We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta (ΣΔ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the ΣΔ approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the ΣΔ array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the ΣΔ approach for both MRC and zero-forcing receivers.

We consider channel estimation for an uplink massive multiple input multiple output (MIMO) system where the base station (BS) uses a first-order spatial Sigma-Delta (ΣΔ) analogto-digital converter (ADC) array. The ΣΔ array consists of closely spaced sensors which oversample the received signal and provide a coarsely quantized (1-bit) output. We develop a linear minimum mean squared error (LMMSE) estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. The performance of the proposed ΣΔ LMMSE estimator is compared via simulation to channel estimation using standard 1-bit quantization and also infinite resolution ADCs.

Gains in area spectral efficiency have been recently demonstrated in networks of large-antenna arrays by means of fog massive MIMO operation and virtual sector-based processing. In this paper, we adopt such sector-based processing and operation to localize users in the network. We investigate the viability of some widely used supervised-learning methods in estimating user locations by observing across the fog massive MIMO network signals transmitted by the users. In particular, we evaluate linear regression (LR), weighted K-nearest neighbors (WKNN), and neural networks (NN) in the context of a network of massive-antenna remote-radio heads (RRHs) using simulations based on a spatially consistent channel model. As our simulations reveal, NN-based location estimators trained with user-sector channel gains could be a viable approach for providing user location information at the network side.

We consider uplink transmission for a massive multiuser multiple-input multiple-output (MU-MIMO) system in which the base station (BS) is equipped with a mixed analog-to-digital converter (ADC) architecture. In this architecture, a portion of the antennas at the BS is connected to low-power one-bit ADCs while the other is fed to power-hungry high-resolution ADCs. By taking into account the mixed-ADC architecture, we derive a closed-form expression for the sum spectral efficiency (SE) when maximum ratio combining (MRC) detection is employed at the BS. Then, we formulate an optimization problem to determine, for a given power budget at the BS, what is the optimal distribution of one-bit and high-resolution ADCs that maximizes the sum SE. Interestingly, it is shown that in most realistic scenarios, using only one-bit ADCs provides the best spectral efficiency for a given power budget constraint.