Optimal Placement of Baseband Functions for Energy Harvesting Virtual Small Cells

TL;DR

This paper investigates the optimal placement of baseband functions in energy-harvesting virtual small cells, balancing QoS and energy constraints using dynamic programming to improve energy efficiency and system performance.

Contribution

It introduces a method using dynamic programming to determine the optimal functional split in EH-powered small cells, considering QoS and energy availability.

Findings

Identifies performance bounds for optimal baseband function placement.

Demonstrates the effectiveness of DP in optimizing energy use.

Provides insights into energy-aware functional split decisions.

Abstract

Flexible functional split in Cloud Radio Access Network (CRAN) greatly overcomes fronthaul capacity and latency challenges. In such architecture, part of the baseband processing is done locally and the remaining is done remotely in the central cloud. On the other hand, Energy Harvesting (EH) technologies are increasingly adopted due to sustainability and economic advantages. Power consumption due to baseband processing has a huge share in the total power consumption breakdown of smaller base stations. Given that such base stations are powered by EH, in addition to QoS constraints, energy availability also conditions the decision on where to place each baseband function in the system. This work focuses on determining the performance bounds of an optimal placement of baseband functional split option in virtualized small cells that are solely powered by EH. The work applies Dynamic Programming (DP), in particular, Shortest Path search is used to determine the optimal functional split option considering traffic QoS requirements and available energy budget.