UC Irvine

A key potential of massive multiple-input multiple-output (MIMO) systems which has made it interesting from a practical standpoint is its ability to substantially increase the network capacity with inexpensive, low-power components. Nevertheless, the static power consumption at the base station (BS) will increase proportionally to the number of antennas. Hence, considering hardware-aware design together with power consumption at the BS seems necessary in realizing practical massive MIMO systems. Among the various components responsible for power dissipation at the BS, the contribution of analog-to-digital converters (ADCs) is known to be dominant. Consequently, the idea of replacing the high-power high-resolution ADCs with power efficient low-resolution ADCs could be a viable approach to address power consumption concerns at the massive MIMO BSs. In this thesis, we study two different architectures in design of massive MIMO systems with one-bit ADCs, namely, mixed-ADC architecture and spatial Σ∆ architecture. The basic premise behind the mixed-ADC architecture is to achieve the benefits of conventional massive MIMO systems by just exploiting small pairs of high-resolution ADCs. In spatial Σ∆ architecture, by subtracting the quantized output of one antennas radio frequency (RF) chain from the signal at an adjacent antenna, coupled with spatial oversampling, the quantization noise is shaped to angular regions that the signal of interest is not present. We start with finding the optimal distribution of high-resolution and one-bit ADCs in a base station with energy constraint to maximize the spectral efficiency. Then, we use the Bussgang decomposition to develop a linear minimum mean-squared error (LMMSE) channel estimator for mixed-ADC architecture based on the combined round-robin measurements and we derive a closed-form expression for the resulting mean-squared error (MSE). We also perform a spectral efficiency (SE) analysis of the mixed-ADC implementation for the maximum ratio combining (MRC) and zero-forcing (ZF) receivers, and obtain expressions for a lower bound on the SE that takes into account the channel estimation error and the loss of efficiency due to the round-robin training. Finally, the possible SE improvement that can be achieved by using an antenna selection algorithm is investigated. Next we introduce the spatial Σ∆ architecture by adopting the oversampling and Σ∆ quantization approach in the time domain signal processing. We propose the appropriate design of spatial Σ∆ architecture by applying a scalar version of Bussgang approach. The results of the analysis indicate the significant gain of the Σ∆ approach compared with standard one-bit quantization for users that lie in the angular sector where the shaped quantization error spectrum is low. To alleviate the impact of strong interference in systems with one-bit quantizer, we extend the Σ∆ approach to present spatial feedback beamformer (FBB) Σ∆ architecture. We compare the symbol error rate of the FBB Σ∆ array with that of a system with high-resolution ADCs. The results show the superior performance of the one-bit FBB Σ∆ architecture which achieves performance equivalent to that of a system with only high resolution ADCs. To complete our analysis, we study the impact of mutual coupling and space-constrained arrays on the performance of mixed-ADC and spatial Σ∆ architectures. This phenomenon can eliminate the need for round-robin training for channel estimation in mixed-ADC architectures. For spatial Σ∆ architecture which relies on spatial oversampling (antenna spacing less than half a wavelength), the impact of mutual coupling may become significant as the antenna spacing decreases. Unlike temporal oversampling, there is a limit to the amount of spatial oversampling that can be achieved, due to the physical dimensions of the antennas. In the last section of this dissertation, we show that the one-bit Σ∆ array is particularly advantageous in space-constrained scenarios, and can still provide significant gains even in the presence of mutual coupling when the antennas are closely spaced. Through this thesis, we show that by exploiting the advanced capabilities of (MIMO) signal processing methods, performance of massive MIMO systems with coarse quantization can be significantly improved.