Hybrid Precoding Based on Active Learning for mmWave Massive MIMO Communication Systems

In this paper, a cost-effective and high-accuracy precoding technique based on $\epsilon $ -Fuzzy pareto active learning (FPAL) is proposed for millimeter wave (mmWave) massive MIMO communications. The proposed method achieves a low iteration convergence with low complexity. Two practical structures, namely fully-connected and partially-connected structures are considered for hybrid precoding. Furthermore, the effect of high and low-resolution quantization in the digital-to-analog converter, phase shifter, and imperfect channel state information are discussed. The performance results of the proposed technique and alternating minimization methods beside fully digital techniques are compared and discussed in the terms of the spectral efficiency (SE), bit error rate (BER), normalized mean square error (NMSE), energy efficiency (EE) for phase-shifter (PS) with low bit quantization, and imperfect channel state information (CSI). To address the applicability of the proposed method, experimental results are obtained from a real mmWave hardware setup compliant with 3GPP standards, and verify the simulated ones for the proposed e -FPAL hybrid precoding scheme. The reduced complexity, higher performance and EE make the proposed method suitable for 5G and beyond communication systems.