Performance Scaling of mmWave Personal IoT Networks (PINs) for XR Applications

Abstract

To provide a high-quality user experience in Extended Reality (XR) applications, high-throughput and low-latency communication is essential. A promising solution is the use of distributed networks operating in the higher frequency bands, such as millimeter-wave (mmWave) wearable Personal IoT Networks (PINs). However, in crowded environments, intra-network interactions can disrupt the Quality of Experience (QoE) for users. To improve the QoE, the understanding of the system-level performance trade-offs in these networks is important. This paper investigates the impact of various system parameters on the system-level performance of mmWave wearable PINs with 3D beamforming and data rate adaptation to the channel conditions in an environment with human body blockage. We employ an analytical methodology that combines stochastic geometry and queueing theory to devise an expression for the stationary distribution of the system and use it to compute the key metrics that describe the system-level performance. To assess mmWave PINs for XR in crowded environments, we examine the system operation trade-offs and explore the performance scaling.