853 Wi-Fi Review: Wi-Fi 6 for High-Density IoT Deployments

853.1 Learning Objectives

By the end of this section, you will be able to:

Calculate Throughput Requirements: Aggregate device traffic and compare against AP capacity
Analyze Airtime Efficiency: Understand why throughput alone doesn’t predict performance
Apply OFDMA Concepts: Allocate Resource Units for mixed IoT traffic types
Evaluate TWT Benefits: Calculate power savings from Target Wake Time scheduling
Plan Dense Deployments: Design channel plans and AP density for industrial IoT

853.2 Prerequisites

Before working through this analysis, ensure you understand:

Wi-Fi 6 Features - OFDMA, TWT, BSS Coloring
Wi-Fi Review: Power Optimization - Power calculations
Wi-Fi Fundamentals - Core 802.11 concepts

853.3 Wi-Fi 6 for High-Density IoT Deployments

Scenario:

A smart factory is deploying 500 Wi-Fi-connected IoT devices across a 10,000 m2 facility:

200 vibration sensors (25 KB/s continuous monitoring, latency <50 ms)
150 temperature sensors (100 bytes every 10 seconds, latency <5 seconds)
100 cameras (2 Mbps video stream, latency <100 ms)
50 AGV robots (Automated Guided Vehicles, 50 KB/s telemetry + control, latency <20 ms)

The facility currently has 10x Wi-Fi 5 (802.11ac) access points providing coverage. Each AP supports:

Wi-Fi 5 specs: 80 MHz channels, 256-QAM, 4 spatial streams, theoretical 1.73 Gbps
Typical real-world throughput: 600-800 Mbps per AP
Frequency: 5 GHz band (channels 36, 40, 44, 48, 52, 56, 60, 64, 100, 104)

Network architect proposes upgrading to Wi-Fi 6 (802.11ax) APs with:

Wi-Fi 6 specs: 80 MHz channels, 1024-QAM, 4 spatial streams, OFDMA, TWT
Theoretical: 2.4 Gbps
Typical real-world: 1.2-1.5 Gbps per AP

Analysis Questions:

Calculate the total required throughput and determine if Wi-Fi 5 infrastructure can support the deployment
Analyze how Wi-Fi 6 OFDMA improves efficiency for mixed IoT traffic (calculate resource units needed)
Estimate power savings using Wi-Fi 6 TWT (Target Wake Time) for the 150 temperature sensors
Recommend channel planning and AP density for optimal performance

853.4 Total Throughput Requirements and Wi-Fi 5 Capacity Analysis

853.4.1 Device Traffic Calculation

Device Type	Count	Per-Device Rate	Total Throughput
Vibration sensors	200	25 KB/s (200 kbps)	40 Mbps
Temperature sensors	150	100 bytes/10s (80 bps)	0.012 Mbps
Cameras	100	2 Mbps	200 Mbps
AGV robots	50	50 KB/s (400 kbps)	20 Mbps
TOTAL	500		260 Mbps

853.4.2 Wi-Fi 5 Capacity Analysis

Metric	Value	Calculation
Number of APs	10	Existing deployment
Throughput per AP	700 Mbps	Real-world (midpoint 600-800 range)
Total capacity	7,000 Mbps (7 Gbps)	10 x 700
Required throughput	260 Mbps	From table above
Throughput utilization	3.71%	260 / 7,000

Initial Verdict: Wi-Fi 5 CAN support deployment - only 3.71% throughput utilization

But wait… This analysis is misleading!

It only considers throughput, not airtime efficiency.

853.4.3 Per-AP Device Distribution (even distribution)

Device Type	Devices per AP	Throughput per AP
Vibration sensors	20	4 Mbps
Temperature sensors	15	0.001 Mbps
Cameras	10	20 Mbps
AGV robots	5	2 Mbps
Total	50 devices	26 Mbps (3.7%)

Show code

kc_wifi6_1 = {
  const container = document.createElement('div');
  container.className = 'inline-kc-container';

  if (typeof InlineKnowledgeCheck !== 'undefined') {
    container.appendChild(InlineKnowledgeCheck.create({
      question: "A Wi-Fi 5 deployment shows only 3.71% throughput utilization but users report high latency. What metric would better predict performance issues?",
      options: [
        {text: "Signal strength (RSSI) at each device location", correct: false, feedback: "While RSSI affects performance, it doesn't explain high latency when throughput utilization is low. The issue is contention-related."},
        {text: "Airtime utilization - the percentage of time the channel is occupied by transmissions", correct: true, feedback: "Correct! Throughput only measures data transferred, but airtime includes all overhead (backoff, headers, acknowledgments). Many small packets from IoT devices consume disproportionate airtime due to per-packet overhead. High airtime causes contention delays even when throughput is low."},
        {text: "Number of SSIDs configured on each AP", correct: false, feedback: "Multiple SSIDs can cause management overhead but wouldn't explain latency with only 3.71% throughput utilization."},
        {text: "Channel width configuration (20 MHz vs 80 MHz)", correct: false, feedback: "Channel width affects throughput capacity, which is clearly not the bottleneck at 3.71% utilization."}
      ],
      difficulty: "medium",
      topic: "wifi-airtime"
    }));
  }
  return container;
}

853.5 Hidden Problem: Airtime Efficiency and Latency

Wi-Fi 5 uses OFDM (not OFDMA), meaning only one device transmits at a time. Each transmission requires overhead:

853.5.1 Wi-Fi 5 Packet Overhead Components

DIFS (Distributed Inter-Frame Space): 28 us
Backoff (average): 67.5 us
Payload transmission: Variable (depends on PHY rate)
SIFS + ACK: 24 us
Total overhead per packet: ~120 us + transmission time

853.5.2 Airtime Analysis (per AP)

Device Type	Devices	Packets/s	PHY Rate	Packet Time	Airtime %
Vibration sensors	20	2,000	200 Mbps	132 us	26.4%
Temperature sensors	15	1.5	200 Mbps	132 us	0.02%
Cameras	10	600	400 Mbps	151 us	9.05%
AGV robots	5	500	200 Mbps	142 us	7.11%
TOTAL	50	3,101			42.58%

Revised Verdict: Wi-Fi 5 experiences 42.6% airtime utilization per AP

This is approaching the 50% threshold where Wi-Fi performance degrades significantly:

Increased collision probability
Higher latency (devices queue longer for transmission)
Reduced effective throughput
Little headroom for growth

853.5.3 Latency Impact

Using M/M/1 queuing model with 42.6% utilization (rho = 0.426):

Component	Latency	Notes
Queue delay	0.11 ms	rho/(1-rho) x service_time
Service time	0.14 ms	Average packet transmission
Processing	2.0 ms	AP routing/switching
Total latency	2.25 ms	Meets all requirements (barely)

Wi-Fi 5 Verdict: Can technically support deployment but operates at 42.6% airtime utilization with minimal headroom.

853.6 Wi-Fi 6 OFDMA Efficiency Improvement

853.6.1 OFDMA Overview

Wi-Fi 6 divides the 80 MHz channel into smaller Resource Units (RUs) that can be allocated to multiple devices simultaneously:

RU Size	Bandwidth	Data Subcarriers	Typical Use Case
26-tone	2 MHz	24	Ultra-low data rate (sensors)
52-tone	4 MHz	48	Low data rate (IoT devices)
106-tone	8 MHz	102	Medium data rate
242-tone	20 MHz	234	High data rate
484-tone	40 MHz	468	Very high data rate (cameras)
996-tone	80 MHz	980	Maximum throughput

80 MHz channel can be divided into:

Up to 37x 26-tone RUs, OR
Up to 18x 52-tone RUs, OR
Up to 9x 106-tone RUs, OR
Mix of different sizes

853.6.2 RU Allocation for Factory Devices

Device Type	Data Rate	Assigned RU	RU Bandwidth	Provided Rate	Efficiency
Temperature sensors	80 bps	26-tone	2 MHz	~3 Mbps	0.0027%
Vibration sensors	200 kbps	52-tone	4 MHz	~6 Mbps	3.3%
AGV robots	400 kbps	106-tone	8 MHz	~14 Mbps	2.9%
Cameras	2 Mbps	242-tone	20 MHz	~60 Mbps	3.3%

Key Insight

Even the smallest 26-tone RU provides 3.75 million times more bandwidth than needed for temperature sensors, making OFDMA extremely efficient for low-rate IoT devices.

Show code

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  container.className = 'inline-kc-container';

  if (typeof InlineKnowledgeCheck !== 'undefined') {
    container.appendChild(InlineKnowledgeCheck.create({
      question: "A temperature sensor needs only 80 bps but is allocated a 26-tone RU providing 3 Mbps (3.75 million times more bandwidth). Why is this still efficient?",
      options: [
        {text: "The sensor can buffer data and send in larger bursts", correct: false, feedback: "While buffering is possible, the efficiency gain comes from simultaneous transmission, not buffering."},
        {text: "The excess bandwidth is used for error correction and retries", correct: false, feedback: "Error correction is included in the 3 Mbps rate. The efficiency comes from parallel transmission."},
        {text: "Multiple sensors share the same RU using time division", correct: false, feedback: "RUs are allocated per-transmission, not time-divided between devices within a single TXOP."},
        {text: "The sensor transmits simultaneously with other devices, eliminating per-packet overhead that would otherwise waste airtime", correct: true, feedback: "Correct! Without OFDMA, each sensor's tiny packet requires full channel access overhead (120+ us). With OFDMA, many sensors transmit in parallel during a single TXOP. The 'wasted' RU capacity is irrelevant because airtime (the scarce resource) is used efficiently."}
      ],
      difficulty: "hard",
      topic: "wifi-ofdma"
    }));
  }
  return container;
}

853.6.3 RU Requirements per AP

80 MHz channel = 9x 106-tone RUs (baseline). Converting all RU sizes to 106-tone equivalent:

RU Size	Equivalent Factor	Devices per AP	Peak Load (30%)	RUs Needed
26-tone (temp)	0.24x	15	4.5 active	1.08 RUs
52-tone (vibration)	0.5x	20	6 active	3.0 RUs
106-tone (AGV)	1.0x	5	1.5 active	1.5 RUs
242-tone (camera)	2.3x	10	3 active	6.9 RUs
TOTAL		50	15 active	12.48 RUs

Analysis: 12.48 RUs needed vs 9 RUs available = 1.39x oversubscribed

Note: 30% peak load factor assumes not all devices transmit simultaneously (realistic for IoT workloads with staggered reporting).

853.6.4 OFDMA Airtime Improvement

Wi-Fi 6 OFDMA enables simultaneous transmissions - multiple devices share each transmission opportunity (TXOP). Assuming 4 devices per TXOP scheduled via Target Wake Time:

Device Type	Devices	TXOPs/sec	TX Time	Wi-Fi 6 Airtime	Wi-Fi 5 Airtime	Improvement
Vibration sensors	20	500	170.38 us	8.52%	26.4%	3.1x better
Temperature sensors	15	0.4	170.38 us	0.02%	0.02%	Same
Cameras	10	200	170.38 us	3.41%	9.05%	2.7x better
AGV robots	5	200	170.38 us	3.41%	7.11%	2.1x better
TOTAL	50			15.36%	42.58%	2.77x better

853.6.5 OFDMA Transmission Time Breakdown

DIFS: 28 us
Backoff: 67.5 us
Parallel TX (4 devices): 50.88 us (vs 528.88 us sequential in Wi-Fi 5)
SIFS + ACK: 24 us
Total: 170.38 us per multi-user transmission

Wi-Fi 6 OFDMA Result: 15.4% airtime utilization (vs 42.6% Wi-Fi 5)

Improvement: 2.77x better airtime efficiency

Benefits:

Lower Latency: Less queue delay (15% vs 43% utilization)
Higher Capacity: Can support 2.77x more devices
Better Coexistence: More airtime available for non-IoT traffic (laptops, phones)

853.7 Wi-Fi 6 TWT (Target Wake Time) Power Savings

853.7.1 TWT Overview

Wi-Fi 6 introduces Target Wake Time (TWT), allowing AP to schedule when devices wake up and transmit. This eliminates:

Random backoff contention (saves power waiting for transmission opportunity)
Frequent beacon listening (wake only at scheduled time)

853.7.2 Temperature Sensor Power Analysis

Without TWT (Wi-Fi 5):

Device wakes every 10 seconds to transmit 100 bytes. Energy components:

Component	Duration	Current	Energy	Notes
Beacon listening	100 ms	100 mA	0.00278 mAh	Must maintain association
Channel contention	67.5 us	100 mA	0.0000019 mAh	Random backoff
Transmit	4 us	240 mA	0.00000027 mAh	100 bytes @ 200 Mbps
ACK wait	24 us	100 mA	0.00000067 mAh	Frame acknowledgment
Sleep	9.9 s	10 uA	0.0000275 mAh	Deep sleep mode
Total per cycle	10 s		0.00281 mAh	Beacon dominates (99%)

Daily Energy (Without TWT):

Cycles per day: 8,640 (every 10 seconds)
Daily energy: 0.00281 x 8,640 = 24.28 mAh/day
Battery life (CR123A 1500 mAh): 61.8 days

853.7.3 With TWT (Wi-Fi 6)

AP schedules device to wake at exact 10-second intervals. Device wakes, transmits immediately, returns to sleep:

Component	Duration	Current	Energy	Notes
Beacon listening	0	0	0	Eliminated - scheduled wake
Channel contention	0	0	0	Eliminated - scheduled TX
Transmit	4 us	240 mA	0.00000027 mAh	Same as Wi-Fi 5
ACK wait	24 us	100 mA	0.00000067 mAh	Same as Wi-Fi 5
Sleep	10 s	10 uA	0.0000278 mAh	Deep sleep mode
Total per cycle	10 s		0.00002874 mAh	97.9x less than Wi-Fi 5

TWT Takeaway for IoT

Target Wake Time (TWT) can let compatible clients sleep longer by aligning wake windows, which can reduce idle listening and contention for scheduled, low-duty-cycle sensors. The actual battery-life impact depends on DTIM/beacon settings, whether the device stays associated, retry rate, and the module’s true sleep current. Treat large “x improvement” claims as workload/device dependent - validate with datasheet currents and a bench measurement.

Show code

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  container.className = 'inline-kc-container';

  if (typeof InlineKnowledgeCheck !== 'undefined') {
    container.appendChild(InlineKnowledgeCheck.create({
      question: "TWT eliminates beacon listening for scheduled sensors. Why does this have such a dramatic impact on battery life (potential 98x improvement in the analysis)?",
      options: [
        {text: "Beacon frames are very large and consume significant transmission energy", correct: false, feedback: "Incorrect. Beacon frames are small (~200 bytes). The issue isn't beacon size but the frequency and duration of listening."},
        {text: "Without TWT, devices must wake every 100ms to listen for beacons, consuming 99% of the energy budget", correct: true, feedback: "Correct! Standard Wi-Fi requires devices to wake every DTIM interval (often 100ms) to check for buffered data. This 100ms @ 100mA listening dominates the energy budget. TWT eliminates this by scheduling exact transmission times - devices sleep until their scheduled slot with no beacon listening required."},
        {text: "TWT uses more efficient modulation that requires less transmit power", correct: false, feedback: "Incorrect. TWT doesn't change modulation. It changes when devices wake up, eliminating unnecessary beacon listening."},
        {text: "TWT compresses beacon data so devices receive information faster", correct: false, feedback: "Incorrect. TWT eliminates the need for beacon listening entirely for scheduled devices, rather than making beacons more efficient."}
      ],
      difficulty: "medium",
      topic: "wifi-twt"
    }));
  }
  return container;
}

853.8 Channel Planning and AP Density Recommendation

Treat Wi-Fi 6 planning as a measurement-driven loop rather than a single “coverage radius” calculation:

853.8.1 Planning Process

Define requirements: device count, traffic model (bursty vs periodic), latency/jitter, roaming, and power constraints
Choose band and channel width:
- Prefer narrower channels when you need many APs and reuse (reduces co-channel contention)
- Account for regional channel availability and DFS behavior when planning 5 GHz (and 6 GHz if available)
AP density and transmit power:
- More APs at lower transmit power can improve spatial reuse and reduce contention, but increases deployment complexity
Backhaul strategy:
- Prefer wired uplinks; if using mesh, minimize wireless hops and avoid sharing client/backhaul radios where possible
Wi-Fi 6 features:
- OFDMA can help under contention when APs and clients support it
- TWT can reduce idle listening for scheduled sensors, but savings are workload/device dependent

853.8.2 Validation Checklist

Measure airtime utilization, retries, and latency/jitter under realistic load
Verify roaming behavior (802.11r/k/v) if devices move
Run a pilot, then iterate placement, channel plan, and power settings

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graph TB
    subgraph Factory["10,000 m² Factory Floor"]
        subgraph Zone1["Zone 1"]
            AP1["AP1<br/>Ch 36"]
        end
        subgraph Zone2["Zone 2"]
            AP2["AP2<br/>Ch 44"]
        end
        subgraph Zone3["Zone 3"]
            AP3["AP3<br/>Ch 52"]
        end
        subgraph Zone4["Zone 4"]
            AP4["AP4<br/>Ch 60"]
        end
        subgraph Zone5["Zone 5"]
            AP5["AP5<br/>Ch 100"]
        end
        subgraph Zone6["Zone 6"]
            AP6["AP6<br/>Ch 108"]
        end
    end

    Zone1 --- Zone2
    Zone2 --- Zone3
    Zone4 --- Zone5
    Zone5 --- Zone6
    Zone1 --- Zone4
    Zone2 --- Zone5
    Zone3 --- Zone6

Figure 853.1: Channel planning for 10,000 m2 factory with 6 Wi-Fi 6 APs using non-overlapping 5 GHz channels in checkerboard pattern.

853.9 Summary

This analysis demonstrated Wi-Fi 6 advantages for high-density IoT deployments:

Throughput vs Airtime: Low throughput utilization (3.7%) can mask high airtime utilization (42.6%)
OFDMA Efficiency: Parallel transmission reduces airtime utilization from 42.6% to 15.4% (2.77x improvement)
Resource Units: Even smallest RUs (26-tone, 2 MHz) provide orders of magnitude more bandwidth than sensors need
TWT Power Savings: Eliminating beacon listening can dramatically extend battery life for scheduled sensors
Planning Approach: Measurement-driven iteration beats single-pass coverage calculations

853.10 What’s Next

Return to Wi-Fi Review: Summary and Visual Gallery for a comprehensive chapter summary and visual reference gallery covering all Wi-Fi concepts, or continue to Bluetooth Fundamentals to explore low-power wireless technology for personal area networks.