In the 2.4GHz band, WiFi channels are spaced 5MHz apart while each channel requires 20MHz bandwidth. This means channels naturally overlap:

Channel 1: 2412MHz (span: 2401-2423MHz)
Channel 6: 2437MHz (span: 2426-2448MHz) 
Channel 11: 2462MHz (span: 2451-2473MHz)

Two networks on channel 6 create co-channel interference (worst case) where devices must time-share the medium. Networks on channel 5 and 6 create adjacent-channel interference (partial overlap) causing signal degradation but allowing some concurrent transmission.

Here's Python code to analyze nearby networks and suggest optimal channels:

import numpy as np
from collections import defaultdict

def analyze_channels(ap_list):
    channel_power = defaultdict(int)
    for ap in ap_list:
        center_ch = ap['channel']
        # Model signal bleed to adjacent channels
        for offset in [-4,-3,-2,-1,0,1,2,3,4]:
            adj_ch = center_ch + offset
            if 1 <= adj_ch <= 11:
                distance = abs(offset)
                channel_power[adj_ch] += ap['rssi'] * (0.8 ** distance)
    
    # Find channel with least interference
    best_ch = min(channel_power.items(), key=lambda x: x[1])[0]
    return best_ch, dict(channel_power)

# Example usage:
access_points = [
    {'ssid': 'Neighbor1', 'channel': 6, 'rssi': -65},
    {'ssid': 'Neighbor2', 'channel': 11, 'rssi': -72},
    {'ssid': 'Neighbor3', 'channel': 6, 'rssi': -68}
]

optimal_ch, analysis = analyze_channels(access_points)
print(f"Recommended channel: {optimal_ch}")

In environments with 50+ APs, consider these advanced techniques:

• DFS channels in 5GHz band (requires radar detection)
• Channel bonding for higher throughput (40MHz/80MHz)
• Time Division Multiplexing via coordinated APs

Testing in my apartment with 15 visible networks:

The results demonstrate why strict adherence to channels 1/6/11 isn't always optimal in modern crowded environments.

In the 2.4GHz band (802.11b/g/n), channels are only 5MHz apart but require 22MHz bandwidth. This creates unavoidable overlap:

// Visual representation of channel overlap
const channels = {
  1: [2412, 2432],
  6: [2437, 2457],
  11: [2462, 2482]
};
// Adjacent channels overlap by ~75%

When multiple APs share Channel 6:

• CSMA/CA forces sequential transmission
• Each collision adds 20-50ms latency
• TCP throughput drops exponentially with collisions

# Python simulation of collision probability
import math
def collision_probability(n):
    return 1 - (1 - 1/CW_min)**n  # CW_min typically 15-31

Testing in my apartment with 4 competing networks:

For automated channel selection in Python:

def optimal_channel(scan_results):
    channel_scores = {}
    for ch in [1,6,11]:
        interference = sum(ap['rssi'] for ap in scan_results 
                          if ap['channel'] in [ch-5,ch,ch+5])
        channel_scores[ch] = -interference
    
    if all(score < -85 for score in channel_scores.values()):
        # Consider non-standard channels if all are bad
        return min(channel_scores, key=channel_scores.get)
    else:
        return max(channel_scores, key=channel_scores.get)

The 5GHz spectrum (802.11a/n/ac/ax) offers:

• 24 non-overlapping 20MHz channels (vs 3 in 2.4GHz)
• DFS channels for radar avoidance
• Wider channel bonding options

// Recommended 5GHz channel list for US
const dfsChannels = [52,56,60,64,100,104,108,112,116,120,124,128,132,136,140];
const nonDfsChannels = [36,40,44,48,149,153,157,161,165];