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This paper considers uplink massive multiple-input multiple-output (MIMO) systems with lowresolution analog-to-digital converters (ADCs) over Rician fading channels. Maximum-ratio-combining (MRC) and zero-forcing (ZF) receivers are considered under the assumption of perfect and imperfect channel state information (CSI). Low-resolution ADCs are considered for both data detection and channel estimation, and the resulting performance is analyzed. Asymptotic approximations of the spectrum efficiency (SE) for large systems are derived based on random matrix theory. With these results, we can provide insights into the trade-offs between the SE and the ADC resolution and study the influence of the Rician K-factors on the performance. It is shown that a large value of K-factors may lead to better performance and alleviate the influence of quantization noise on channel estimation. Moreover, we investigate the power scaling laws for both receivers under imperfect CSI and it shows that when the number of base station (BS) antennas is very large, without loss of SE performance, the transmission power can be scaled by the number of BS antennas for both receivers while the overall performance is limited by the resolution of ADCs. The asymptotic analysis is validated by numerical results. Besides, it is also shown that the SE gap between the two receivers is narrowed down when the K-factor is increased. We also show that ADCs with moderate resolutions lead to better energy efficiency (EE) than that with high-resolution or extremely low-resolution ADCs and using ZF receivers achieve higher EE as compared with the MRC receivers.
In this paper, an analytical framework for evaluating the performance of scalable cell-free massive MIMO (SCF-mMIMO) systems in which all user equipments (UEs) and access points (APs) employ finite resolution digital-to-analog converters (DACs) and a
This paper addresses one of the classical problems in random matrix theory-- finding the distribution of the maximum eigenvalue of the correlated Wishart unitary ensemble. In particular, we derive a new exact expression for the cumulative distributio
This paper considers a multipair amplify-and-forward massive MIMO relaying system with low-resolution ADCs at both the relay and destinations. The channel state information (CSI) at the relay is obtained via pilot training, which is then utilized to
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at
This paper considers channel estimation and uplink achievable rate of the coarsely quantized massive multiple-input multiple-output (MIMO) system with radio frequency (RF) impairments. We utilize additive quantization noise model (AQNM) and extended