UE reception sensitivity & \(-95\) dBm \\ \hline eNodeB reception sensitivity & \(-123\) dBm \\ \hline UE antenna gain & \(-1\) dBi \\ \hline eNodeB antenna gain & 15 dBi \\ \hline SR periodicity & 10 ms \\ \hline Uplink control channels & 2 \\ \hline Channel model & Suburban Erceg, Terrain C [33] \\ \hline Radio network model & Single cell, 3 km radius \\ \hline Adaptive SPS parameters & \(n_{S}=3\), \(N_{P,max}=3\) \\ \hline M2M traffic model & Poisson arrival process with mean \(\lambda_{S}\) \\  & requests/millisecond for traffic class \(S\) where \(\lambda_{S}\) is varied for different simulation runs \\ \hline M2M request size & Mean packet size 10 Bytes (exponentially  distributed) \\ \hline DRX parameters & ON-timer = 5 ms, Inactivity timer = 50 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Traffic Class** & **PDB (ms)** \\ \hline A & 50 \\ B & 100 \\ C & 150 \\ D & 200 \\ E & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traffic Model for Multi-service Traffic Classes.  
For \(f_{S}=0.2\), Figure 9(i) shows that for all classes, TDD configuration 6 offers the lowest capacity (5 uplink subframes/frame) and it increases for TDD configuration 0 (6 uplink subframes/frame) and the highest capacity can achieved for FDD (10 uplink subframes/frame). More capacity can also be achieved as we move from class A to class E, because the \(T_{PDB,S}\) values increase thus allowing for longer \(T_{SPS,S}\) and higher values of \(m_{S}\) supporting more UEs to be multiplexed in time. Figure 9(ii) shows that for \(f_{S}=0.4\), the capacity is doubled for all classes as the respective values of \(T_{SPS,S}\) are also doubled.  
For simulation of the multi-service traffic classes, the 5 traffic classes mentioned in Table 2 are run for different values of \(f_{S}\) (with respective full service capacity) for the adaptive SPS algorithm. For the dynamic scheduler, the number of M2M UEs deployed in each class is equivalent to that for \(f_{S}=0.4\).  
For simulation of the DRX power saving with the adaptive SPS algorithm, the traffic model is shown in Table 3. Four traffic classes were deployed with the described traffic profile and delay budget values. The number of UEs per traffic class was increased for different simulation runs to observe the performance of the algorithm.