al or TTI). This metric is important because it impacts the Medium Access Control (MAC) and Physical (PHY) layer implementation/test complexity as well as testability. To deduce what we think is a reason- able number of UE/TTI, we show a couple of strategies, with a few Small Cell use-cases as practical examples. There are three strategies we can take: Option 1: Engineering deduction Step 1, assume a M/D/1 queueing model 2 with a single server representing a single scheduled UE/TTI, a 2000 slot/second (FR1) service rate u and a (p = 0.95, so 0.95 X 2000 ) 1900 "virtual Transport Block"/second arrival rate, where we assume a 95% loaded system (p = 0.95) Plugging this into the equation for average waiting time: we get w = 1/2000 + 0.95/(2 X 2000 X (1-0.95)) = 5 microseconds. Step 2, given that M/D/C queues are tough to evaluate with a lot of complex math we would like to avoid, we are going to make the gross simplification that we can simply multiply this waiting time w by the ratio of active/scheduled UE to approximate a (say) round robin scheduler that reduces the effective service rate. So, in case of 64 active UE and 8 scheduled UE we get to (64/8) X 5 milliseconds = 40 milliseconds average waiting time in the system. Now, we can plot all of this in a graph to play with numbers (Fig. 3-7). Note that the waiting time for low system loading here is unreasonably high because we assume a very basic round robin scheduler that doesn't skip users for which no data is avail- able. As such, we focus on the right-hand side of the chart. What it shows is that when the system gets highly loaded, queueing time can get reasonably high. Let's define "high" as 20 milliseconds-an arbitrary but reasonable choice for an Enhanced mobile broadband (eMBB) use-case. This would suggest a ratio of 8 between active UE and Scheduled UE to be an "pretty good" choice for a highly loaded system (90%). Assuming an Integrated Small Cell with 64 Connected UEs, and a 5x ratio between Connected and Active UEs means 64/5 ~ = Active UEs and (13/8 