A stack-based sequential priority first decoder that returns Maximum-Likelihood solutions to M-QAM modulated MIMO system-type problems, i.e., a lattice decoder with optional justified rectangular boundary control. In such problems, the depth of the search tree is known and the number of children per node is also fixed.
Real or complex inputs are permitted; in practice, this implementation of the sequential decoding algorithm is near-ML because it operates with finite memory. If that memory is exceeded, nodes are dropped from the stack and the number of such dropped nodes is returned.
In addition, this implementation allows specification of the size of the finite memory block (in terms of number of nodes) allocated for its execution. Generally we find that restricting the sequential decoder to finite memory is not a major consideration, as very near-ML performance can be achieved with relatively low allocations.
Please see the function pre-amble (type `help Talg_det` at the Matlab prompt) for more details.