Distributed multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, emerges as a promising technology for sixth-generation (6G) systems to support uniform coverage and reliable communication. For the design and optimization of such systems, measurement-based investigations of real-world distributed MIMO channels are essential. In this paper, we present a sub-6 GHz indoor channel measurement campaign, featuring eight distributed antenna arrays with 128 elements in total. Multi-link channels are measured at 50 positions along a 12-meter user route. A clustering algorithm enabled by interacting objects is proposed to identify clusters in the measured channels. The algorithm jointly clusters the multipath components for all links, effectively capturing the dynamic contributions of common clusters to different links. In addition, a Kalman filter-based tracking framework is introduced for cluster prediction, tracking, and updating along the user movement. Using the clustering and tracking results, cluster-level characterization of the measured channels is performed. First, the number of clusters and their visibility at both link ends are analyzed. Next, a maximum-likelihood estimator is utilized to determine the entire cluster visibility region length. Finally, key cluster-level properties, including the common cluster ratio, cluster power, shadowing, spread, among others, are statistically investigated. The results provide valuable insights into cluster behavior in typical multi-link channels, necessary for accurate modeling of sub-6 GHz distributed MIMO channels.

This paper presents LuMaMi28, a real-time 28 GHz multi-user (MU) multiple-input multiple-output (MIMO) testbed. In this testbed, the base station has 16 transceiver chains with a fully-digital beamforming architecture (with different pre-coding algorithms) and simultaneously supports multiple user equipments (UEs) with spatial multiplexing. The UEs are equipped with a beam-switchable antenna array for real-time antenna selection where the one with the highest channel magnitude, out of four pre-defined beams, is selected. For the beam-switchable antenna array, we consider two kinds of UE antennas, with different beam-width and different peak-gain. Based on this testbed, we provide measurement results for millimeter-wave (mmWave) MU-MIMO performance in different real-life scenarios with static and mobile UEs. We explore the potential benefit of the mmWave MU-MIMO systems with antenna selection based on measured channel data, and discuss the performance results through real-time measurements.

Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.

Channel estimation is essential in massive MIMO systems. Pilot Contamination (PC) however, causes a major bottleneck in the acquisition of this information. The exploitation of the Angle of Arrival (AoA) provides multiple techniques for channel estimation under PC. However, many AoA estimation techniques require information on the signal statistics which is not available in dynamic scenarios. In this paper we propose and analyse the Matrix Pencil Method (MPM) to decorrelate contaminated channels based on their estimated AoA. We evaluate this method both through simulations and experiments in a real-life testbed. Our assessment focuses on a system with a Uniform Linear Array (ULA). The performance of the MPM is validated through simulations1with varying number of antennas, SNR and AoA difference. The results show that our approach effectively decorrelates the channels starting from 20 antennas and an SNR of 15 dB, which outperforms the theoretical expectation. This allows us to enhance the channel estimation quality under PC to the level of no PC. Real-life measurements confirm the simulated results. Our MPM implementation can achieve a target AoA estimation accuracy both with and without PC. We anticipate that the method can be extended for a Uniform Rectangular Array (URA).1We would like to thank NVIDIA for providing the GPU that was used to greatly accelerate our simulations.