Multiple input multiple output (MIMO) symbol detection problem belongs to non-deterministic polynomial acceptable hard combinatorial optimization (CO) class. One of the key trials in design of MIMO scheme is to develop a low complexity detection algorithm without much compromise in performance. Detection approaches proposed in the literature can be split into non-linear and linear algorithms. Vertical Bell-Labs Layered Space Time (V-BLAST) and Sphere Decoder (SD) are non-linear methods used for extracting transmitted data; whereas, Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) detections are comparatively in complex and effectual linear techniques. In this work, the heuristic 1-Opt local search method used for solving computationally hard combinatorial optimization problems is applied to the ZF and MMSE detection algorithms. First, simple MIMO decoding using ZF and MMSE is accomplished to find the estimated symbol then the transmitted symbol is calculated using the heuristic 1-opt approach by means of the estimated symbol. Simulation results demonstrate that 1-opt search when applied to the ZF and MMSE displays better bit error rate (BER) performance than the simple ZF and MMSE detectors. It is also verified through simulations that the proposed 1-Opt based detectors display better BER performance as compared to the complex nonlinear VBLAST and SD at considerably reduced complexity. Hence, the proposed detectors are appropriate for effective hardware implementations.
Bibliographical noteFunding Information:
This article was funded by Electricity Generating Authority of Thailand (Grant No. GGR010100089000).
© 2020, The Korean Institute of Electrical Engineers.
- Maximum likelihood (ML) detector