Struct Demapping 

pub struct Demapping<'a> {
    pub log_priors: Option<&'a [f64]>,
    pub jacobian: Jacobian,
}

The a priori information and the approximation of a soft demapper (Demap::demap_with_into). With the log-priors $\lambda_{x} = \ln P(x)$ of the points $x$ of the alphabet and the variance $\sigma^{2}$ of the complex noise, the LLR of a bit of the received symbol $y$ is

$$ L(b) = \ln \sum_{x:\, b = 0} e^{\lambda_{x} - |y - x|^{2} / \sigma^{2}} - \ln \sum_{x:\, b = 1} e^{\lambda_{x} - |y - x|^{2} / \sigma^{2}}, $$

each sum exact or max-log by Demapping::jacobian. A constant added to every $\lambda_{x}$ cancels, so the log-priors need not be normalised. The default, uniform priors and the exact logarithm, is that of Demap::demap_into.

Examples

use comnum_core::traits::{Demapping, Jacobian};
let lp = [-0.5f64, -1.5];
let d = Demapping { log_priors: Some(&lp), jacobian: Jacobian::MaxLog };
assert_eq!(Demapping::default(), Demapping { log_priors: None, jacobian: Jacobian::Exact });
assert_ne!(d, Demapping::default());

Fields

log_priors: Option<&'a [f64]>

The a priori log-probabilities $\ln P(x)$, one per point in the order of the labels (probabilistic shaping); None for uniform priors, which need no table.

jacobian: Jacobian

The Jacobian logarithm of the sums over the labels: exact or max-log.

Trait Implementations

impl<'a> Clone for Demapping<'a>

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<'a> Copy for Demapping<'a>

impl<'a> Debug for Demapping<'a>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'a> Default for Demapping<'a>

fn default() -> Self

Returns the “default value” for a type.

impl<'a> PartialEq for Demapping<'a>

fn eq(&self, other: &Self) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl<'a> StructuralPartialEq for Demapping<'a>