Soft-decision decoding
AssigneeAirbus Defence and Space GmbH
InventorTerence Dodgson
Life of this patent
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Abstract
A method of soft-decision decoding including training a machine learning agent with communication signal training data; providing to the trained machine learning agent a signal that has been received via a communications channel; operating the machine learning agent to determine respective probabilities that the received signal corresponds to each of a plurality of symbols; and, based on the determined probabilities, performing soft decision decoding on the received signal.
Claims19
A method of soft-decision decoding, the method comprising:
- training a machine learning agent with communication signal training data;
- providing to the trained machine learning agent a signal that has been received via a communications channel;
- operating the machine learning agent to determine respective probabilities that the received signal corresponds to each of a plurality of symbols;
- and
- based on the determined respective probabilities, performing soft decision decoding on the received signal,
- wherein operating the machine learning agent comprises determining a normalized probability pair for each symbol bit, a normalized probability pair comprising a first probability that a respective symbol bit is a zero and a second probability that the respective symbol bit is a one, and
- wherein the first probability and the second probability of the normalized probability pair sum to 1.
An apparatus for use in soft-decision decoding, the apparatus being configured to:
- train a machine learning agent with communication signal training data;
- provide to the trained machine learning agent a signal that has been received via a communications channel;
- operate the machine learning agent to determine respective probabilities that the received signal corresponds to each of a plurality of symbols;
- and
- based on the determined respective probabilities, perform soft decision decoding on the received signal,
- wherein operating the machine learning agent comprises determining a normalized probability pair for each symbol bit, a normalized probability pair comprising a first probability that a respective symbol bit is a zero and a second probability that the respective symbol bit is a one, and
- wherein the first probability and the second probability of the normalized probability pair sum to 1.
A non-transitory computer-readable storage media comprising a set of instructions which, when executed by a computerized device, cause the computerized device to perform a method of soft-decision decoding, the method comprising:
- training a machine learning agent with communication signal training data;
- providing to the trained machine learning agent a signal that has been received via a communications channel;
- operating the machine learning agent to determine respective probabilities that the received signal corresponds to each of a plurality of symbols;
- and
- based on the determined respective probabilities, performing soft decision decoding on the received signal,
- wherein operating the machine learning agent comprises determining a normalized probability pair for each symbol bit, a normalized probability pair comprising a first probability that a respective symbol bit is a zero and a second probability that the respective symbol bit is a one, and
- wherein the first probability and the second probability of the normalized probability pair sum to 1.
Dependencies are inferred from the claim text.