Dynamic energy savings in Cloud-RAN: An experimental assessment and implementation (original) (raw)
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IEEE Access, 2014
This paper focuses on energy efficiency aspects and related benefits of radio-accessnetwork-as-a-service (RANaaS) implementation (using commodity hardware) as architectural evolution of LTE-advanced networks toward 5G infrastructure. RANaaS is a novel concept introduced recently, which enables the partial centralization of RAN functionalities depending on the actual needs as well as on network characteristics. In the view of future definition of 5G systems, this cloud-based design is an important solution in terms of efficient usage of network resources. The aim of this paper is to give a vision of the advantages of the RANaaS, to present its benefits in terms of energy efficiency and to propose a consistent system-level power model as a reference for assessing innovative functionalities toward 5G systems. The incremental benefits through the years are also discussed in perspective, by considering technological evolution of IT platforms and the increasing matching between their capabilities and the need for progressive virtualization of RAN functionalities. The description is complemented by an exemplary evaluation in terms of energy efficiency, analyzing the achievable gains associated with the RANaaS paradigm.
Network Efficiency Amendment Utilizing Cloud Radio Access Network In Mobile Communications
International Journal of Engineering & Technology, 2018
Mobile data traffic is finding exponential growth currently in telecommunications industry. It has become important to concentrate on both spectral and energy efficiencies in utilizing cellular networks under green communication standpoint. Thus, for 5G the utmost priority is that to increase data traffic and reduce the total network energy ingesting by half. The proposed work is to design the Cloud Radio Access Network (C-RAN) with energy efficient, flexible and capacity-enhanced features by effectively bundling and establishing relation between BBU and RRU utilizing Catechistic technique. Mathematical results with realistic parameters prove that the projected optimization design clearly improve the energy efficiency of C-RAN’s compared to standard schemes.
Energy-efficient BBU pool virtualisation for C-RAN with quality of service guarantees
IET Communications, 2019
Cloud radio access network (C-RAN) has been introduced as a promising network paradigm for improving the spectral and energy efficiency of next-generation mobile systems. In C-RAN, the computation resources of the centralised baseband units (BBUs) can be virtualised and dynamically shared among cells for energy-efficient BBU pool utilisation. In this study, a BBU virtualisation scheme is proposed to minimise the total power consumption in the BBU pool subject to constraints on users' quality of service in terms of real-time requirements, individual fronthaul capacity and BBU capacity. As the BBU processing time and transmission delay for each user data can be compromised to meet the user's real-time requirements while minimising the BBU power consumption, a priori user association phase is proposed and formulated as an optimisation problem to maximise the users' transmission rate, and hence minimising their transmission delay. Then, the BBU processing allocation phase is formulated as a bin-packing problem to minimise the overall power consumption in the BBU pool. Since this problem is combinatorial, a heuristic algorithm is proposed based on best-fit-decreasing algorithm to solve it. Extensive simulations show that the proposed scheme outperforms the comparable ones in terms of power consumption with reduction up to 33%.
User-Centric Cloud RAN: An Analytical Framework for Optimizing Area Spectral and Energy Efficiency
IEEE Access
In this article, we develop a statistical framework to quantify the area spectral efficiency (ASE) and the energy efficiency (EE) performance of a user-centric cloud based radio access network (UC-RAN) downlink. We propose a user-centric remote radio head (RRH) clustering mechanism, which: 1) provides significant improvement in the received signal-to-interference-ratio through selection diversity; 2) enables efficient interference protection by inducing repulsion among scheduled user-centric RRH clusters; and 3) can self-organize the cluster radius to deal with spatio-temporal variations in user densities. It is shown that under the proposed user-centric clustering mechanism, the ASE (bits/s/Hz/m 2) maximizes at an optimal cluster size. It is observed that this cluster size is sensitive to changes in both RRH and user densities and, hence, must be adapted with variations in these parameters. Next, we formulate the cost paid for the UC-RAN capacity gains in terms of power consumption, which is then translated into the EE (bits/s/Joule) of the UC-RAN. It is observed that the cluster radius which maximizes the EE of the UC-RAN is relatively larger as compared with that which yields maximum ASE. Consequently, we notice that the tradeoff between the ASE and the EE of UC-RAN manifests itself in terms of cluster radius selection. Such tradeoff can be exploited by leveraging a simple two player cooperative game. Numerical results show that the optimal cluster radius obtained from the Nash bargaining solution of the modeled bargaining problem may be adjusted through an exponential weightage parameter that offers a mechanism to utilize the inherent ASE-EE tradeoff in a UC-RAN. Furthermore, in comparison with existing state-of-the-art non user-centric network models, our proposed scheme, by virtue of selective RRH activation and non overlapping usercentric RRH clusters, offers higher and adjustable system ASE and EE, particularly in dense deployment scenarios. INDEX TERMS User-centric architectures, cloud radio access networks, self organizing networks, area spectral efficiency, energy efficiency, Nash bargaining solution, Poisson point process.
Energy-Efficient Hybrid Powered Cloud Radio Access Network (C-RAN) for 5G
IEEE Access
Owing to the ever-increasing energy consumption, energy efficiency (EE) is an important parameter for next generation 5G network. The Cloud Radio Access Network (C-RAN) is a viable solution for tackling 5G network problems in an energy-efficient manner. Integrating renewable energy with C-RAN can be a powerful tool for reducing operating costs and carbon dioxide emission. But, the regional unpredictability and intermittency nature of renewable energy can result in energy outages and worsening the service quality. As a consequence, the most reliable way is to combine commercial grid supplies with renewable energy. In this paper, we proposed a network model for the downlink C-RAN with hybrid power supplies. The goal of the proposed hybrid supply with solar and grid energy collaboration is to optimize the use of renewable solar energy by reducing grid Power Consumption (PC) and, most significantly, enhancing energy efficiency and preserving service quality. A comprehensive analysis is performed to evaluate EE performance of the proposed C-RAN under a variety of network settings. The numerical results affirm the proposed C-RAN models establishing a significant improvement in network EE compared to the conventional one.
Performance Evaluations of Cloud Radio Access Networks
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2017
With the skyrocketing amount of data communications, traditional Radio Access Networks (RANs) infrastructure suffers from high capital and operating expenditures. Many countries and mobile network operators, therefore, propose software-defined radio access networks for centralized management, and further apply cloud computing technologies into cellular networks. Cloud Radio Access Network (Cloud-RAN) is a new paradigm for the next generation mobile network which provides ultra-high density deployments, dynamic reconfiguration of computing resources, as well as achieves high energy efficiency. To quantify the performance of Cloud-RAN infrastructure deployment, we build up real Software RAN testbeds based on an opensource LTE implementation over the latest virtualization technologies. We evaluate the performance of different testbed deployments by several test scenarios, in order to show the overhead introduced by virtualization. In addition, our testbed setup and measurement methodology will stimulate more systems research on the emerging Cloud-RAN infrastructure.
Novel Wake-up Scheme for Energy-Efficient Low-Latency Mobile Devices in 5G Networks
IEEE Transactions on Mobile Computing
Improved mobile device battery lifetime and latency minimization are critical requirements for enhancing the mobile broadband services and user experience. Long-term evolution (LTE) networks have adopted discontinuous reception (DRX) as the baseline solution for prolonged battery lifetime. However, in every DRX cycle, the mobile device baseband processing unit monitors and decodes the control signaling, and thus all instances without any actual data allocation leads to unnecessary energy consumption. This fact together with the long start-up and power-down times can prevent adopting frequent wakeup instants, which in turn leads to considerable latency. In this work, a novel wake-up scheme is described and studied, to tackle the tradeoff between latency and battery lifetime in future 5G networks, seeking thus to facilitate an always-available experience, rather than alwayson. Analytical and simulation-based results show that the proposed scheme is a promising approach to control the user plane latency and energy consumption, when the device is operating in the power saving mode. The aim of this article is to describe the overall wakeup system operating principle and the associated signaling methods, receiver processing solutions and essential implementation aspects. Additionally, the advantages compared to DRX-based systems are shown and demonstrated, through the analysis of the system energy-efficiency and latency characteristics, with special emphasis on future 5G-grade mobile devices.
Far East Journal of Electronics and Communications, 2018
The combination of Cloud Radio Access Network (C-RAN) and Heterogeneous Network (HetNet) networks has recently attracted a great deal of interest in academic and industrial fields. This architecture, called H-CRAN (Heterogeneous C-RAN) is therefore rightly considered as a promising solution for increasing the spectral efficiency and energy efficiency of 5G networks. As C-RAN network offers the advantage of energy reduction because BBU can manage many RRHs and the HetNets increases user's throughput on account of the reduction between base stations and users. This article proposes a call admission control (CAC) algorithm focused on the radio remote heads (RRHs) in order to further reduce the power consumed by the H-CRAN by switching into sleep mode, the under-used RRHs. Simulations were performed into two contexts depending on the cell radius: in the context 1, the radius is 1 km and 0.5 km in the second context. These simulations focused on the power consumed as function of UEs number. The simulations have shown that, more the cell radius is lower and
Bandwidth and Energy-Aware Resource Allocation for Cloud Radio Access Networks
IEEE Transactions on Wireless Communications, 2018
Cloud Radio Access Network (C-RAN) is emerging as a transformative paradigmatic architecture for the next generation of cellular networks. In this article, a novel resource allocation solution that optimizes the energy consumption of a C-RAN is proposed. First, an energy consumption model that characterizes the computation energy of the Base Band Unit (BBU) pool is introduced based on empirical results collected from a programmable C-RAN testbed. Then, the resource allocation problem is split into two subproblems-namely the Bandwidth Power Allocation (BPA) and the BBU Energy-Aware Resource Allocation (EARA). The BPA, which is first cast via Mixed-Integer Nonlinear Programming (MINLP) and then reformulated as a convex problem, aims at assigning a feasible bandwidth and power to serve all users while meeting their Quality of Service (QoS) requirements. The second subproblem, i.e., the BBU EARA, is defined as a bin-packing problem that aims at minimizing the number of active Virtual Machines (VMs) in the BBU pool to save energy. Simulation results coupled with real-time experiments on a small-scale C-RAN testbed show that the proposed resource allocation solution optimizes the energy consumption of the network while meeting practical constraints and QoS requirements, and outperforms competing algorithms such as Best Fit Decreasing (BFD), RRH-Clustering (RC), and SINR-based.
Elastic-RAN: An adaptable multi-level elasticity model for cloud radio acess networks
Computer Communications, 2019
Cellular mobile networks in 2020 will increase ten times their coverage area, with more than 50 billion connected devices. We will lead to a massive increase in data traffic, also fostering the development of 5G networks. Therefore industry and scientific initiatives have a crucial role in proposing related projects to meet such demand. Cloud Radio Access Networks (C-RANs) are gaining more and more attention in this context by adopting an architecture in which baseband units (BBUs) run into cloud computing resources, therefore taking advantage of distributed systems flexibility and cloud elasticity. One of the significant challenges in C-RANs lies in the high complexity of orchestrating computational resources to process incoming requests with both high performance and low infrastructure cost. In this regard, this article presents the Elastic-RAN model, which proposes a multi-level and adaptable elasticity for C-RANs. First, we explore the multi-level feature as follows: (i) one level for the BBU pools (. ., physical machines), given the high volume of traffic to particular BBU pools; (ii) another level for BBUs themselves (virtual machines) due to the high CPU and memory demands to process the incoming requests. Second, the adaptive feature refers to the moldable elasticity grain which resources in both previous levels are provisioned as close as possible to the current processing needs. We evaluated Elastic-RAN through experiments that simulated different load profiles, considering both CPU and network demands. We observed that Elastic-RAN might achieve gains up to 64% in the execution time when compared to a traditional C-RAN. Cellular network operators using the proposed technique will spend less energy and will have a solution that dynamically adjusts the baseband signal processing accordingly to the demand in their access networks.