Shannon Channel Capacity
Maximum error-free data rate of a noisy channel.
Formula
Variables
Example
Fundamental limit of communication
How it works
Shannon’s channel capacity gives the highest theoretical bit rate that can be sent over a noisy communication link with arbitrarily low error if the right coding scheme is used. It grows linearly with bandwidth, but only logarithmically with signal-to-noise ratio, which means doubling bandwidth helps more directly than trying to endlessly boost power. The log term appears because information is limited by how reliably the receiver can distinguish signal variations from noise; once noise is present, each extra increase in SNR yields diminishing returns.
When to use it
Use this when estimating the ultimate throughput of a radio link, fiber-optic channel, Wi‑Fi system, or any digital communication channel under additive noise. It is especially useful for comparing whether a design problem should be solved by increasing bandwidth, improving SNR, or both.
Worked example, step by step
Given: bandwidth B = 8 MHz, signal-to-noise ratio SNR = 24 (linear, not dB) Step 1: Write Shannon capacity formula: C = B log2(1 + SNR) Step 2: Substitute values: C = 8 × 10^6 × log2(1 + 24) Step 3: Simplify inside the log: C = 8 × 10^6 × log2(25) Step 4: Evaluate the logarithm: log2(25) ≈ 4.644 Step 5: Multiply: C ≈ 8 × 10^6 × 4.644 = 37.152 × 10^6 bits/s Final answer: C ≈ 37.2 Mb/s
Common mistakes to avoid
- Using SNR in decibels directly instead of converting it to a linear ratio first
- Forgetting that B must be in Hz if you want C in bits per second
- Treating Shannon capacity as a guaranteed real-world data rate rather than an ideal upper bound
Did You Know?
Shannon's capacity theorem sets the absolute speed limit for reliable communication over any noisy channel.
Share this formula
More in Computer Science
View allShannon Entropy
AdvancedAverage information content of a random source (bits).
Master Theorem (form)
AdvancedAnalyzes running time of divide-and-conquer algorithms.
Hamming Distance
AdvancedNumber of positions at which two strings differ.
Bits to Represent n
BasicMinimum number of bits to represent n distinct values.
Binary to Decimal
BasicConverts a binary number to decimal.
Nodes in a Full Binary Tree
IntermediateTotal nodes in a perfect binary tree of height h.