# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 5 Performance Evaluation  
We have compared the performance of the proposed SRA algorithm based on dynamic programming to that of the novel greedy algorithm [13], which is used as the baseline for performance comparisons in this paper. To evaluate the proposed SRA algorithm, we use the MATLAB LTE toolbox (version 2017a, Mathworks, Natick, MA, USA), with a 5G library that supports the system architecture as per the 3 GPP recommendations [83], similar to the state-of-the-art work, such as [13; 14]. For fair comparisons, we use the same simulation settings as the baseline [13]. To implement the greedy SRA and dynamic programming algorithms, we have modified the lteDLResourceGrid function.  
Performance is measured in terms of goodput and fairness [84] for the two dominant 5G waveforms, i.e., OFDM and FBMC [85; 86; 87]. In the rest of this section, we call the proposed method dynamic and the baseline method [13] as greedy for brevity. The greedy-FBMC and greedy-OFDM conventions are used to address the reproduced baseline approach [13] for the FBMC and OFDM waveforms, respectively. Their results are plotted using dotted lines. Similarly, dynamic-FBMC and dynamic-OFDM refer to the proposed SRA algorithm with the FBMC and OFDM waveforms, respectively. The results of dynamic-FBMC and dynamic-OFDM are plotted with solid lines for clarity of presentation.  
In our previous work [17], we evaluated performance for LTE with a 20-MHz bandwidth and 100 RBs, since the 5G standardization was still underway at that time. As the 5G standardization has been finalized, we have three broad categories of use cases: eMBB, URLLC and eMTC for 5G NSA, as discussed before. From the scheduling perspective, we can distinguish these use cases as different requirements for bandwidth and RBs: (1) eMBB with a 100-MHz bandwidth with 500 RBs or more; (2) URLLC with up to 5 MHz and 25 RBs; and (3) eMTC with 1.4 MHz and 6 RBs or less. Using these settings, we compare the performance of greedy-FBMC, greedy-OFDM, dynamic-FBMC and dynamic-OFDM. Unfortunately, during the performance evaluation, we found two limitations of the LTE toolbox: (1) for a single cell, only up to 16 users can be simulated; and (2) a maximum of 100 RBs can be modeled in the PHY layer; hence, we cannot evaluate the eMBB case in this paper. A more extensive evaluation is reserved for future work. The results of the URLLC and eMTC use-cases are shown in Figures 3 and 4 for goodput and in Figures 5 and 6 for fairness measurements. A detailed discussion of goodput and fairness measurements is given in the following subsections.  
Figure 3: _Cont._Figure 4: _Cont._  
Figure 3: Average goodput results for the greedy and dynamic approaches. EPA, Extended Pedestrian A; EVA, Extended Vehicular A; ETU, Extended Typical Urban; LTE, Long Term Evolution; URLLC, Ultra-Reliable Low Latency Communication; eMTC, enhanced Machine Type Communication.  
Figure 4: Impact of correlations on average goodput for the greedy and dynamic approaches.  
Figure 5: _Cont_.  
Figure 5: Goodput Jain Fairness Index for the greedy and dynamic approaches.  
Figure 6: Delay Jain Fairness Index for the greedy and dynamic approaches.