qampy.core.equalisation

Adaptive equaliser functions

Functions for adaptive equalisation and filer application.

The following adaptive equaliser methods are provided:

Non-decision based:

• Constant Modulus Algorithm (CMA) after [1]

• Radius Directed Error (RDE) [1]

• Modfied Constant Modulus Algorithm (MCMA) after [2]

• Modified Radius Directed Error (MRDA) after [3]

• Signed based CMA [1]

Decision based:

• Symbol Based Decision (SBD) after [3]

• Modified Decision Directed Modulus Algorithm (MDDMA) after [4]

• Decision-directed LMS after [1]

Adaptive Step Size Algorithms:

In addition algorithms can use a adaptive stepsize based on the step size adoption in [5] It is possible to use an adaptive step for all equalisers using the adaptive_stepsize keyword parameter

Real-valued equalisers:

A subset of the equalisers can be run as real-valued equalisers.

Data aided:

In addition there is a data aided SBD algorithm and real_valued DD algorithm

The available methods are specified in the following sets

qampy.core.equalisation.DATA_AIDED = ('dd_data_real', 'sbd_data')

Data-aided equalisation methods

qampy.core.equalisation.TRAINING_FCTS = ('sbd', 'mddma', 'dd', 'sbd_data', 'dd_real', 'dd_data_real', 'cma', 'cma2', 'mcma', 'rde', 'mrde', 'cma_real', 'sgncma_real', 'sgncma')

All available equaliser method#:

qampy.core.equalisation.NONDECISION_BASED = ('cma', 'cma2', 'mcma', 'rde', 'mrde', 'cma_real', 'sgncma_real', 'sgncma')

Non-decision based equalisation methods

qampy.core.equalisation.DECISION_BASED = ('sbd', 'mddma', 'dd', 'sbd_data', 'dd_real', 'dd_data_real')

Decision based equalisation methods

qampy.core.equalisation.REAL_VALUED = ('cma_real', 'dd_real', 'dd_data_real', 'sgncma_real')

Real-valued equalisation methods

References

[1] (1,2,3,4)

Faruk, M.S. and Savory, J.S. “Digital Signal Processing for Coherent Transceivers Employing Multilevel Formats”, Journal of Lightwave Technology, vol. 35, no. 5, pp. 1125–1141, Mar. 2017, doi: 10.1109/JLT.2017.2662319.

[2]

Oh, K. N., & Chin, Y. O. “Modified constant modulus algorithm: blind equalization and carrier phase recovery algorithm”. Proceedings IEEE International Conference on Communications ICC ’95, 1, 498–502 (1995) http://doi.org/10.1109/ICC.1995.525219

[3] (1,2)

Filho, M., Silva, M. T. M., & Miranda, M. D. “A FAMILY OF ALGORITHMS FOR BLIND EQUALIZATION OF QAM SIGNALS”. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6–9) (2011).

[4]

Fernandes, C. A. R., Favier, G., & Mota, J. C. M. “Decision directed adaptive blind equalization based on the constant modulus algorithm. Signal, Image and Video Processing”, 1(4), 333–346. (2007) http://doi.org/10.1007/s11760-007-0027-2

[5]

Ashmawy, D., Banovic, K., Abdel-Raheem, E., Youssif, M., Mansour, H., and Mohanna, M. “Joint MCMA and DD blind equalization algorithm with variable-step size,” Proc. 2009 IEEE Int. Conf. Electro/Information Technol. EIT 2009, no. 1, pp. 174–177, 2009.

Module Attributes

DECISION_BASED	Decision based equalisation methods
NONDECISION_BASED	Non-decision based equalisation methods
REAL_VALUED	Real-valued equalisation methods
DATA_AIDED	Data-aided equalisation methods
TRAINING_FCTS	All available equaliser method#:

Functions

apply_filter(E, os, wxy[, method, modes])	Apply the equaliser filter taps to the input signal.
dual_mode_equalisation(E, os, mu, M[, wxy, ...])	Blind equalisation of PMD and residual dispersion, with a dual mode approach.
equalise_signal(E, os, mu, M[, wxy, Ntaps, ...])	Blind equalisation of PMD and residual dispersion, using a chosen equalisation method.