Algorithm and Architecture of a Low-Complexity and High-Parallelism Preprocessing-Based K-Best Detector for Large-Scale MIMO Systems

As a branch of sphere decoding, the K-best method has played an important role in detection in large-scale multiple-input-multiple-output (MIMO) systems. However, as the numbers of users and antennas grow, the preprocessing complexity increases significantly, which is one of the major issues with the K-best method. To address this problem, this paper proposes a preprocessing algorithm combining Cholesky sorted QR decomposition and partial iterative lattice reduction (CHOSLAR) for K-best detection in a 64-quadrature amplitude modulation (QAM) 16 x 16 MIMO system. First, Cholesky decomposition is conducted to perform sorted QR decomposition. Compared with conventional sorted QR decomposition, this method reduces the number of multiplications by 25.1% and increases parallelism. Then, a constant-throughput partial iterative lattice reduction method is adopted to achieve near-optimal detection accuracy. This method further increases parallelism, reduces the number of matrix swaps by 45.5%, and reduces the number of multiplications by 67.3%. Finally, a sorting-reduced K-best strategy is used for vector estimation, thereby, reducing the number of comparators by 84.7%. This method suffers an accuracy loss of only approximately 1.44 dB compared with maximum likelihood detection. Based on CHOSLAR, this paper proposes a fully pipelined very-large-scale-integration architecture. A series of different systolic arrays and parallel processing units achieves an optimal tradeoff among throughput, area consumption, and power consumption. This architectural layout is obtained via TSMC 65-nm 1P9M CMOS technology, and throughput metrics of 1.40 Gbps/W (throughput/power) and 0.62 Mbps/kG (throughput/area) are achieved, demonstrating that the proposed system is much more efficient than state-of-the-art designs.