In WiFi network design, signal strength is measured in dBm (decibel-milliwatts), with values ranging from 0 (strongest) to -100 (weakest). The industry has established practical thresholds for reliable connectivity:

• -30 dBm to -50 dBm: Excellent signal (near perfect connectivity)
• -50 dBm to -65 dBm: Good signal (reliable for most applications)
• -65 dBm to -75 dBm: Fair signal (basic connectivity, may experience slowdowns)
• -75 dBm to -85 dBm: Poor signal (unstable connections, frequent drops)
• Below -85 dBm: Unusable connection

Modern 802.11 standards have different sensitivity requirements:

# Python example: Checking signal quality thresholds
def check_signal_quality(rssi, protocol):
    thresholds = {
        '802.11ax': -70,  # WiFi 6
        '802.11ac': -72,  # WiFi 5
        '802.11n': -75,   # WiFi 4
        '802.11g': -80
    }
    
    if rssi >= thresholds.get(protocol, -75):
        return "Reliable connection"
    else:
        return "Marginal connection"

For office network design, we recommend:

1. Design for -65 dBm minimum coverage in primary work areas
2. Maintain -70 dBm or better for high-density zones
3. Use -67 dBm as handoff threshold between access points

Here's how to programmatically analyze WiFi coverage using Python:

import numpy as np

def analyze_coverage(signal_map):
    """Analyze WiFi signal strength heatmap"""
    signal_array = np.array(signal_map)
    
    # Calculate coverage metrics
    excellent_coverage = np.sum(signal_array >= -55) / signal_array.size
    good_coverage = np.sum(signal_array >= -65) / signal_array.size
    poor_areas = np.sum(signal_array < -75) / signal_array.size
    
    return {
        'excellent_coverage': excellent_coverage,
        'good_coverage': good_coverage,
        'poor_areas': poor_areas
    }

2.4GHz vs 5GHz signal propagation varies significantly:

When designing enterprise WiFi networks, the most critical metric is Received Signal Strength Indicator (RSSI) measured in dBm. The industry generally considers these thresholds for different use cases:

• -50 dBm to -60 dBm: Excellent signal (4-5 bars)
• -60 dBm to -70 dBm: Good signal (3-4 bars)
• -70 dBm to -80 dBm: Minimum for reliable connectivity (2-3 bars)
• -80 dBm to -90 dBm: Unstable connection (may work for basic IoT)
• Below -90 dBm: Connection drops likely

Modern protocols have different sensitivity requirements:

# Python example for protocol thresholds
protocol_requirements = {
    '802.11b': -80,  # Minimum for 1Mbps
    '802.11g': -76,
    '802.11n': -70,  # HT40 requires stronger signal
    '802.11ac': -65, # For VHT80 operation
    '802.11ax': -62  # For OFDMA efficiency
}

For office network design, we recommend:

1. Aim for -67 dBm minimum at all work areas
2. Maintain -55 dBm in high-density zones
3. Use this JavaScript snippet for signal validation:

function validateCoverage(rssiMap) {
  const MIN_STRENGTH = -70;
  const CRITICAL_AREAS = ['conference', 'workstation'];
  
  return Object.entries(rssiMap).map(([area, strength]) => ({
    area,
    strength,
    status: strength >= MIN_STRENGTH || 
           (CRITICAL_AREAS.includes(area) && strength >= -65)
  }));
}

Professional WiFi planning tools typically use these algorithms:

// Sample path loss calculation (simplified)
function calculatePathLoss(frequencyMHz, distanceMeters) {
  const freeSpaceLoss = 20 * Math.log10(distanceMeters) + 
                       20 * Math.log10(frequencyMHz) - 27.55;
  return freeSpaceLoss;
}

Remember that 5GHz signals attenuate faster than 2.4GHz due to higher frequency, requiring more APs for equivalent coverage.

For a 5,000 sqft office with 50 users:

# Bash script for AP placement validation
#!/bin/bash
MIN_COVERAGE=-70
AP_LOCATIONS=("north" "south" "east" "west" "center")

for ap in "${AP_LOCATIONS[@]}"; do
  signal_strength=$(iwconfig wlan0 | grep -i signal | awk '{print $4}')
  if [[ ${signal_strength//[^0-9]/} -lt ${MIN_COVERAGE//-/} ]]; then
    echo "Warning: $ap AP needs adjustment (Current: $signal_strength)"
  fi
done