Estimate how many people a WiFi network can support during its busiest period. The model converts aggregate throughput into usable payload capacity, divides it by the average demand of an active user, and then expands that simultaneous count using your concurrency assumption. It is useful for early access-point and backhaul planning when you want the assumptions visible instead of relying on a connected-device limit.
The main result is the estimated total user population supportable under the chosen concurrency assumption. The active-user figure is the harder instantaneous capacity constraint.
Capacity is shared and traffic is bursty. Coverage quality, channel width, interference, backhaul, scheduler behavior, and client capabilities can lower real throughput.
With 500 Mbps available, 65% efficiency, 4 Mbps per active user, and 30% concurrency, usable throughput is 325 Mbps. That supports 81 simultaneous users and an estimated 270 total users.
No. It estimates supported users from expected simultaneous demand. Idle associated devices consume little payload capacity but still add protocol overhead.
Real networks lose usable throughput to contention, scheduling, retransmissions, control traffic, and changing radio conditions.
Use a measured busy-hour average or a workload-based estimate. Video, cloud desktops, and large transfers need more throughput than messaging or telemetry.
It is the share of the total user population expected to be actively transferring data during the same busy interval.
No. It is an early planning estimate. Validate the design with coverage modeling, spectrum conditions, device mix, and busy-hour measurements.
| Variable | Meaning | Unit |
|---|---|---|
| Available throughput | Aggregate service capacity before efficiency | Mbps |
| Per-user demand | Average traffic for one active user | Mbps |
| Concurrency | Users active during the same busy interval | % |
| Efficiency | Share converted to usable payload throughput | % |