# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 6 Results
### Multi-Service Adaptive SPS Algorithm
This trade-off is required to keep the eNodeB updated about the UE buffer status and keep the SPS ongoing as new data arrives at the UE, without requiring new connection establishment/control signaling. However, if the SPS period is much shorter than the request inter-arrival gap (which is the case here initially), often there is no data to send at the device and 1 PRB is wasted without sending any actual data. So dynamic scheduling has an advantage at this point since it does not give any grant for such cases. However, as the arrival rate increases (\(\lambda_{S}>1/_{TSPSS}\)), the adaptive scheduler can utilize the resources better than the dynamic since it allows accumulation of more packets at the UE buffer before they can be transmitted. In this way, the allocated PRBs can be fitted better with the buffered packets and more bits/PRB can be transmitted to ensure lower utilization hence higher efficiency. Figure 14: Percentage of packets meeting delay budget (90 UEs). Figure 16 shows the delay budget performance of the adaptive SPS and the dynamic scheduler as we add more class F UEs in the system. Although the adaptive scheduler still supports more packets to meet their delay budget than the dynamic one for increasing number of UEs, its performance gradually degrades from 67% QoS satisfaction for 100 UEs to 37% for 150 UEs. To address this issue, we developed the functionality to pre-empt SPS traffic classes with higher delay budget to admit newly arriving SPS traffic with smaller delay budget. So the pre-emptive adaptive SPS prioritizes class F traffic because it has a lower PDB of 100 ms than the previously allocated traffic classes C, D and E. The pre-emption is done starting with the traffic class with the highest PDB and allocations are pre-empted until there is enough room to support all the UEs of the higher priority traffic class. When only 10 class F UEs are added into the system, class E UEs are pre-empted and those UEs are scheduled dynamically. Afterwards, when more UEs from class F are added, gradually the equivalent number of UEs are pre-empted from class D and C. By this mechanism, it is ensured that the overall percentage of packets meeting their own delay budgets is increased as we can see clearly in Figure 16 for the pre-emptive adaptive scheduler performance. Figure 17 compares the mean PUSCH utilization values for the adaptive SPS, dynamic and pre-emptive adaptive SPS algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Traffic Class (S)**} &
\begin{tabular}{c} **Mean \(\lambda_{S}\)** \\ **(Requests/ms)** \\ \end{tabular} & \multirow{2}{*}{\(T_{PDB,S}\) **(ms)**} & \multirow{2}{*}{\(T_{SPS,F}\) **(ms)**} & \multirow{2}{*}{\(N_{S}\)} \\ \hline F & 0.1 & 100 & 40 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters for New Traffic Class F.  
Figure 15: Mean data and control channel utilization (90 UEs). Figure 16: Percentage of packets meeting delay budget (150 UEs). Figure 17: Mean data channel utilization with increasing number of UEs. Both of the adaptive schedulers shows similar PUSCH utilization for increasing number of UEs and reaches channel capacity saturation for 150 M2M UEs. On the other hand, the PUSCH utilization of the dynamic scheduler reaches a plateau for 140 UEs and does not increase further. So the adaptive schedulers clearly exhibit more capacity in the data channel with the pre-emptive adaptive SPS offering even better delay performance by prioritizing among SPS traffic classes. Figure 18 compares the mean PDCCH utilization values for the three scheduling schemes. The pre-emptive adaptive SPS shows slightly higher PDCCH utilization than the basic adaptive SPS, which is due to the pre-emptive version scheduling more requests dynamically to prioritize class F traffic. Yet both the adaptive SPS schemes achieve much lower control channel utilization than the dynamic one. Full dynamic scheduling requires excessive control signaling that saturates the PDCCH channel for 140 UEs in the simulation.