Interacting Object-Enabled Clustering and Characterization of Distributed MIMO Channels

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 an 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 distributed MIMO channels.

Experimental Analysis of Multipath Characteristics in Indoor Distributed Massive MIMO Channels

Distributed massive multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, has emerged as a promising technology for sixth-generation (6G) wireless networks. This letter introduces an indoor channel measurement campaign designed to explore the behavior of multipath components (MPCs) in distributed massive MIMO channels. Fully coherent channels were measured between eight distributed uniform planar arrays (128 elements in total) and a 12-meter user equipment route. Furthermore, a method is introduced to determine the order (single- or multi-bounce) of MPC interaction by leveraging map information and MPC parameters. In addition, a Kalman filter-based framework is used for identifying the MPC interaction mechanisms (reflection or scattering/diffraction/mixed). Finally, a comprehensive MPC-level characterization is performed based on the measured channels, including the significance of the single-bounce MPCs, the spherical wavefront features, the birth-and-death processes of the MPCs, and the spatial distribution of reflections. The findings serve as a valuable reference for understanding MPC propagation behavior, which is necessary for accurate modeling of indoor distributed massive MIMO channels.

A Measurement-Based Spatially Consistent Channel Model for Distributed MIMO in Industrial Environments

Future wireless communication systems are envisioned to support ultra-reliable and low-latency communication (URLLC), which will enable new applications such as compute offloading, wireless real-time control, and reliable monitoring. Distributed multiple-input multiple-output (D-MIMO) is one of the most promising technologies for delivering URLLC. This paper classifies obstruction and derives a channel model from a D-MIMO measurement campaign carried out at a carrier frequency of 3.75 GHz with a bandwidth of 35 MHz using twelve distributed fully coherent dipole antennas in an industrial environment. Channel characteristics are investigated, including statistical measures such as small-scale fading, large-scale fading, delay spread, and transition rates between line-of-sight and obstructed line-of-sight conditions for the different antenna elements, laying the foundations for an accurate channel model for D-MIMO systems in industrial environments. Furthermore, correlations of large-scale fading between antennas, spatial correlation, and tail distributions are included to enable proper evaluations of reliability and rare events. Based on the results, a channel model for D-MIMO in industrial environments is presented together with a recipe for its implementation.

Spatial separation of closely-spaced users in measured distributed massive MIMO channels

Aiming for the sixth generation (6G) wireless communications, distributed massive multiple-input multiple-output (MIMO) systems hold significant potential for spatial multiplexing. In order to evaluate the ability of a distributed massive MIMO system to spatially separate closely spaced users, this paper presents an indoor channel measurement campaign. The measurements are carried out at a carrier frequency of 5.6 GHz with a bandwidth of 400 MHz, employing distributed antenna arrays with a total of 128 elements. Multiple scalar metrics are selected to evaluate spatial separability in line-of-sight, non line-of-sight, and mixed conditions. Firstly, through studying the singular value spread, it is shown that in line-of-sight conditions, better user orthogonality is achieved with a distributed MIMO setup compared to a co-located MIMO array. Furthermore, the dirty-paper coding (DPC) capacity and zero forcing (ZF) precoding sum-rate capacities are investigated across varying numbers of antennas and their topologies. The results show that in all three conditions, the less complex ZF precoder can be applied in distributed massive MIMO systems while still achieving a large fraction of the DPC capacity. Additionally, in line-of-sight conditions, both sum-rate capacities and user fairness benefit from more antennas and a more distributed antenna topology. However, in the given NLoS condition, the improvement in spatial separability through distributed antenna topologies is limited.

Achieving Distributed MIMO Performance with Repeater-Assisted Cellular Massive MIMO

5G systems are being deployed all over the world and one key enabler of these systems is massive multiple-input multiple-output (MIMO). This technology has brought large performance gains in terms of serving many users. Despite the possibility to further exploit the spatial domain, there are situations where it is not possible to offer more than a single, or a few, data streams per user and where cell-edge coverage is an issue due to the lack of enough efficient channel scatterers. Looking ahead, distributed MIMO systems, where the antennas are spread over a larger area, are investigated for next generation systems. However, distributed MIMO comes with many practical deployment issues, making it a big challenge to adopt. As another way forward, we envision repeater-assisted cellular massive MIMO, where repeaters are deployed to act as channel scatterers to increase the rank of the channel and provide macro diversity for improved coverage and reliability. After elaborating on the requirements and hardware aspects of repeaters that enable this vision, we demonstrate through simulations the potential of repeater-assisted cellular massive MIMO to achieve distributed MIMO performance. Following this, we discuss open questions and future research directions.

Fading in reflective and heavily shadowed industrial environments with large arrays

One of the use cases for 5G systems and beyond is ultra-reliability low-latency communication (URLLC). An enabling technology for URLLC is massive multiple-input multiple-output (MIMO), which can increase reliability due to improved user separation, array gain and the channel hardening effect. Measurements have been performed in an operating factory environment at 3.7 GHz with a co-located massive MIMO array and a unique randomly distributed array. Channel hardening can appear when the number of antennas is increased such that the variations of channel gain (small-scale fading) is decreased and it is here quantified. The cumulative distribution function (CDF) of the channel gains then becomes steeper and its tail is reduced. This CDF is modeled and the required fading margins are quantified. By deploying a distributed array, the large-scale power variations can also be reduced, further improving reliability. The large array in this rich scattering environment, creates a more reliable channel as it approaches an independent identically distributed (i.i.d.) complex Gaussian channel, indicating that one can rethink the system design in terms of e.g. channel coding and re-transmission strategies, in order to reduce latency. To conclude, massive MIMO is a highly interesting technology for reliable connectivity in reflective and heavily shadowed industrial environments.