This paper studies the performance of quantized massive multiple-input multiple-output (MIMO) systems with superimposed pilots (SP), using linear minimum mean-square-error (LMMSE) channel estimation and maximum ratio combining (MRC) detection. In contrast to previous works, arbitrary-bit analog-to-digital converters (ADCs) are considered. We derive an accurate approximation of the uplink achievable rate considering the removal of estimated pilots. Based on the analytical expression, the optimal pilot power factor that maximizes the achievable rate is deduced and an expression for energy efficiency (EE) is given. In addition, the achievable rate and the optimal power allocation policy under some asymptotic limits are analyzed. Analysis shows that the systems with higher-resolution ADCs or larger number of base station (BS) antennas need to allocate more power to pilots. In contrast, more power needs to be allocated to data when the channel is slowly varying. Numerical results show that in the low signal-to-noise ratio (SNR) region, for 1-bit quantizers, SP outperforms time-multiplexed pilots (TP) in most cases, while for systems with higher-resolution ADCs, the SP scheme is suitable for the scenarios with comparatively small number of BS antennas and relatively long channel coherence time.

This letter presents performance analysis in the high signal-to-noise ratio (SNR) region for matched filter (MF) precoding in single cell Massive MIMO systems. The outage probability function is derived in closed form, and the data rate of each user is also given. We have also presented asymptotic analysis in terms of data rate for MF when the number of users and the number of antennas grow without bounds. The expressions of these analytical results are rather simple and are thus convenient for overall performance evaluation. The simulation results show that the analysis are very accurate.

In this paper, we devise low-complexity uplink detection algorithms for Massive MIMO systems. We treat the uplink detection as an ill-posed problem and adopt the Landweber Method to solve it. In order to reduce the computational complexity and increase the convergence rate, we propose improved Landweber Method with optimal relax factor (ILM-O) algorithm. In addition, to reduce the order of Landweber Method by introducing a set of coefficients, we propose reduced order Landweber Method (ROLM) algorithm. An analysis on the convergence and the complexity is provided. Numerical results demonstrate that the proposed algorithms outperform the existing algorithm.