Performance of Joint XR and Best Effort eMBB Traffic in 5G-Advanced Networks

In this paper, we address the joint performance of eXtended reality (XR) and best effort enhanced mobile broadband (eMBB) traffic for a 5G-Advanced system. Although XR users require stringent throughput and latency performance, operators do not lose significant additional network capacity when adding XR users to an eMBB dominated network. For instance, adding an XR service at 45 Mbps with 10 ms packet delay budget, yields close to a 45 Mbps drop in eMBB capacity. In an XR only network layer, we show how the capacity in number of supported XR users depends significantly on the rate but also the latency budget. We show also how the XR service capacity is significantly reduced in the mixed service setting as the system goes into full load and other-cell interference becomes significant. The presented results can be used by cellular service providers to assess their networks performance of XR traffic based on their current eMBB performance, or as input to dimensioning to be able to serve certain XR traffic loads

PDU-set Scheduling Algorithm for XR Traffic in Multi-Service 5G-Advanced Networks

In this paper, we investigate a dynamic packet scheduling algorithm designed to enhance the eXtended Reality (XR) capacity of fifth-generation (5G)-Advanced networks with multiple cells, multiple users, and multiple services. The scheduler exploits the newly defined protocol data unit (PDU)-set information for XR traffic flows to enhance its quality-of-service awareness. To evaluate the performance of the proposed solution, advanced dynamic system-level simulations are conducted. The findings reveal that the proposed scheduler offers a notable improvement in increasing XR capacity up to 45%, while keeping the same enhanced mobile broadband (eMBB) cell throughput as compared to the well-known baseline schedulers

High Order Volterra Series Analysis Using Parallel Computing

INTRODUCTION The Volterra series technique has been used extensively in various applications in the area of nonlinear circuit analysis and optimization (see e.g. references [1]--[28]). Examples are in the (i) analysis of intermodulation in small signal amplifiers [6]--[12], (ii) determination of oscillation frequency and amplitude in near sinusoidal oscillators [3]--[5], (iii) analysis of mixers with moderate local oscillator levels [13, 14], analysis of communication systems [14]--[18], and (v) analysis of noise in nonlinear networks [24]--[28]. The use of the Volterra series technique basically involves two steps: (i) first, from specified input signal frequencies to determine all relevant Volterra transfer functions of the network, and (ii) next, to determine the output response from the non-linear network based on specified amplitudes of the input signals. One limitation in the use of Volterra series is that the determination of Volterra transfer functions is usually lim