Hybrid Network- and User-Centric Scalable Cell-Free Massive MIMO for Fronthaul Signaling Minimization

Cell-free massive multiple-input multiple-output (CFmMIMO) coordinates a great number of distributed access points (APs) with central processing units (CPUs), effectively reducing interference and ensuring uniform service quality for user equipment (UEs). However, its cooperative nature can result in intense fronthaul signaling between CPUs in large-scale networks. To reduce the inter-CPU fronthaul signaling for systems with limited fronthaul capacity, we propose a low-complexity online UE-AP association approach for scalable CFmMIMO that combines network- and user-centric clustering methodologies, relies on local channel information only, and can handle dynamic UE arrivals. Numerical results demonstrate that compared to the state-of-the-art method on fronthaul signaling minimization, our approach can save up to 94% of the fronthaul signaling load and 83% of the CPU processing power at the cost of only up to 8.6% spectral efficiency loss, or no loss in some cases.

Vehicle Trajectory Tracking Through Magnetic Sensors

Traffic surveillance is an important issue in Intelligent Transportation Systems(ITS). In this paper, we propose a novel surveillance system to detect and track vehicles using ubiquitously deployed magnetic sensors. That is, multiple magnetic sensors, mounted roadside and along lane boundary lines, are used to track various vehicles. Real-time vehicle detection data are reported from magnetic sensors, collected into data center via base stations, and processed to depict vehicle trajectories including vehicle position, timestamp, speed and type. We first define a vehicle trajectory tracking problem. We then propose a graph-based data association algorithm to track each detected vehicle, and design a related online algorithm framework respectively. We finally validate the performance via both experimental simulation and real-world road test. The experimental results demonstrate that the proposed solution provides a cost-effective solution to capture the driving status of vehicles and on that basis form various traffic safety and efficiency applications.