A signal detection method based on adversarial learning under unknown channel model

A technology of signal detection and channel model, applied in the field of signal detection

Active Publication Date: 2021-10-19
XI AN JIAOTONG UNIV
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Problems solved by technology

However, in some new communication systems, such as underwater acoustic communication, molecular communication, etc., or in traditional systems with complex channel conditions, it is difficult to establish an effective model describing signal propagation, and it is impossible to use traditional signal detection algorithms for signal detection.

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  • A signal detection method based on adversarial learning under unknown channel model
  • A signal detection method based on adversarial learning under unknown channel model
  • A signal detection method based on adversarial learning under unknown channel model

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Embodiment Construction

[0045] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0046] The signal detection method based on adversarial learning under the unknown channel model of the present invention, the sequence transmitted by the communication system is composed of N symbols, is the transmitted symbol at time i, i∈{1,2,...,N}, is a discrete signal set containing m signal constellation points. The channel input at the i-th moment is the state vector Let y[i] represent the channel output at time i, then y[i] is the random mapping of the channel input state vector S[i], l is the memory length of the channel, assuming it is less than the length of the data sequence, that is: l The corresponding channel output can be expressed as The communication system signal detection method includes the following steps:

[0047] 1) Send a pilot symbol sequence with a length of K, where at the i-th moment, send symbol x[i] from the signal set ...

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Abstract

The invention discloses a signal detection method based on adversarial learning under an unknown channel model. The steps include: sending a pilot sequence to collect a data set, then initializing a generative adversarial network, including a generator G and a discriminator D, and then using the data The set-pair generative confrontation network is iteratively trained against the network. After the training is completed, the trained generator G is used to generate the channel transition probability, and it is used in the Viterbi algorithm to realize the unknown channel model condition that cannot be achieved by traditional methods. Signal Detection.

Description

technical field [0001] The invention relates to the technical field of signal detection, in particular to a signal detection method based on adversarial learning under an unknown channel model. Background technique [0002] In modern digital communication systems, signal detection is an important component. The signal is encoded and modulated at the sending end, and then sent to the receiving end after passing through the channel. At this time, the signal received by the receiving end is a signal that has undergone noise interference or intersymbol interference, so it is necessary to use signal detection to detect the received signal. The signal is judged, and the traditional signal detection algorithm relies on the statistical model of the channel. In wireless communication, electromagnetic waves are used to transmit information, and the propagation mechanism can be described by Maxwell's equations, so mathematical formulas can be used to establish a probability and statis...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B17/309H04B17/391G06K9/62G06N3/04G06N3/08
CPCH04B17/309H04B17/391G06N3/08G06N3/045G06F18/214
Inventor 孙黎王宇威
Owner XI AN JIAOTONG UNIV
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