A Highly Parallelized MIMO Detector for Vector-Based Reconfigurable Architectures

This paper presents a highly parallelized MIMO signal detection algorithm targeting vector-based reconfigurable architectures. The detector achieves high data-level parallelism and near-ML performance by adopting a vector-architecture-friendly technique - parallel node perturbation. To further reduce the computational complexity, imbalanced node and successive partial node expansion schemes in conjunction with sorted QR decomposition are applied. The effectiveness of the proposed algorithm is evaluated by simulations performed on a simplified 4x4 MIMO LTE-A testbed and operation analysis. Compared to the K-Best detector and fixed-complexity sphere decoder (FSD), the number of visited nodes in the proposed algorithm is reduced by 15 and 1.9 times respectively, with less than 1dB performance degradation. Benefiting from the fully deterministic non-iterative dataflow structure, reconfiguration rate is 95% less than that of the K-Best detector and 17% less than the case of FSD.