Massive MIMO with Low-Resolution Data Converters: Algorithm Design and Performance Evaluation
Massive multiuser multiple-input multiple-output (MIMO) is foreseen to be a key technology in next-generation (5G) cellular communication systems, due to huge potential gains in spectral efficiency and energy efficiency. In this thesis, we investigate the performance of massive MIMO systems, which operate over a Rayleigh-fading channel, for the case when the base station (BS) is equipped with low-resolution data converters. More specifically, in the uplink the received signal at the BS is converted into the digital domain by a set of low-resolution analog-to-digital converters (ADCs). In the downlink, the transmit signal is generated by a set of low-resolution digital-to-analog converters (DACs).
First, we consider the narrowband massive MIMO uplink for the case when the BS is equipped with low-resolution ADCs. Our focus is on the case where neither the transmitter nor the receiver have any a priori channel state information (CSI), which implies that the fading realizations have to be learned through pilot transmission followed by channel estimation at the receiver, based on coarsely quantized observations. We derive a low-complexity channel estimator and present lower bounds and closed-form expressions for the achievable rates with the proposed channel estimator and linear detection algorithms.
Second, we consider the narrowband massive MIMO downlink for the case when the BS is equipped with low-resolution DACs. We derive lower bounds and closed-form expressions for the achievable rates with linear precoding under the assumption that the BS has access to perfect CSI. We also propose novel nonlinear precoding algorithms that are shown to significantly outperform linear precoders for the case of 1-bit DACs.
Finally, focusing on the case of 1-bit DACs and linear precoding, we extend our analysis to the case of frequency-selective channels and to oversampling DACs.
Our results suggest that the resolution of data converters in a massive MIMO system can be reduced significantly compared to what is used in today’s state-of-the-art MIMO systems, without significant reductions in the overall system performance.