MIMO hard ML detectors and soft-output detectors accelerated through box optimization

Efficient MIMO ML/MAP detectors through Box optimization

Box optimization is a technique that can be used to speed up Maximum Likehood MIMO detection. The technique is described in the paper quoted below. This zip file contains several implementations of hard and soft MIMO detectors, along with small test files.
All of them use the box optimization techniques described in the papers detailed below. You can find detailed information in these papers.
V.M. Garcia-Molla, A.Vidal, A.Gonzalez, S.Roger, Improved Maximum
Likelihood Detection through Sphere Decoding combined with Box
Optimization, Signal Processing, Elsevier 98 (2014) 287--294.
Victor M. Garcia-Molla, M. Angeles Simarro, F.J. Martínez-Zaldívar, Alberto González, Antonio M. Vidal,
Maximum likelihood soft-output detection through Sphere Decoding combined with box optimization,
Signal Processing, Elsevier 125 (2016) 249–-260
doi:10.1016/j.sigpro.2016.02.006
The software makes use of some functions of the Matlab communications toolbox, such as qammod.
However its use is not crucial and they could be easily replaced by user-written equivalent routines.
The package is supplied "as is", without any accompanying support services, maintenance, or future updates.
We make no warranties, explicit or implicit, that the software contained in this package is free of error
or that it will meet your requirements for any particular application. It should not be relied on for any purpose
where incorrect results could result in loss of property, personal injury, liability or whatsoever. If you do
use our software for any such purpose, it is at your own risk. The authors disclaim all liability of any kind,
either direct or consequential, resulting from your use of these programs.

Each detector comes in a self-contained folder, including a simple test program. Please note that the soft detectors
make use of the hard detector BOHD.