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  2. Shannon–Hartley theorem - Wikipedia

    en.wikipedia.org/wiki/ShannonHartley_theorem

    In the channel considered by the ShannonHartley theorem, noise and signal are combined by addition. That is, the receiver measures a signal that is equal to the sum of the signal encoding the desired information and a continuous random variable that represents the noise. This addition creates uncertainty as to the original signal's value.

  3. Channel capacity - Wikipedia

    en.wikipedia.org/wiki/Channel_capacity

    An application of the channel capacity concept to an additive white Gaussian noise (AWGN) channel with B Hz bandwidth and signal-to-noise ratio S/N is the ShannonHartley theorem: C = B log 2 ⁡ ( 1 + S N ) {\displaystyle C=B\log _{2}\left(1+{\frac {S}{N}}\right)\ }

  4. Template:Information theory - Wikipedia

    en.wikipedia.org/wiki/Template:Information_theory

    Download as PDF; Printable version; In other projects ... Shannon's source coding theorem; Channel capacity; Noisy-channel coding theorem; ShannonHartley theorem;

  5. Noisy-channel coding theorem - Wikipedia

    en.wikipedia.org/wiki/Noisy-channel_coding_theorem

    The channel capacity can be calculated from the physical properties of a channel; for a band-limited channel with Gaussian noise, using the ShannonHartley theorem. Simple schemes such as "send the message 3 times and use a best 2 out of 3 voting scheme if the copies differ" are inefficient error-correction methods, unable to asymptotically ...

  6. Computer engineering compendium - Wikipedia

    en.wikipedia.org/wiki/Computer_engineering...

    Download as PDF; Printable version; In other projects Wikidata item; Appearance. ... Channel capacity ShannonHartley theorem Nyquist–Shannon sampling theorem

  7. Shannon capacity - Wikipedia

    en.wikipedia.org/wiki/Shannon_capacity

    Download as PDF; Printable version; In other projects ... move to sidebar hide. Shannon capacity may mean Channel capacity, the capacity of a channel in ...

  8. Information theory - Wikipedia

    en.wikipedia.org/wiki/Information_theory

    the mutual information, and the channel capacity of a noisy channel, including the promise of perfect loss-free communication given by the noisy-channel coding theorem; the practical result of the ShannonHartley law for the channel capacity of a Gaussian channel; as well as; the bit—a new way of seeing the most fundamental unit of information.

  9. Shaping codes - Wikipedia

    en.wikipedia.org/wiki/Shaping_codes

    C is the channel capacity in bits per second; B is the bandwidth of the channel in hertz; S is the total signal power over the bandwidth and N is the total noise power over the bandwidth. S/N is the signal-to-noise ratio of the communication signal to the Gaussian noise interference expressed as a straight power ratio (not as decibels).