# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 4 Energy Efficiency Analyses of Radio-over-Fiber Networks
### Approaches for Improving the Energy Efficiency of Cloud Radio Access Networks
#### 4.2.2 Techniques for Improving Energy Efficiency in Cloud Radio Access Networks
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **PS Method** &
\begin{tabular}{c} **Summary of Contributions Related to the** \\ **Improvement of FiWi Network Energy Efficiency** \\ \end{tabular} \\ \hline
[9] & Unified resource management scheme &
\begin{tabular}{c} The realization of the FiWi network with MEC led to a \\ significant reduction in power consumption and \\ increase in the battery life of edge devices. \\ \end{tabular} \\ \hline
[91] & \
\begin{tabular}{c} Unified resource management scheme \\ and cloudlet-aware DBA algorithms \\ \end{tabular} &
\begin{tabular}{c} A resource management scheme that takes into account \\ the use of cloudlets and incorporates offloading tasks \\ into the FiWi DBA process. The proposed management scheme could significantly reduce the amount of energy \\ used by edge devices and extend their battery \\ life significantly. \\ \end{tabular} \\ \hline
[92] & \
\begin{tabular}{c} Priority-based task offloading and \\ caching (PrO) method \\ \end{tabular} &
\begin{tabular}{c} The proposed PrO scheme efficiently manages tasks by \\ caching, offloading and performing local computing \\ while preserving the priority order, which resulted in \\ reduced delay and energy consumption. \\ \end{tabular} \\ \hline
[93] & \
\begin{tabular}{c} ACCO and GT-CCO PS methods \\ \end{tabular} &
\begin{tabular}{c} Proposed a two cloud–MEC collaborative computation \\ offloading mechanisms. Using a combination of MEC \\ and centralized remote cloud services resulted in \\ significantly lower energy consumption compared with \\ solutions without a centralized cloud. \\ \end{tabular} \\ \hline
[94] & GT-CCO PS method &
\begin{tabular}{c} Proposed a FiWi network architecture that enables the \\ coexistence of centralized cloud and MEC in the IoT \\ applications. A game-theoretic collaborative \\ computation offloading scheme was proposed as a \\ solution for improving energy efficiency and handling a \\ large number of mobile devices effectively. \\ \end{tabular} \\ \hline
[7] & ISA-CCO PS method &
\begin{tabular}{c} Confirmed that the proposed ISA-CCO solution is more \\ effective than previously proposed ACCO and GT-CCO \\ in terms of reducing energy consumption and \\ improving processing response time on mobile devices. \\ \end{tabular} \\ \hline
[95] & TSGO PS method &
\begin{tabular}{c} Energy-efficient offloading strategy for MEC-enhanced \\ FiWi. The three EE benchmarks to evaluate EE \\ mechanisms for MEC FiWi were proposed and it was \\ confirmed that the proposed strategy could significantly \\ decrease energy consumption in MEC-enhanced \\ FiWi networks. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the related works on PS methods in networks based on the MEC concept. Furthermore, the authors in ref. [81] developed a radio resource management method for energy-efficient H-CRAN (Table 3). The EE of H-CRAN was studied using an energy consumption model that includes all networking devices. Using this model, the authors developed an energy-efficient radio resource management algorithm to improve the network EE of the H-CRAN. The authors in ref. [82] proposed an adaptive RoF system for reducing energy consumption while maintaining the required transmission rate the next-generation C-RAN systems (Table 3). The proposed system is based on the 2x2 MIMO-RoF model that employs coherent optical orthogonal frequency division multiplexing (CO-OFDM) technology. Through extensive numerical analyses, the authors verified that the proposed system could achieve significantly lower energy consumption with a high spectrum efficiency. Additionally, the proliferation of the NFV technique has enabled the adoption of virtual machines which execute BBU functions in a way such that multiple virtual machines can run on a single, generic server at the BBU pool (Figure 9). By increasing the number of deployed RRUs and active virtual machines within the server, the authors in ref. [83] emphasized the need for solving the problem of increased power consumption.