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  2. Channel capacity - Wikipedia

    en.wikipedia.org/wiki/Channel_capacity

    Channel capacity, in electrical engineering, computer science, and information theory, is the theoretical maximum rate at which information can be reliably transmitted over a communication channel.

  3. Error correction code - Wikipedia

    en.wikipedia.org/wiki/Error_correction_code

    Low-density parity-check (LDPC) codes are a class of highly efficient linear block codes made from many single parity check (SPC) codes. They can provide performance very close to the channel capacity (the theoretical maximum) using an iterated soft-decision decoding approach, at linear time complexity in terms of their block length.

  4. Shannon–Hartley theorem - Wikipedia

    en.wikipedia.org/wiki/Shannon–Hartley_theorem

    What is the channel capacity for a signal having a 1 MHz bandwidth, received with a SNR of −30 dB ? That means a signal deeply buried in noise. −30 dB means a S/N = 10 −3. It leads to a maximal rate of information of 10 6 log 2 (1 + 10 −3) = 1443 bit/s. These values are typical of the received ranging signals of the GPS, where the ...

  5. Turbo code - Wikipedia

    en.wikipedia.org/wiki/Turbo_code

    The first sub-block is the m-bit block of payload data. The second sub-block is n/2 parity bits for the payload data, computed using a recursive systematic convolutional code (RSC code). The third sub-block is n/2 parity bits for a known permutation of the payload data, again computed using an RSC code. Thus, two redundant but different sub ...

  6. Low-density parity-check code - Wikipedia

    en.wikipedia.org/wiki/Low-density_parity-check_code

    As with other codes, the maximum likelihood decoding of an LDPC code on the binary symmetric channel is an NP-complete problem, [24] shown by reduction from 3-dimensional matching. So assuming P != NP , which is widely believed, then performing optimal decoding for an arbitrary code of any useful size is not practical.

  7. Noisy-channel coding theorem - Wikipedia

    en.wikipedia.org/wiki/Noisy-channel_coding_theorem

    In information theory, the noisy-channel coding theorem (sometimes Shannon's theorem or Shannon's limit), establishes that for any given degree of noise contamination of a communication channel, it is possible (in theory) to communicate discrete data (digital information) nearly error-free up to a computable maximum rate through the channel.

  8. List of interface bit rates - Wikipedia

    en.wikipedia.org/wiki/List_of_interface_bit_rates

    The figures below are simplex data rates, which may conflict with the duplex rates vendors sometimes use in promotional materials. Where two values are listed, the first value is the downstream rate and the second value is the upstream rate. The use of decimal prefixes is standard in data communications.

  9. Information theory - Wikipedia

    en.wikipedia.org/wiki/Information_theory

    Shannon's main result, the noisy-channel coding theorem, showed that, in the limit of many channel uses, the rate of information that is asymptotically achievable is equal to the channel capacity, a quantity dependent merely on the statistics of the channel over which the messages are sent.