commpy.channelcoding.Trellis

class Trellis(memory, g_matrix, feedback=0, code_type='default')

Class defining a Trellis corresponding to a k/n - rate convolutional code.

Parameters :

memory : 1D ndarray of ints

Number of memory elements per input of the convolutional encoder.

g_matrix : 2D ndarray of ints (octal representation)

Generator matrix G(D) of the convolutional encoder. Each element of G(D) represents a polynomial.

feedback : int, optional

Feedback polynomial of the convolutional encoder. Default value is 00.

code_type : {‘default’, ‘rsc’}, optional

Use ‘rsc’ to generate a recursive systematic convolutional code.

If ‘rsc’ is specified, then the first ‘k x k’ sub-matrix of

G(D) must represent a identity matrix along with a non-zero feedback polynomial.

Examples

>>> from numpy import array
>>> import commpy.channelcoding.convcode as cc
>>> memory = array([2])
>>> g_matrix = array([[05, 07]]) # G(D) = [1+D^2, 1+D+D^2]
>>> trellis = cc.Trellis(memory, g_matrix)
>>> print trellis.k
1
>>> print trellis.n
2
>>> print trellis.total_memory
2
>>> print trellis.number_states
4
print trellis.number_inputs
2
>>> print trellis.next_state_table
[[0 2]
 [0 2]
 [1 3]
 [1 3]]
>>>print trellis.output_table
[[0 3]
 [3 0]
 [1 2]
 [2 1]]

Attributes

Methods

visualize([trellis_length, state_order, ...])

Plot the trellis diagram.

__init__(memory, g_matrix, feedback=0, code_type='default')

Methods

__init__(memory, g_matrix[, feedback, code_type])
visualize([trellis_length, state_order, ...])	Plot the trellis diagram.