## 8 Scheduling and Fairness with RRUs  
Scheduling considered here is a centralised scheduling in which all the network resources will be assigned and processed at the BBUs end. RRUs do not have their own scheduler or resources but rather act as a transparent relay to their corresponding BBUs, to improve the resources use efficiency. Scheduling DBS network with distributed RRUs will not only improve the data throughput but also the resources fairness. Fairness index [40] rates how equally the resources are distributed over \(N\) number of devices, defined by:  
\[J(x)=\frac{\left(\sum_{i=1}^{N}x_{i}\right)^{2}}{N\sum_{i=1}^{N}x_{i}^{2}} \tag{10}\]  
where \(x\) is a vector of length \(N\), which contain the data throughput achieved by the \(N\) devices. The scheduling simulation has been conducted as per Algorithm 1 for RR, PF and best CQI algorithms. Seven BSs has been considered with 30 devices per node. This is because that the network size is determined by the inter-site distance (ISD). ISD has been set to 150 in order to simulate dense network scheme in mmWave which help compensate for the high path loss at this band [30; 41; 42]. Therefore, with our chosen 600 m\({}^{2}\) ROI, there are only seven sites that \"fit\" in the ROI, expanding the ROI will insure more sites in the simulation but will NOT affect the result, as the ISD is the same. Generally, we have used 500 devices in order to check the dimension of the connectivity and weather these devices completed their data transaction in time or not. As per the network model table, there are 500 MTC devices in total under test. For a certain bandwidth/number of resources, the devices have been added group by group until some devices cannot have enough resources to complete data transaction (considered in outage). As shown in Table 3--MIMO figure, for instance, there are 474 devices that completed their payload transfer. This has been elaborated in the section of results and discussion. The outcome is shown as per Tables 3-5, where a maximum of 474 out of 500 devices have successfully completed their data transaction (MIMO at 20 MHz). Then when it comes to show the scheduling, spectral efficiency calculation and fairness calculation, only 30 devices were used, in order to check the successfully received bits, calculate the consumed energy per bit, to all devices in test, which in this case 30 devices only (for the sake of simplicity). As shown in Figure 10, the peak, average and cell edge data throughput has been shown for both DBS and CBS architecture. The data throughput here is calculated from all successfully transmitted bits from all MTC devices during the simulated TTIs. The simulated time here is the time (in TTI) required by the corresponding MTC devices in order to complete full data transaction, as depicted in Algorithm 1. It is shown that DBS has better average and cell edge performance but it is sacrificing the peak data throughput, except for best-CQI scheduler due to its greedy nature, as it gives its resources to devices having high SINR, ignoring those at the cell-edge. Figures 11-13 present the CDF of the MTC average spectral efficiency of the three scheduling algorithms used in Algorithm 1, which show that DBS has improved the system performance in terms of average spectral efficiency in all scheduling algorithms. Fairness index j on the other hand has also improved due to the distributed RRUs of DBS architecture. The resources and data throughput in the former architecture guarantee a fairer distribution over the N devices in the simulation model. Figure 14 demonstrates the fairness of CBS and DBS architecture, where DBS shows better resource fairness, the data label shows the percentage gain in fairness of DBS over CBS network, due to the spatial distribution of remote antennas. Figure 11: Average spectral efficiency for RR scheduling algorithm, with and without distributed RRUs. Figure 12: Average spectral efficiency for PF scheduling algorithm, with and without distributed RRUs. Figure 10: Peak/avg/Edge TP with/out RRUs.