Spectral Efficiency Considerations for 6G

TL;DR

This paper introduces Radio Resource Utilization Efficiency (RUE) as a new metric for assessing 6G wireless systems, comparing cellular and cell-free MIMO, and analyzing factors affecting spectral efficiency and potential improvements.

Contribution

It proposes a novel metric, RUE, to evaluate radio resource efficiency in 6G, and provides comparative analysis and insights for future system design and optimization.

Findings

5G RUE is 47%, indicating room for improvement.

Increasing bandwidth from 100MHz to 1.6GHz enhances spectral efficiency.

Practical limitations and implementation losses significantly impact system performance.

Abstract

As wireless connectivity continues to evolve towards 6G, there is an ever-increasing demand to not only deliver higher throughput, lower latency, and improved reliability, but also do so as efficiently as possible. To this point, the term efficiency has been quantified through applications to Spectral Efficiency (SE) and Energy Efficiency (EE). In this paper we introduce a new system metric called Radio Resource Utilization Efficiency (RUE). This metric quantifies the efficiency of the available radio resources (Spectrum, Access Method, Time Slots, Data Symbols, etc.) used to deliver future 6G demands. We compare the system performance of Typical Cellular and Cell-Free Massive MIMO deployments as a vehicle to demonstrate the need for this new metric. We begin by providing a concise treatment of items impacting SE by introducing three categories: 5G Radio Resources, Practical Limitations (such as channel matrix rank deficiency) and Implementation Losses (SINR degradation). For the example Radio Access Technology configuration analyzed, we show 5G yields an RUE of 47% (revealing significant room for improvement when defining 6G). Practical limitation assumptions are compared to 5G Multi-User MIMO (MU-MIMO) measurements conducted in a commercialized deployment. SE losses are characterized to offer guidance to advanced algorithms employing Machine Learning (ML) based techniques. We present the benefits of increasing the transmission Bandwidth (BW) from 100MHz to 1.6GHz. We describe a Next Generation RAN architecture that can support 6G and AI-RAN.