The Complexity-Rate Tradeoff of Centralized Radio Access Networks

TL;DR

This paper introduces an analytical framework to evaluate the computational tradeoffs in centralized radio access networks, demonstrating that pooling resources enhances throughput efficiency.

Contribution

It proposes new metrics and an analytical model to quantify computational load and benefits of resource pooling in centralized RANs.

Findings

Pooling computational resources increases throughput efficiency.

The framework accurately predicts computational load under realistic conditions.

Centralized processing outperforms local processing in terms of throughput per resource.

Abstract

In a centralized RAN, the signals from multiple RAPs are processed centrally in a data center. Centralized RAN enables advanced interference coordination strategies while leveraging the elastic provisioning of data processing resources. It is particularly well suited for dense deployments, such as within a large building where the RAPs are connected via fibre and many cells are underutilized. This paper considers the computational requirements of centralized RAN with the goal of illuminating the benefits of pooling computational resources. A new analytical framework is proposed for quantifying the computational load associated with the centralized processing of uplink signals in the presence of block Rayleigh fading, distance-dependent path-loss, and fractional power control. Several new performance metrics are defined, including computational outage probability, outage complexity, computational gain, computational diversity, and the complexity-rate tradeoff. The validity of the analytical framework is confirmed by comparing it numerically with a simulator compliant with the 3GPP LTE standard. Using the developed metrics, it is shown that centralizing the computing resources provides a higher net throughput per computational resource as compared to local processing.