Power-constrained Gaussian channel capacity
For Y=X+Z with iid real Gaussian noise Z∼N(0,N), N>0, independent of input, and average input power E[X²]≤P, capacity is C=(1/2)log₂(1+P/N) bits per channel use.
Key knowledge
For Y=X+Z with iid real Gaussian noise Z∼N(0,N), N>0, independent of input, and average input power E[X²]≤P, capacity is C=(1/2)log₂(1+P/N) bits per channel use. Gaussian input attains the information maximum.
Conditions and scope
- This is a real discrete-time memoryless channel with P≥0.
- Capacity is an asymptotic reliability limit, not a guaranteed rate for a finite code or a bits-per-second value without bandwidth accounting.
Sources · 1
References
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Evidence and review
Lecture 14 sections 14.3.1–14.3.2 power constraint and capacity theorem; corroborated by Lecture 15 maximization.
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