Adaptive Preconditioned Iterative Linear Detection and Architecture for Massive MU-MIMO Uplink

Being the enabling technique for 5G wireless communications, massive multiple-input multiple-output (MIMO) system can drastically increase the capacity efficiency. However, a few hundreds of antennas will inevitably introduce notable complexity and therefore hinders its direct adoption. Though the state-of-the-art (SOA) iterative methods such as conjugate gradient (CG) detection show complexity advantage over the conventional ones such as MMSE detection, their convergence rates slow down if the antenna configurations become more complicated. To this end, first this paper devotes itself in exploring the convergence properties of iterative linear solvers and then leverages the proposed adaptive precondition technique to improve the convergence rate. This adaptive precondition technique is general and has been incorporated with steepest descent (SD) detection as a show case. An approximated calculation for log-likelihood ratios (LLRs) is proposed for further complexity reduction. Analytical and numerical results have shown that with the same iteration number, the adaptive preconditioned SD (APSD) detector outperforms the CG one around 1 dB when BER = 10−3. Hardware architecture for the APSD detector is proposed based on iteration bound analysis and architectural optimization for the first time. Architectures for other adaptive preconditioned iterative linear detectors can be easily derived by following similar design flow. Compared with the SOA designs, FPGA implementations have verified the APSD detector’s advantage in balancing throughput and complexity, and guaranteed its application feasibility for 5G wireless.