Location-Informed Interference Suppression Precoding Methods for Distributed Massive MIMO Systems

The evolution of mobile networks towards user-centric cell-free distributed Massive MIMO configurations requires the development of novel signal processing techniques. More specifically, digital precoding algorithms have to be designed or adopted to enable distributed operation. Future deployments are expected to improve coexistence between cellular generations, and between mobile networks and incumbent services such as radar. In dense cell-free deployments, it might also not be possible to have full channel state information for all users at all antennas. To leverage location information in a dense deployment area, we suggest and investigate several algorithmic alterations on existing precoding methods, aimed at location-informed interference suppression, for usage in existing and emerging systems where user locations are known. The proposed algorithms are derived using a theoretical channel model and validated and numerically evaluated using an empirical dataset containing channel measurements from an indoor distributed Massive MIMO testbed. When dealing with measured CSI, the impact of the hardware, in addition to the location-based channel, needs to be compensated for. We propose a method to calibrate the hardware and achieve measurement-based evaluation of our location-based interference suppression algorithms. The results demonstrate that the proposed methods allow location-based interference suppression without explicit CSI knowledge at the transmitter, under certain realistic network conditions.

Building a real-time physical layer labeled data logging facility for 6G research

This work describes the architecture and vision of designing and implementing a new test infrastructure for 6G physical layer research at KU Leuven. The Testbed is designed for physical layer research and experimentation following several emerging trends, such as cell-free networking, integrated communication, sensing, open disaggregated Radio Access Networks, AI-Native design, and multiband operation. The software is almost entirely based on free and open-source software, making contributing and reusing any component easy. The open Testbed is designed to provide real-time and labeled data on all parts of the physical layer, from raw IQ data to synchronization statistics, channel state information, or symbol/bit/packet error rates. Real-time labeled datasets can be collected by synchronizing the physical layer data logging with a positioning and motion capture system. One of the main goals of the design is to make it open and accessible to external users remotely. Most tests and data captures can easily be automated, and experiment code can be remotely deployed using standard containers (e.g., Docker or Podman). Finally, the paper describes how the Testbed can be used for our research on joint communication and sensing, over-the-air synchronization, distributed processing, and AI in the loop.