The typical applications of MEC includes mobile commerce, mobile learning, mobile healthcare, mobile gaming and other practical applications like social networking, showing maps, storing images and video [91]. These applications when processed in the mobile devices consume a lot of battery power, as such energy efficient schemes for saving energy in mobile devices by offloading application to the MEC cloud are presented in this section.
The authors in [92] investigate a green MEC system and developed an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimisation-based dynamic computation offloading algorithm, which jointly makes the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of the Lyapunov is that the decisions depend only on the current system state without requiring distribution information of the computation task request and of the wireless channel. Nevertheless, the authors assume that the battery capacity is sufficiently large which is impractical, also the authors ignore the execution delay caused by the MEC server.
Chen L. et al. in [93] addresses the challenge of incorporating MEC into dense cellular networks, and propose an efficient online algorithm, called ENGINE (ENergy constrained offloadINg and slEeping) which makes joint computation offloading in order to maximise the QoS while keeping the energy consumption low. However, the authors assume that traffic among BSs is equally distributed whereas traffic is randomly distributed in reality. Zhang K. et al. in [52] proposed an energy efficient computation offloading (EECO) mechanisms for MEC in 5G HetNets. In EECO, an optimisation problem was formulated to minimise the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. However, the authors do not show the impact on the response time of an application offloaded to the MEC server.
The authors in [85] study the multi-user computation offloading problem for MEC computing in a multi-channel wireless interference environment by formulating the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases. However, the application to be offloaded is assumed to be atomic (the application cannot be divided into tasks), hence the whole application code is sent to the MEC server which can incur more transmission costs.
Deng M. et al. in [94] investigated the computation offloading decision making problem in a multi-cell MEC scenario. The authors proposed an adaptive sequential offloading game approach and designed a multi-user computation offloading algorithm where the mobile users sequentially make offloading decisions based on the current interference environment and available computation resources. Numerical results show that their proposed algorithm can achieve efficient performance and scale well as the network system size increases. Nevertheless, there is also a lack of a global control manager to manage the requests from the mobile users.
Beck et al. in [95] proposed MEC enabled Voice over LTE (ME-VoLTE) architecture to reduce battery consumption of mobile devices during video call and provide a communication protocol for negotiating the offloading strategy. Here, the process of video encoding during video call processing is offloaded at the MEC edge server. Nevertheless, the authors consider a MEC server at the edge of a BS which has limited processing capacity.
The downfalls of the above MEC energy saving schemes in the mobile devices is that the MEC server at the edge of the network (at the BS) are of limited capacity and storage, hence too many requests from users can overload the MEC server in next generation networks where there will be many applications for mobile users. There is also a lack of a global control manager to manage the requests from the mobile users which leads to poor performance.
3.4 Concluding Remarks
This chapter has looked at the various energy saving schemes in C-RAN which fall primarily under the categories of BS switching, BBU reduction, CoMP and dynamic resource allocation. The key drawbacks with the existing techniques is that they only consider static network traffic, do not consider power consumption in the fronthaul and only consider standalone BBUs which are not shared to further reduce energy consumption. The chapter also presented the energy saving techniques for MEC proposed in literature which mainly look at offloading computation hungry application tasks to the MEC server while meeting delay deadlines of the application. The downfalls of these conventional MEC energy saving schemes in the mobile devices are that the MEC server at the edge of the network (at the BS) are of limited capacity and storage as such too many requests from users can overload the MEC server in next generation networks where there will be many applications for mobile users. Hence there is need for new schemes that will further reduce energy consumption within the C-RAN at the radio side and in the BBU pool and also within the mobile device.
4 Proposed Energy Efficient 5G C-RAN
Framework
4.1 Introduction
This chapter introduces the proposed energy efficient 5G C-RAN framework termed H-C-RAN as it combines HetNets and C-RAN. In the proposed H-C- RAN framework, energy saving is done in three ways as shown in Figure 4-1.
i) At the radio side of C-RAN: A novel BS sleeping mechanism is
implemented that incorporates CAC;
ii) At the cloud side: Baseband processing workload consolidation via
virtual BBU placement is employed along with an advanced CAC scheme implemented in the BS cloud to improve QoS; and
iii) At the mobile device side: MEC paradigm is implemented in H-C-RAN
framework to save energy in the mobile device by computation offloading where processing and energy hungry applications are split into tasks which are then executed in the MEC server in the BS cloud.
These key blocks of the proposed framework are discussed in the following sub sections in details.