Private 5G networks will soon be ubiquitous across the future-generation smart wireless access infrastructures hosting a wide range of performance-critical applications. A high-performing User Plane Function (UPF) in the data plane is critical to achieving such stringent performance goals, as it governs fast packet processing and supports several key control-plane operations. Based on a private 5G prototype imple-mentation and analysis, it is imperative to perform dynamic resource management and orchestration at the UPF. This paper leverages Mobile Edge Cloud-Intelligent Agent (MEC-IA), a logically centralized entity that proactively distributes resources at UPF for various service types, significantly reducing the tail latency experienced by the user requests while maximizing resource utilization. Extending the MEC-IA functionality to MEC layers further incurs data plane latency reduction. Based on our extensive simulations, under skewed uRLLC traffic arrival, the MEC-IA assisted bestfit UPF-MEC scheme reduces the worst-case latency of UE requests by up to 77.8% w.r.t. baseline. Additionally, the system can increase uRLLC connectivity gain by 2.40× while obtaining 40% CapEx savings.

We present Wall-Street, a smart surface designed for vehicles to boost 5G mmWave connectivity for passengers. It improves mmWave connections in three ways: (1) it steers outdoor mmWave signals into the vehicle, ensuring all users have coverage; (2) it enables the vehicle to receive data from the current cell while measuring signals from potential handover cells, allowing smooth transitions without interrupting service; (3) during handovers, it combines/splits signals from/to both the current and new cells, creating a make-before-break connection. Our demonstration shows Wall-Street's versatile signal manipulation abilities. These include steering single beams, simultaneously reflecting and transmitting beams for neighboring cell measurements with concurrent communication, and combining or splitting beams for seamless cell transitions.

We present experiments using mobile 28 GHz Phased Array Antenna Modules (PAAMs), demonstrating their ability to perform beam steering with high granularity. The mobile node contains a 64-element IBM 28 GHz PAAM along with a USRP software defined radio, allowing for configuration of the transmit/receive (TX/RX) parameters. These parameters include beam shape, beam steering, and duty cycling. We demonstrate the capabilities of the mobile PAAMs by forming a wireless OFDM link between two mobile PAAMs. We then showcase the beam steering capabilities of the PAAM by performing beam sweeping on the RX PAAM to find the angle of arrival from the TX PAAM. A simple graphical user interface is presented for configuring the PAAMs. A tutorial is available online for users interested in experimentation with 28 GHz PAAMs*.

This paper presents a platform for measuring, evaluating and modeling the energy efficiency aspects of an O-RAN 5G wireless network. We describe our open-source based O-RAN testbed which includes both bare-metal and Kubernetes network functions, in addition to physical network components. We focus on measuring power consumption of servers and workloads of cloudified and virtualized network functions. We show that a combination of different power measurements can be used successfully to achieve the accuracy and granularity required for energy efficiency measurement, evaluation and modeling.