Multi-User XR Offloading via Massive MIMO: A System-Level Analysis using a Real-Life Dataset

TL;DR

This paper analyzes the potential of using Massive MIMO technology for multi-user XR offloading, focusing on latency, localization accuracy, and power consumption through a system-level framework and case study.

Contribution

It introduces a system-level analysis framework for multi-user XR offloading with Massive MIMO and provides insights into trade-offs between latency, accuracy, and power.

Findings

Massive MIMO reduces latency and transmission power for XR offloading.

Trade-offs exist between latency, localization error, and device power.

Further evaluations are needed for complete power consumption analysis.

Abstract

SLAM is one of the biggest bottlenecks of XR devices, which have strict requirements for latency, power consumption, and user satisfaction. A solution that has been proposed and studied to meet the requirements is to offload SLAM to a remote server, which leverages computational hardware but may suffer due to incurred delays and transmission power. In this work, we propose offloading SLAM using Massive MIMO, which is attractive due to lower latencies, transmission power, and a more reliable link for multiple users. A framework for system-level analysis of latency and localisation error in multi-user offloaded XR with Massive MIMO has been proposed, and a case study with varying system-level parameters has been performed with it. The case study showed that there are important trade-offs between latency, localisation error, and device transmission power. We find that Massive MIMO is a promising technology for XR offloading, but that further evaluations including complete device power consumption are needed to get the full picture.