[EN] Achieving optimal detection performance with low complexity is
one of the major challenges, mainly in multiple-input multiple-output
(MIMO) detection. This paper presents three low-complexity Soft-Output
MIMO detection algorithms
that are based mainly on Box Optimization (BO) techniques. The proposed
methods provide good performance with low computational cost using
continuous constrained optimization techniques. The rst proposed
algorithm is a non-optimal Soft-Output detector of reduced complexity.
This algorithm
has been compared with the Soft-Output Fixed Complexity (SFSD) algorithm,
obtaining lower complexity and similar performance. The two remaining
algorithms are employed in a turbo receiver, achieving the max-log
Maximum a Posteriori (MAP) performance. The two Soft-Input Soft-Output
(SISO) algorithms were proposed in a previous work for soft-output MIMO
detection. This work presents its extension for iterative decoding. The
SISO algorithms presented
are developed and compared with the SISO Single Tree Search algorithm
(STS), in terms of efficiency and computational cost. The results show
that the proposed algorithms are more efficient for high order
constellation than the STS algorithm.Simarro, MA.; García Mollá, VM.; Vidal Maciá, AM.; Martínez Zaldívar, FJ.; Gonzalez, A. (2018). Soft MIMO detection through sphere decoding and box optimization. Signal Processing. 145:48-58. https://doi.org/10.1016/j.sigpro.2017.11.010S485814
