Evaluating Massive MIMO Precoding based on 3D-Channel Measurements with a Spider Antenna

Massive Multiple-Input Multiple-Output (MIMO)communications uses a large number of antennas at the base station to increase the data rate and user density in future wireless systems. For simulation, it has become common practice to use i.i.d. complex Gaussian matrix entries to obtain an average MIMO channel behavior. More refined models have been devised and proposed to standardization bodies; yet, channel modeling remains an active area of research, as current models tend to be, still, quite limited, e.g., when it comes to evaluating clustering algorithms, with regions of spatial orthogonality for concurrent scheduling of users, which is an essential concept in massive MIMO precoding. For this, spatial correlations need to be included. To further refine channel modeling, we have built a "spider antenna" prototype that allows spatially continuous measurements in three dimensions, enabling a high-resolution sampling over, initially, a volume of 2m x 2m x 2m for indoor measurements. Several experiments have been conducted to illustrate the new insights to be gained when studying user orthogonality, clustering and precoding in a massive MIMO context. Furthermore, the influence of antenna array geometry and user spacing on the achievable rate over actually measured channels is studied.Comment: Submitted to ISWCS201

Area Rate Evaluation based on Spatial Clustering of massive MIMO Channel Measurements

Channel models for massive MIMO are typically based on matrices with complex Gaussian entries, extended by the Kronecker and Weichselberger model. One reason for observing a gap between modeled and actual channel behavior is the absence of spatial consistency in many such models, that is, spatial correlations over an area in the x, y-dimensions are not accounted for, making it difficult to study, e.g., area-throughput measures. In this paper, we propose an algorithm that can distinguish between regions of non-line-of-sight (NLoS) and line-of-sight (LoS) via a rank-metric criterion combined with a spiral search. With a k-means clustering algorithm a throughput per region (i.e., cluster) can be calculated, leading to what we refer to as "area-throughput". For evaluating the proposed orthogonality clustering scheme we use a simple filtered MIMO channel model which is spatially consistent, with known degrees of freedom. Moreover, we employ actual (spatially consistent) area channel measurements based on spatial sampling using a spider antenna and show that the proposed algorithm can be used to estimate the degrees of freedom, and, subsequently, the number of users that maximizes the throughput per square meter.Comment: Submitted to WSA201

On Deep Learning-based Massive MIMO Indoor User Localization

We examine the usability of deep neural networks for multiple-input multiple-output (MIMO) user positioning solely based on the orthogonal frequency division multiplex (OFDM) complex channel coefficients. In contrast to other indoor positioning systems (IPSs), the proposed method does not require any additional piloting overhead or any other changes in the communications system itself as it is deployed on top of an existing OFDM MIMO system. Supported by actual measurements, we are mainly interested in the more challenging non-line of sight (NLoS) scenario. However, gradient descent optimization is known to require a large amount of data-points for training, i.e., the required database would be too large when compared to conventional methods. Thus, we propose a twostep training procedure, with training on simulated line of sight (LoS) data in the first step, and finetuning on measured NLoS positions in the second step. This turns out to reduce the required measured training positions and thus, reduces the effort for data acquisition.Comment: submitted to SPAWC 201