A Framework Design for Load-balanced Green Access Networks supporting GSM Femtocell

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1 A Framework Design for Load-balanced Green Access Networks supporting GSM Femtocell Ray-Guang Cheng 1,*, Wei-Lin Hsu 1 and Ping-Chen Lin 1 1 Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC * Corresponding Author / crg@mail.ntust.edu.tw, TEL: KEYWORDS : Green communication, Software defined radio, Radio over fiber, Load balancer Reducing the energy consumption and carbon footprint emissions to improve the global climate change has become the global concern. However, CO 2 generated from the current mobile devices and infrastructure has increased. Many researchers intended to develop the communication systems with low energy-consumption technologies, called the green communication. This paper proposes a framework of the load balanced green access network supporting the GSM femtocell service. By using the USRP software-defined radio device, we can build a GSM femtocell base station by software configuration. Besides, the proposed network can also extend the coverage of base stations by integrating with radio over fiber technology. With the load balancer, the proposed green access network can accomplish low power consumption, high energy efficiency, and easy to maintain. The experimental results showed that it can effectively save 24% energy consumption for the overall network and meet the quality-of-service of user when the proposed framework is applied. Manuscript received: December 18, 2013 / Accepted: January 23, Introduction 2. Related works To avoid the global climate change, promoting reducing energy consumption and carbon footprint emissions to improve the environment has become a critical issue in the world. Several researchers found that the energy consumption of the cellular network is much higher and is still rising in recent years [1, 2]. Fettweis and Zimmermann [1] first pointed out that the base stations (BSs) of radio access network (RAN) can consume over 80% power of an operator s mobile network. In addition, Badic et al. [2] mentioned that each BS may consume up to 2700W of electric power in cellular networks. If considering a wide area network, it may result in energy consumption of ten million watts per year. Moreover, authors also found that the volume of transmission data grows twofold every five years. Therefore, how to design an appropriate green access network has become an important issue. Based on the suggestions in [3], we can achieve our goal for green communication via two strategies: First aspect is a cellular network can adopt the load adoptive hardware components (or software modules). It allows dynamically adjusting the traffic load to save energy. The other aspect is to improve the cell planning for BSs. Based on certain performance metrics (e.g., grade of service, GoS), system can decide how many BSs should be turned off based on the population of mobile users to improve the spectrum efficiency. In this paper, we considered both aspects to design the proposed green RAN and improve energy efficiency. In order to improve the quality of service for mobile users, the network operator starts to build large amount of BSs. However, it will lead to enormous power consumptions and high operating costs. To provide effective management of bandwidth allocation and energysaving features for BSs, many network deployment and cell planning methods have become major research topic. In [4], China Mobile proposed the network framework of C-RAN with a load balancer. The C stands for three different technologies, which mean a centralized processing, collaborative radio, and real-time cloud infrastructure. Based on the load balancer, we can realize a green RAN with the features of low cost, high performance, and energy-saving. According to the evaluation of network technology, the optical fiber communication technology is allowed to support very high bandwidth and very high transmission rate. However, researchers found that it cannot provide the good user experience for mobile users. This is because the connecting method of optical fiber communication is still using the wireline fiber, which cannot provide the flexibility (e.g., mobility, ubiquitous service) for mobile users. For this reason, using the radio over fiber (RoF) [5-6] technology as the network deployment strategy is the most potentially solution in the future. More specifically, combining the existing wired optical fiber access network with the RF network can highly reduce the deployment cost and will be an inevitable trend for the future. Fig. 1 shows the combined network framework with RoF technology. This framework can be divided into four parts: a BS, a 40

2 head-end unit (HEU), an optical fiber network, and several remote antenna units (RAUs). BS is responsible for the signal exchange, control and renewal. The HEU contains a laser diode (LD) and a photo detector (PD). The LD can convert the radio frequency (RF) signal into an optical signal and sends to the RAU through an optical fiber network. In addition, there are four components, a LD, a PD, a low noise amplifier (LNA), and a power amplifier (PA) in a RAU. After receiving the optical signal at RAU, RAU converts the optical signal into a RF signal by the PD, which is further amplified by PA. Eventually, the RF signal is transmitted to the wireless medium (i.e., air) by the antenna [7]. The main benefit is that the transmission carried on the optical fiber network is able to significantly reduce the packet error rate (PER) and improve system capacity over a long distance. Fig. 1 A combined network framework with RoF technology To build up a cellular-based network, several researchers proposed different network architectures. In [8], author suggested to construct a GSM femtocell network by using the open base transceiver station (OpenBTS) software project and the universal software radio peripheral (USRP) hardware equipment. Since the OpenBTS is an open source software project developed by GUN Radio, it provides high flexibility to configure and add new module. According to [9], as shown in Fig. 2, the software-defined radio (SDR) BS is under the OpenBTS project and use at least one Linux computer. Using a USRP and serveral GSM mobile phones can build up the GSM network. It is worth mentioned that the system also requires the Asterisk [10] open source telephone switch to create voice over internet protocol (VoIP) communication. Fig. 2 The details for OpenBTS network [9] 3. Framework design of the load balanced green access network Based on the concepts of both C-RAN and RoF, this paper proposes a framework of load balanced green access network supporting GSM femtocell service. The proposed network allows having the features of low cost, high bandwidth, and high flexibility by using the GSM femtocell service and the load balancer. As shown in Fig. 3, the GSM femtocell composed by PC1 and USRP1 to PCn and USRPn in the baseband pool. The RF signal from GSM femtocell will provide corresponding wavelengths λ1~λn via photoelectric conversion in the HEU. The optical signal with different wavelength will be sent via different optical fiber to the corresponding RAUi for mobile user i. Also, a different wavelength is used to avoid interference among mobile users. In addition, the GSM femtocell composed by PCn+1 and USRPn+1 will use a single broadcast wavelength λb through the photoelectric conversion in the HEU. This single wavelength λb will be divided into n λb via an optical splitter and are sent to all RAUs (i.e., RAU1 to RAUn) for all mobile users. When the number of mobile users in RAU1 is more than the upper bound of traffic channel, λ1 and λb should be transmitted at the same time to serve the mobile users in RAU1. When the number of mobile users in RAU1 is lower than the channel threshold, only λb is transmitted to serve the mobile users in RAU1 and turning off USRP1 41

3 for energy saving. Hence, based on the proposed framework, the system can dynamically adjust the number of power-on GSM femtocell and further reduce the energy consumption. 3.1 Broadcast mode During a non-rush hour, there is only less number of mobile users served in this network. Therefore, we can use only one BS with the broadcast mode to serve all mobile users in different areas. As shown in Fig. 4, the GSM Femtocell deliver RF signal to the HEU. HEU can convert the RF signal to an optical signal before it comes to the optical fiber. Then, the optical signal will be divided into n optical signal streams with the same wavelength λ B via an optical splitter and sent to different RAUs. Hence, multiple mobile users can be served by the same GSM femtocell in a wide area. Fig. 3 Framework of load balanced green access network supporting GSM femtocell service Fig. 4 The network architecture of broadcast mode 3.2 Unicast mode During a rush hour, the number of mobile users served by a BS is increased. Hence, extra service should be provided by a new BS. In this mode, as shown in Fig. 5, the number of the GSM femtocell is decided by the service demands of RAUs. The USRP of the GSM femtocell will deliver the RF signal to the HEU to convert the RF signal to the optical signal before it comes to the optical fiber. The optical signal converted by each GSM femtocell uses a different wavelength and will be aggregated at the multiplexer (or MUX). The MUX can carry all optical signals with different wavelengths in the same optical fiber. After forwarding a certain distance in the same optical fiber, the system can separate different wavelength from the same fiber via the demultiplexer (DeMUX). Hence, the extra service can be provided by the individual RAU, which can guarantee the QoS for mobile users. 3.3 Design of load balancer The design concept of load balancer is to detect the real-time traffic load for the BS and dynamically adjust the number of the operating GSM femtocell to achieve the goal of energy saving. As 42

4 shown in Fig. 3, in the system framework diagram, a load balancer in the baseband pool will collect the number of mobile users that is served within the service range (PC 1 to PC n ) of all BSs. The information will be uploaded to the load balancer via Internet. In worth noted that the load balancer is a computer, named load balanced PC, which is responsible to determine the number of poweron GSM femtocells in the network. The load balanced PC will adjust the service of GSM femtocells, which can improve the system performance since it can avoid the network congestion due to a shortage of traffic channel and save the energy as well. In practice, there is an upper bound for the number of traffic channel (TCH) in a single GSM femtocell. Let C GSM be the upper bound channel number in a single GSM femtocell. To calculate the number of power-on GSM femtocell, we can have (1) where C is the total number of voice call in the network. This parameter can be determined by the summarization of number of voice call served by each GSM femtocell C i. That is, Fig. 6 shows the implementation details for a load balancer. However, to fulfill the function of the load balancer, both a hardware module and a software program are required to dynamically adjust power on/off of the GSM femtocells. The hardware module is based on the Zigbee module [11], which allows the load balancer to send control messages to each GSM femtocell. The software program operated on load balanced PC is used to monitor the number of traffic channel and decide whether to send the control message to certain GSM femtocell. (2) Fig. 5 The network architecture of unicast mode Fig. 6 The implementation details for a load balancer 4. Experiments and discussions and show the effectiveness for energy saving. This section we first show the measurement results of power consumption for each network components. We then give an example that how the load balancer work during one day (i.e., 24 hours) and compare the energy consumption whether we adopt the load balancer 4.1 Measurement results of power consumption In this section, we first introduce the GSM femtocell system and its power measurement results. We used the PROVA-8500 electric power saving tester [12] to measure power of the GSM femtocell and 43

5 the load balancer under different conditions. For notational convenience, we define the as the measured power consumption for the GSM femtocell that has q mobile users occupied its traffic channel. The power consumption for the radio power switch control module and load balanced PC in a load balancer are denoted by and (in W/hr). Note that includes the power consumption of Zigbee module. To calculate the total power consumption for a load balancer, it only needs to sum up the power consumption for the load balanced PC and the radio power switch control module. In addition, the power consumption for the computers at the GSM femtocell in the sleep mode (in W/hr) is defined as PC sleep. The measurement results for each mode are 1.6 W/hr, W/hr, 0.9W/hr, and a full-load power 44.51W/hr, respectively. 4.3 Operating mode of a load balancer with energy-saving effect In practice, due to the program limitation of the OpenBTS of the GSM femtocell, the current single femtocell of OpenBTS can only support 7 traffic channels (i.e., C GSM = 7) for mobile users to make phone calls. Hence, we can obtain 7 (3) Fig. 7 shows the voice traffic daily profile for Europe [13]. First, let s find the Erlang value at the peak hour as an analytical example. Based on [12], we know that the load traffic, where is the mean arrival rate, i.e. the number of call per hour; H is the average duration for one call. NGSM 1 Fig. 7 Voice traffic daily profile in Europe and assigned number of GSM femtocells during one day [13]. Table 1 Corresponding traffic of the Erlang value required by a different BS # of GSM BSs (N GSM ) Erlang (A) Traffic %~80.8% %~56% %~32.8% %~11.76% %~0% Let s apply this example to a densely- populated commercial building. Assume the average duration of a call (H) is 90 seconds and the number of calls in an hour ( during the peak hours is Therefore, the load traffic A in this building is given by 1000 call hr hr call 25 Erlang (4) First, define h i as the operation time required activating i BSs (in hour). According to the traffic distribution in Fig. 7 and Table 1, we know it is about 7 hours (h 1 = 7) to use a single BS per day; about 2.5 hours (h 2 = 2.5) to use two BSs; about 2 hours (h 3 = 2) to use three BSs; about 2.5 hours (h 4 = 2.5) to use four BSs; about 10 hours (h 5 = 10) to use five BSs. To compare the power consumptions whether use of load balancer, it is necessary to calculate the following power consumptions to determine the total energy consumption. First, for the system with a load balancer, it is necessary to determine the power consumption of the load balanced PC and the power switch control module. In addition, when only one GSM femtocell is activated, the load balancer s power is only from the USRP and the BS computer will go into a sleep mode, its power consumption is 1.64W/hr. The power consumption for 24 hours for a system using the load balancer will be calculated in two parts. First part, both and are in operation during one day, we can first calculate the power consumed by the controller of load balancer as follows 44

6 24 hr W For the second part, the calculation of the power consumption is based on the number of the BSs that are activated and the operation time (i.e., h i ) and load traffic, which is given by 5 7 BS ON (5 ) sleep i i1 P i SGF i PC h W therefore, the total power consumption that system uses the load balancer for 24 hours, can be determined by summing the results of Eqs. (5) and (6) W (7) without using the load balancer, the system will not be dynamically adjusted and must always keep 5 BSs in operation. The consumed power for 24 hours is 5 24 hr W (8) after adopting the load balancer, the power efficiency can be improved as 100% 24% (9) 5. Conclusion When the radio communication technology is rapidly developed, the requirement of data traffic is also increased as well. This leads to the incensement of energy consumption every year and also gives negative impact to the environment. Therefore, designing a radio energy-saving access network to meet the green communication concept is an inevitable trend. This paper designs a load balanced green access network framework supporting GSM femtocell service. With implementing the GSM femtocell with the technologies of both RoF and load balancer, we can centralize controlling the radio resources of BSs. Based on our proposed framework, it is easy to manage and save energy. The experimental results show that the network energy consumption for the proposed framework can save up to 24% energy compared with the system without using the proposed framework. In the meanwhile, the QoS for mobile users are also guaranteed while applying the proposed framework. (5) (6) [1] G. Fettweis and E. Zimmermann, ICT energy consumptiontrends and challenges, Proc. 11th International Symposium on Wireless Personal Multimedia Communications (WPMC), September (2008) [2] B. Badic, T. O Farrrell, P. Loskot, and J. He, Energy efficient radio access architectures for green radio: large versus small cell size deployment, Proc. IEEE Vehicular Technology Conference Fall (VTC-Fall), pp. 1-5, September (2009) DOI: /VETECF [3] A. J. Fehske, F. Richter, and G. P. Fettweis, Energy efficiency improvements through micro sites in cellular mobile radio networks, Proc. IEEE International Workshop on Global Telecommunications (GLOBECOM Workshops), pp. 1-5, December (2009) DOI: /GLOCOMW [4] China Mobile Research Institute, Green evolution of radio access network C-RAN, mod=view&aid=10, March (2010) [5] Y. C. Liang, Optical fiber telecommunication network (Wunan Publisher, Taiwan, 2007). [6] Y. Z. Chu, Optical fiber telecommunication technology and network (Machine Industry Publisher, Taiwan, 2004), vol. 1. [7] S. L. Lee, M. H. Chuang, C. H. Lee, C. H. Sun, and M. C. Tseng, Design and applications of radio-over-fiber schemes to indoor wireless coverage and high-speed rails, Proc. IEEE 3rd International Conference Photonics (ICP), pp , October (2012) DOI: /ICP [8] A. Apvrille, Openbts for dummies, Fortinet, Tech. Rep., November 29, [9] A. Loula, OpenBTS: installation and configuration guide, _En_v0.1.pdf, May 25 (2009) [10] Asterisk - Wikipedia., _(PBX) [11] EZcom estimation toolkit operating manual, pdf?dl=0, August (2011) [12] PROVA-8500 energy-saving tester, e.htm [13] M. A. Imran, E. Katranaras, G. Auer, O. Blume, V. Giannini, I. Godor, and W. Wajda, Energy efficiency analysis of the reference systems, areas of improvements and target breakdown, EARTH Project, Tech. Rep., December 31 (2010) REFERENCES 45