Signal creates value
Model the link between towers and Bitcoin.
Explore a cellular-connected computing setup, its energy and network requirements, and a return estimate with every key assumption in view.
Planning model · no live tower telemetry
Same signals.
Brighter future.
01 / Configuration
Feasibility planner
Adjust the site profile and market assumptions to recalculate the model.
Site configuration
Planning inputs for the connected compute setup
02 / Modeled outcome
Estimated monthly profile
Planned compute units
1
200 TH/s combined
Hardware estimate
$4,500
One-time equipment cost
Electricity estimate
$287
2,394 kWh / month
Estimated Bitcoin
0.003463 BTC
$225 gross / month
Estimated monthly result after electricity
-$62/ month
No simple hardware payback in this estimate
This subtracts modeled electricity only. It does not include hardware depreciation, pool fees, cooling, connectivity charges, taxes, financing, or repairs.
03 / Network profile
A clear view of usage and capacity.
A planning estimate based on monthly data volume and the uplink capacity entered above. It does not read a carrier network or measure a tower connection.
Projected network usage
Estimated data transfer
20 GB / month
0.062 Mbps average per site
Average throughput only; burst demand and radio conditions require field measurement.
04 / Return assumptions
Every estimate starts with an assumption.
Change the reference values below to see how the return estimate responds. Results are scenarios, not predictions or promised returns.
Model basis: 144 blocks per day, a 30-day month, your entered block reward and reference BTC price, and the availability percentage applied to both output and energy.
Network hashrate, reward, and price are user-editable reference assumptions. Actual difficulty, pool fees, block timing, uptime, transaction fees, and market price can change. No live market or mining-pool data is connected.
A measured approach
Infrastructure first. Algorithms next.
01Understand site power, connectivity, and operating constraints.
02Compare hardware and energy requirements against those limits.
03Evaluate the model before considering scale or deployment.