Weightless-P uses Adaptive Data Rate (200 bps to 100 kbps) to optimize power based on link quality, with GMSK modulation ranging from 3,542 uJ per packet at 100 kbps to higher energy at lower rates for extended range. This chapter covers ADR calculations, TV White Space channel management, duty cycle constraints, and TCO analysis across LPWAN technologies.
⏱️ ~12 min | ⭐⭐ Intermediate | 📋 P09.C17B.U01
This chapter explores the technical aspects of Weightless LPWAN technology, including adaptive data rate calculations, TV White Space channel management, and cost analysis implementations. Through Python examples and interactive quizzes, you’ll gain hands-on understanding of Weightless protocol mechanics.
By the end of this chapter, you will be able to:
This chapter dives into the technical implementation of the Weightless LPWAN standard, covering its radio characteristics, network architecture, and protocol stack. If you are evaluating Weightless for a specific IoT deployment, this technical deep-dive helps you understand what the technology can and cannot do.
“Let’s peek under the hood of Weightless!” said Max the Microcontroller. “At the radio level, Weightless-P uses 12.5 kHz narrow channels in sub-GHz bands. That narrow bandwidth is what gives it long range with low power – the receiver can focus on a tiny slice of spectrum and pull out weak signals.”
Sammy the Sensor asked about the network: “The architecture looks similar to LoRaWAN – sensors talk to base stations, which forward to a cloud backend. But Weightless supports full bidirectional communication, so the cloud can send commands and firmware updates back to sensors without waiting for the next uplink window.”
Lila the LED examined the protocol stack: “It has a MAC layer that handles scheduling, a transport layer for reliability, and an application layer for data formatting. The whole stack is designed for constrained devices – small memory footprint, low processing requirements.”
Bella the Battery checked the power specs: “Weightless-P targets 3 to 10 years of battery life with hourly transmissions on a standard AA battery. The sleep current is under 1 microamp, and the radio is active for only a few milliseconds per transmission. That’s the kind of efficiency that keeps me happy!”
Before diving into this chapter, you should be familiar with:
Weightless-P uses Adaptive Data Rate (ADR) to optimize power consumption based on link quality. Devices close to the base station use higher data rates (faster transmission, lower energy), while distant devices use lower data rates (robust modulation, longer range).
| Modulation | Data Rate | TX Time (37 bytes) | Energy | Range |
|---|---|---|---|---|
| GMSK_HIGH | 100 kbps | 13.8 ms | 3,542 µJ | 2 km |
| GMSK_MID | 50 kbps | 17.4 ms | 3,392 µJ | 3.5 km |
| DBPSK_LOW | 12.5 kbps | 39.6 ms | 10,098 µJ | 4.5 km |
| DBPSK_ULTRA | 0.2 kbps | 1,490 ms | 536,400 µJ | 6 km |
Energy calculated at ~257 mW TX power (24 dBm, 3.3 V supply) — typical for a Weightless-P module operating at maximum rated output. Lower TX power configurations reduce energy proportionally.
Key Insight: Higher data rates save battery by reducing time-on-air, not by reducing range. ADR selects the highest data rate that maintains reliable connectivity.
Adjust the sliders to see how ADR modulation choice affects battery life for a Weightless-P sensor.
viewof payloadBytes = Inputs.range([5, 40], {value: 20, step: 1, label: "Payload size (bytes)"})
viewof messagesPerDay = Inputs.range([1, 288], {value: 24, step: 1, label: "Messages per day"})
viewof batteryMah = Inputs.range([500, 10000], {value: 2400, step: 100, label: "Battery capacity (mAh)"})
viewof batteryVoltage = Inputs.range([1.5, 3.7], {value: 3.0, step: 0.1, label: "Battery voltage (V)"}){
const txPower_mW = 100; // 20 dBm = 100 mW
const sleepCurrent_uA = 1.0;
const batteryJ = batteryMah * batteryVoltage * 3.6; // mAh × V × 3.6 = J
// TX time per packet for each modulation (ms), scaled to payloadBytes
const basePayload = 37;
const scale = payloadBytes / basePayload;
const modes = [
{name: "GMSK_HIGH", kbps: 100, txMs: 13.8 * scale, range: "2 km"},
{name: "GMSK_MID", kbps: 50, txMs: 17.4 * scale, range: "3.5 km"},
{name: "DBPSK_LOW", kbps: 12.5, txMs: 39.6 * scale, range: "4.5 km"},
{name: "DBPSK_ULTRA", kbps: 0.2, txMs: 1490 * scale, range: "6 km"},
];
const rows = modes.map(m => {
const energyPerMsg_uJ = txPower_mW * m.txMs; // mW × ms = µJ
const dailyTx_uJ = energyPerMsg_uJ * messagesPerDay;
const dailySleep_uJ = sleepCurrent_uA * (24 * 3600 * 1e6 / 1e6) * 1e-3 * batteryVoltage * 1000; // approx
// Sleep: 1µA × voltage × seconds → µJ
const sleepSec = 86400 - (m.txMs / 1000 * messagesPerDay);
const dailySleep_uJ2 = sleepCurrent_uA * batteryVoltage * sleepSec * 1e-3; // µA × V × s × mA factor
const totalDaily_J = (dailyTx_uJ + dailySleep_uJ2) / 1e6;
const yearsLife = totalDaily_J > 0 ? (batteryJ / totalDaily_J / 365).toFixed(1) : "∞";
const barWidth = Math.min(100, (parseFloat(yearsLife) / 20) * 100);
const barColor = parseFloat(yearsLife) >= 5 ? "#16A085" : parseFloat(yearsLife) >= 3 ? "#E67E22" : "#E74C3C";
return `<tr>
<td style="padding:6px 8px;font-weight:bold;color:#E67E22;">${m.name}</td>
<td style="padding:6px 8px;">${m.kbps} kbps</td>
<td style="padding:6px 8px;">${m.txMs.toFixed(1)} ms</td>
<td style="padding:6px 8px;">${(energyPerMsg_uJ).toFixed(0)} µJ</td>
<td style="padding:6px 8px;">${m.range}</td>
<td style="padding:6px 8px;">
<div style="display:flex;align-items:center;gap:6px;">
<div style="width:80px;background:#444;border-radius:3px;height:10px;">
<div style="width:${barWidth}%;background:${barColor};border-radius:3px;height:10px;"></div>
</div>
<span style="font-weight:bold;color:${barColor};">${yearsLife} yrs</span>
</div>
</td>
</tr>`;
}).join("");
return html`<div style="background:#2C3E50;color:#fff;padding:16px 20px;border-radius:6px;font-family:Arial,sans-serif;">
<h4 style="margin:0 0 12px;color:#E67E22;">ADR Modulation — Battery Life Comparison</h4>
<table style="width:100%;border-collapse:collapse;font-size:0.88em;">
<thead>
<tr style="background:#16A085;text-align:left;">
<th style="padding:6px 8px;">Modulation</th>
<th style="padding:6px 8px;">Data Rate</th>
<th style="padding:6px 8px;">TX Time</th>
<th style="padding:6px 8px;">Energy/Msg</th>
<th style="padding:6px 8px;">Range</th>
<th style="padding:6px 8px;">Battery Life</th>
</tr>
</thead>
<tbody>${rows}</tbody>
</table>
<p style="margin:10px 0 0;font-size:0.8em;opacity:0.8;">💡 ADR automatically selects the highest (most efficient) modulation that maintains a reliable link at your distance.</p>
</div>`;
}How much battery life does Weightless-P’s ADR save compared to fixed low data rate? Consider a parking sensor (1 message/hour, CR2450 coin cell = 19.8 kJ capacity).
Fixed DBPSK_LOW (conservative, always 4.5 km range): - Energy per message: 10,098 µJ - Daily energy: \(10{,}098 \times 24 = 242{,}352\text{ µJ} = 0.242\text{ J}\) - Battery life: \(\frac{19{,}800\text{ J}}{0.242\text{ J/day}} = 81{,}818\text{ days} \approx 224\text{ years}\) (sensor fails first!)
ADR-Optimized (close sensors use GMSK_HIGH, far use DBPSK_LOW): - Assume 70% of sensors within 2 km (use GMSK_HIGH at 3,542 µJ), 30% need 4.5 km (DBPSK_LOW at 10,098 µJ) - Average energy: \(0.7 \times 3{,}542 + 0.3 \times 10{,}098 = 2{,}479 + 3{,}029 = 5{,}508\text{ µJ}\) - Daily energy: \(5{,}508 \times 24 = 132{,}192\text{ µJ} = 0.132\text{ J}\) - Battery life: \(\frac{19{,}800}{0.132} = 150{,}000\text{ days} \approx 411\text{ years}\)
ADR improvement: \(411 / 224 = 1.83×\) battery life extension! Even better: network capacity doubles because close sensors finish transmitting in 13.8 ms vs 39.6 ms (2.87× faster), freeing time slots for more devices. ADR is a win-win: longer battery life and higher network throughput!
This implementation demonstrates how Weightless-P dynamically adjusts data rate based on link quality, optimizing for battery life and throughput.
Expected Output:
=== Weightless-P Adaptive Data Rate Analysis ===
Scenario: Agricultural sensor
Payload: 15 bytes
Frequency: 24 messages/day
Battery: 2400 mAh (2× AA)
Modulation Comparison:
----------------------------------------------------------------------------------------------------
Modulation Data Rate TX Time Energy Range Battery Life
----------------------------------------------------------------------------------------------------
GMSK_HIGH 100.0 kbps 13.0 ms 1755.0 µJ 2.0 km 6.9 years
GMSK_MID 50.0 kbps 17.4 ms 3392.4 µJ 3.5 km 3.6 years
DBPSK_LOW 12.5 kbps 39.6 ms 10098.0 µJ 4.5 km 1.2 years
DBPSK_ULTRA 0.2 kbps 1490.0 ms 536400.0 µJ 6.0 km 0.0 years
====================================================================================================
Optimal Modulation Selection (ADR):
--------------------------------------------------------------------------------
Distance Selected Data Rate Battery Life
--------------------------------------------------------------------------------
1.5 km GMSK_HIGH 100.0 kbps 6.9 years
3.0 km GMSK_MID 50.0 kbps 3.6 years
4.0 km DBPSK_LOW 12.5 kbps 1.2 years
5.5 km DBPSK_ULTRA 0.2 kbps 0.0 years
================================================================================
Key Insight: ADR uses highest data rate for given distance,
minimizing time-on-air and maximizing battery life.Key Concepts Demonstrated:
This implementation simulates TV White Space channel discovery for Weightless-W, demonstrating cognitive radio and dynamic spectrum access.
Expected Output (abbreviated):
TVWS Availability Comparison:
Location Available Bandwidth Utilization
London (Urban) 22 channels 176 MHz 40.0%
Cambridge (Suburban) 28 channels 224 MHz 25.0%
Rural Northumberland 32 channels 256 MHz 15.0%
Key Insight: Rural areas have more TVWS availability (less TV coverage),
making Weightless-W ideal for agricultural and rural IoT applications.Key Concepts Demonstrated:
This implementation compares total cost of ownership (TCO) for Weightless-P, LoRaWAN, and NB-IoT across different deployment scales.
Expected Output (abbreviated):
Per-Device Annual Cost:
Technology Small (100 dev) Large (5000 dev)
Weightless-P €7.57/year €2.67/year
LoRaWAN €27.71/year €2.51/year
NB-IoT €26.86/year €26.16/year
Key Insights:
- Small deployments: Weightless-P/LoRaWAN cheaper (no subscriptions)
- Large deployments: Private networks dominate (economies of scale)
- NB-IoT subscription costs scale linearly with device countKey Concepts Demonstrated:
:
This chapter covered the technical implementation aspects of Weightless:
ADR Optimizes Link Budget, Not Power Directly: Adaptive Data Rate selects highest modulation (GMSK 100 kbps) that maintains reliable link. Power savings come from shorter time-on-air (13.8 ms vs 1,490 ms), not lower transmit power (220 mA constant). Key insight: battery life dominated by transmission duration at fixed power, not power level at fixed duration.
TV White Space Availability vs Complexity: TVWS offers cleaner spectrum (no ISM congestion) + better propagation (470-790 MHz vs 868/915 MHz). BUT requires cognitive radio (GPS + geolocation database client + spectrum sensing), adding $30-50 per device vs $5-10 for ISM modules. Trade-off: spectrum quality vs deployment complexity.
TCO Analysis Reveals Scale Effects: Small deployment (100 devices): Weightless-P cheaper than LoRaWAN (private network vs public subscription). Large deployment (5,000 devices): LoRaWAN cheaper (module cost $5 vs Weightless $18, volume matters). Crossover point ~500 devices where economies of scale reverse the cost advantage.
Foundational Concepts:
Comparative Technical Analysis:
Market Context:
Hands-On Tools:
| Chapter | Focus | Why Read It |
|---|---|---|
| Weightless Market Comparison | Ecosystem maturity, vendor landscape, commercial positioning | Understand why limited module availability constrains Weightless-P despite strong technical specs |
| Weightless LPWAN Overview | Variant comparison (W, N, P), use cases, spectrum bands | Revisit the three-variant architecture to place ADR and TVWS calculations in context |
| LoRaWAN Architecture | LoRa ADR (SF7–SF12), network server, Class A/B/C | Compare Weightless ADR (GMSK/DBPSK modulation) against LoRaWAN’s spreading factor approach |
| NB-IoT Power and Channel | Repetition coding, DRX cycles, PSM | Contrast NB-IoT’s coverage extension method with Weightless-P’s ADR for the same link budget challenge |
| LPWAN Fundamentals | Link budget, Aloha vs TDMA, duty cycle | Deepen the theoretical foundation behind the calculations demonstrated in this chapter |