Shannon–Hartley theorem
E645105
The Shannon–Hartley theorem is a fundamental result in information theory that quantifies the maximum error-free data transmission rate over a communication channel with a given bandwidth and signal-to-noise ratio.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Shannon–Hartley theorem canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7150363 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shannon–Hartley theorem Context triple: [Nyquist theorem, relatedConcept, Shannon–Hartley theorem]
-
A.
Slepian–Wolf coding theorem
The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
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B.
noisy-channel coding theorem
The noisy-channel coding theorem is a fundamental result in information theory that establishes the maximum rate at which information can be transmitted over a noisy communication channel with arbitrarily low error using appropriate encoding schemes.
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C.
Nyquist theorem
The Nyquist theorem is a fundamental principle in signal processing that states a continuous signal can be perfectly reconstructed from its samples if it is sampled at more than twice its highest frequency component.
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D.
Fano inequality
Fano inequality is a fundamental result in information theory that provides a lower bound on the probability of classification or decoding error in terms of conditional entropy.
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E.
Shannon–Khinchin axioms
The Shannon–Khinchin axioms are a set of fundamental conditions that uniquely characterize Shannon entropy as the standard measure of information and uncertainty in probability theory and information theory.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shannon–Hartley theorem Target entity description: The Shannon–Hartley theorem is a fundamental result in information theory that quantifies the maximum error-free data transmission rate over a communication channel with a given bandwidth and signal-to-noise ratio.
-
A.
Slepian–Wolf coding theorem
The Slepian–Wolf coding theorem is a fundamental result in information theory that characterizes the limits of lossless data compression for correlated sources encoded separately but decoded jointly.
-
B.
noisy-channel coding theorem
The noisy-channel coding theorem is a fundamental result in information theory that establishes the maximum rate at which information can be transmitted over a noisy communication channel with arbitrarily low error using appropriate encoding schemes.
-
C.
Nyquist theorem
The Nyquist theorem is a fundamental principle in signal processing that states a continuous signal can be perfectly reconstructed from its samples if it is sampled at more than twice its highest frequency component.
-
D.
Fano inequality
Fano inequality is a fundamental result in information theory that provides a lower bound on the probability of classification or decoding error in terms of conditional entropy.
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E.
Shannon–Khinchin axioms
The Shannon–Khinchin axioms are a set of fundamental conditions that uniquely characterize Shannon entropy as the standard measure of information and uncertainty in probability theory and information theory.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf | channel capacity theorem ⓘ |
| appliesTo |
band-limited AWGN channel
ⓘ
communication channel ⓘ continuous-time channels ⓘ |
| assumes |
Gaussian noise distribution
ⓘ
additive white Gaussian noise ⓘ band-limited channel ⓘ ideal coding and modulation ⓘ linear time-invariant channel ⓘ perfect error-correcting codes exist at capacity ⓘ |
| bandwidthUnit | hertz ⓘ |
| category | Theorems in information theory ⓘ |
| characterizes | fundamental limit of reliable communication ⓘ |
| contrastsWith | practical system performance below capacity ⓘ |
| describes |
channel capacity
ⓘ
maximum error-free data transmission rate ⓘ |
| establishes | upper bound on reliable communication rate ⓘ |
| expressesAs | C = B log2(1 + S/N) ⓘ |
| field | information theory NERFINISHED ⓘ |
| givesFormulaFor | channel capacity in bits per second ⓘ |
| implies | error-free transmission is bounded by channel capacity ⓘ |
| influences |
coding theory
ⓘ
modulation scheme selection ⓘ spectrum efficiency analysis ⓘ |
| namedAfter |
Claude Shannon
NERFINISHED
ⓘ
Ralph Hartley NERFINISHED ⓘ |
| relatedTo |
Hartley’s law
NERFINISHED
ⓘ
Shannon’s noisy-channel coding theorem NERFINISHED ⓘ |
| relatesQuantity |
bandwidth
ⓘ
channel capacity ⓘ signal-to-noise ratio ⓘ |
| showsThat |
capacity growth with SNR is logarithmic
ⓘ
capacity increases with bandwidth ⓘ capacity increases with signal-to-noise ratio ⓘ |
| unitOfCapacity | bits per second ⓘ |
| usedFor |
estimating maximum data rate of a link
ⓘ
link budget analysis ⓘ |
| usedIn |
data transmission system design
ⓘ
digital communications ⓘ modem design ⓘ wireless communications ⓘ |
| usesSymbol |
B for bandwidth
ⓘ
C for channel capacity ⓘ N for noise power ⓘ S for signal power ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Shannon–Hartley theorem Description of subject: The Shannon–Hartley theorem is a fundamental result in information theory that quantifies the maximum error-free data transmission rate over a communication channel with a given bandwidth and signal-to-noise ratio.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.