Decoding Vertical Space-Time Codes with Least-Squares Techniques

Performance of V-BLAST Receivers Using Limited-Precision QR Decomposition

“…Although suboptimal, it is attractive because it has substantially lower complexity than optimal receivers; in addition, it can effectively exploit space diversity. Several implementations of V-BLAST have been presented in the literature since the original idea was published; some examples are presented in [5,6,7]. We choose the version of the algorithm proposed in [7], when V-BLAST is considered as if it were a least-squares algorithm to solve linear systems of equations called LS-BLAST.…”

“…Several implementations of V-BLAST have been presented in the literature since the original idea was published; some examples are presented in [5,6,7]. We choose the version of the algorithm proposed in [7], when V-BLAST is considered as if it were a least-squares algorithm to solve linear systems of equations called LS-BLAST. The Thin-QR decomposition is used to reduce the computational complexity of the problem; it is used to calculate the MPPI.…”

“…In the second version of V-BLAST we use the Modified Gram-Schmidt algorithm, therefore we denote this version as MGSV-BLAST. All simulations in [7] were done with double precision (Floating-Point Arithmetic) on 32-bit x86 Intel processors and statistics for BER were obtained for each implementation. When designers model at a high level, floating point numbers are useful to model arithmetic operations.…”