Efficient MIMO Preprocessor With Sorting-Relaxed QR Decomposition and Modified Greedy LLL Algorithm

This paper proposes a high-efficient preprocessing algorithm for $16\times 16$ MIMO detections. The proposed algorithm combines a sorting-relaxed QR decomposition (SRQRD) and a modified greedy LLL (MGLLL) algorithm. First, SRQRD is conducted to decompose the channel matrices. This decomposition adopts a relaxed sorting strategy together with a paralleled Givens Rotation (GR) array scheme, which can reduce the processing latency by 60% compared with conventional sorted QR decomposition (SQRD). Then, an MGLLL algorithm is conducted to improve detection performance further. The MGLLL algorithm adopts a paralleled selection criterion, and only process the most urgent iterations. Thus the processing latency and column swaps can be reduced by 50% and 75%, respectively, compared with the standard LLL algorithm. Finally, the bit-error-rate (BER) performance of this preprocessing algorithm is evaluated using two MIMO detectors. Results indicate that this preprocessor suffers a negligible performance degradation compared with the combination of the standard LLL algorithm and SQRD. Based on this preprocessing algorithm, a pipelined hardware architecture is also designed in this paper. A series of systolic coordinated-rotation-digital-computer (CORDIC) arrays are utilized, and highly-pipelined circuits are designed, helping this architecture achieve high frequency performance. This architecture is implemented using 65-nm CMOS technology, which can work at a maximum frequency of 625 MHz to process channel matrices every 16 clock cycles. The latency is 0.9 us. Comparisons indicate that this preprocessor outperforms other similar designs in terms of latency, throughput, and gate-efficiency.