Asymptotic Spectral Efficiency of the Uplink in Spatially Distributed Wireless Networks With Multi-Antenna Base Stations

TL;DR

This paper derives asymptotic spectral efficiency formulas for uplink wireless networks with multi-antenna base stations, showing how efficiency scales with system parameters in interference-limited regimes.

Contribution

It provides new asymptotic expressions for spectral efficiency in large, interference-limited networks using random matrix theory and stochastic geometry.

Findings

Spectral efficiency approaches an asymptote as antennas and nodes grow large.

Efficiency depends mainly on the ratio of antennas to node density.

Linear scaling of network capacity is possible by increasing antennas and base station density.

Abstract

The spectral efficiency of a representative uplink of a given length, in interference-limited, spatially-distributed wireless networks with hexagonal cells, simple power control, and multiantenna linear Minimum-Mean-Square-Error receivers is found to approach an asymptote as the numbers of base-station antennas N and wireless nodes go to infinity. An approximation for the area-averaged spectral efficiency of a representative link (averaged over the spatial base-station and mobile distributions), for Poisson distributed base stations, is also provided. For large N, in the interference-limited regime, the area-averaged spectral efficiency is primarily a function of the ratio of the product of N and the ratio of base-station to wireless-node densities, indicating that it is possible to scale such networks by linearly increasing the product of the number of base-station antennas and the relative density of base stations to wireless nodes, with wireless-node density. The results are useful for designers of wireless systems with high inter-cell interference because it provides simple expressions for spectral efficiency as a function of tangible system parameters like base-station and wireless-node densities, and number of antennas. These results were derived combining infinite random matrix theory and stochastic geometry.