Exact(5)
(a) Remove the input symbol from receiving symbols as a neighbor.
(b) Release such receiving symbols subsequently with exactly one remaining neighbor and recover their neighbors to the ripple. .
Release such receiving symbols subsequently with exactly one remaining neighbor and recover their neighbors to the ripple.
Similarly, the entries of r ′ = ( r 1 ′, …, r Q ′ ) T are the receiving symbols and can be detected based on a simple maximum likelihood method.
Initial step: search for receiving symbols with degree one and release them to recover their unique neighbor input symbols to a buffer, called the ripple.
Similar(55)
With received symbols increasing, PD t) increases.
Their received symbols are stacked in the vector (1).
This quadratic problem is equivalent to maximize the minimum distance between the user received symbols and corresponding decision boundaries.
Previous work[5, 6, 8] considers ML decoding to recover the source symbols from the received symbols.
The true covariance was calculated using all the received symbols and knowledge of the transmitted symbols.
Since transmitted symbols belong to a discrete alphabet, symbol demodulation can be effectively recasted as a classification problem in the space of received symbols.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com